Last updated: July 22, 2025

Duplicate record percent data quality checks, SQL examples

This check measures the percentage of duplicate records values. It raises a data quality issue when the percentage of duplicates is above a minimum accepted value. The default threshold is 0% duplicate values.

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The duplicate record percent data quality check has the following variants for each type of data quality checks supported by DQOps.

profile duplicate record percent

Check description

Verifies that the percentage of duplicate record values in a table does not exceed the maximum accepted percentage.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
profile_duplicate_record_percent	Maximum percentage of duplicate records	uniqueness	profiling		Uniqueness	duplicate_record_percent	max_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the profile duplicate record percent data quality check.

Managing profile duplicate record percent check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=profile_duplicate_record_percent --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_duplicate_record_percent --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_duplicate_record_percent --enable-warning
                    -Wmax_percent=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=profile_duplicate_record_percent --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_duplicate_record_percent --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_duplicate_record_percent --enable-error
                    -Emax_percent=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the profile_duplicate_record_percent check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=profile_duplicate_record_percent

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=profile_duplicate_record_percent

You can also run this check on all tables on which the profile_duplicate_record_percent check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=profile_duplicate_record_percent

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  profiling_checks:
    uniqueness:
      profile_duplicate_record_percent:
        parameters:
          columns:
          - id
          - created_at
        warning:
          max_percent: 0.0
        error:
          max_percent: 1.0
        fatal:
          max_percent: 5.0
  columns: {}

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
        FROM (
            SELECT
                "id", "created_at"
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at"
) grouping_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
        FROM (
            SELECT
                "id", "created_at"
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at"
) grouping_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at]
) grouping_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  profiling_checks:
    uniqueness:
      profile_duplicate_record_percent:
        parameters:
          columns:
          - id
          - created_at
        warning:
          max_percent: 0.0
        error:
          max_percent: 1.0
        fatal:
          max_percent: 5.0
  columns:
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2
        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2

        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2

    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records,
        analyzed_table.[country] AS grouping_level_1,
        analyzed_table.[state] AS grouping_level_2
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at], analyzed_table.[country], analyzed_table.[state]
) grouping_table
GROUP BY

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2

    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

daily duplicate record percent

Check description

Verifies that the percentage of duplicate record values in a table does not exceed the maximum accepted percentage.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_duplicate_record_percent	Maximum percentage of duplicate records	uniqueness	monitoring	daily	Uniqueness	duplicate_record_percent	max_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily duplicate record percent data quality check.

Managing daily duplicate record percent check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_duplicate_record_percent --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_duplicate_record_percent --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_duplicate_record_percent --enable-warning
                    -Wmax_percent=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_duplicate_record_percent --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_duplicate_record_percent --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_duplicate_record_percent --enable-error
                    -Emax_percent=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the daily_duplicate_record_percent check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=daily_duplicate_record_percent

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=daily_duplicate_record_percent

You can also run this check on all tables on which the daily_duplicate_record_percent check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_duplicate_record_percent

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  monitoring_checks:
    daily:
      uniqueness:
        daily_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns: {}

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
        FROM (
            SELECT
                "id", "created_at"
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at"
) grouping_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
        FROM (
            SELECT
                "id", "created_at"
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at"
) grouping_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at]
) grouping_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  monitoring_checks:
    daily:
      uniqueness:
        daily_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns:
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2
        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2

        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2

    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records,
        analyzed_table.[country] AS grouping_level_1,
        analyzed_table.[state] AS grouping_level_2
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at], analyzed_table.[country], analyzed_table.[state]
) grouping_table
GROUP BY

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2

    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

monthly duplicate record percent

Check description

Verifies that the percentage of duplicate record values in a table does not exceed the maximum accepted percentage.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
monthly_duplicate_record_percent	Maximum percentage of duplicate records	uniqueness	monitoring	monthly	Uniqueness	duplicate_record_percent	max_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly duplicate record percent data quality check.

Managing monthly duplicate record percent check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_duplicate_record_percent --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_duplicate_record_percent --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_duplicate_record_percent --enable-warning
                    -Wmax_percent=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_duplicate_record_percent --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_duplicate_record_percent --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_duplicate_record_percent --enable-error
                    -Emax_percent=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the monthly_duplicate_record_percent check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=monthly_duplicate_record_percent

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=monthly_duplicate_record_percent

You can also run this check on all tables on which the monthly_duplicate_record_percent check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_duplicate_record_percent

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  monitoring_checks:
    monthly:
      uniqueness:
        monthly_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns: {}

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
        FROM (
            SELECT
                "id", "created_at"
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at"
) grouping_table

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
        FROM (
            SELECT
                "id", "created_at"
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at"
) grouping_table

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`
) grouping_table

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at]
) grouping_table

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at"
) grouping_table

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records
    FROM (
        SELECT
            "id", "created_at"
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at"
) grouping_table

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  monitoring_checks:
    monthly:
      uniqueness:
        monthly_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns:
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2
        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2

        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2

    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records,
        analyzed_table.[country] AS grouping_level_1,
        analyzed_table.[state] AS grouping_level_2
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at], analyzed_table.[country], analyzed_table.[state]
) grouping_table
GROUP BY

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2

FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2

    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2
) grouping_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

---

daily partition duplicate record percent

Check description

Verifies that the percentage of duplicate record values in a table does not exceed the maximum accepted percentage.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
daily_partition_duplicate_record_percent	Maximum percentage of duplicate records	uniqueness	partitioned	daily	Uniqueness	duplicate_record_percent	max_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily partition duplicate record percent data quality check.

Managing daily partition duplicate record percent check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_partition_duplicate_record_percent --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_duplicate_record_percent --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_duplicate_record_percent --enable-warning
                    -Wmax_percent=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=daily_partition_duplicate_record_percent --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_duplicate_record_percent --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_duplicate_record_percent --enable-error
                    -Emax_percent=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the daily_partition_duplicate_record_percent check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=daily_partition_duplicate_record_percent

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=daily_partition_duplicate_record_percent

You can also run this check on all tables on which the daily_partition_duplicate_record_percent check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=daily_partition_duplicate_record_percent

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  partitioned_checks:
    daily:
      uniqueness:
        daily_partition_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns:
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        CAST(analyzed_table.`date_column` AS DATE) AS time_period,
        TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        CAST(analyzed_table."date_column" AS DATE) AS time_period,
        toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        CAST(analyzed_table.`date_column` AS DATE) AS time_period,
        TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            CAST(analyzed_table_nested."date_column" AS DATE) AS time_period,
            TIMESTAMP(CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            CAST(analyzed_table_nested."date_column" AS DATE) AS time_period,
            TO_TIMESTAMP(CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period_utc
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            TRUNC(CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period,
            CAST(TRUNC(CAST(analyzed_table_nested."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            CAST(analyzed_table_nested."date_column" AS date) AS time_period,
            CAST(CAST(analyzed_table_nested."date_column" AS date) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records,
        CAST(DATE_TRUNC('day', analyzed_table."date_column") AS DATE) AS time_period,
        CAST((CAST(DATE_TRUNC('day', analyzed_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        CAST(analyzed_table.`date_column` AS DATE) AS time_period,
        TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records,
        CAST(analyzed_table.[date_column] AS date) AS time_period,
        CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at], CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
) grouping_table
GROUP BY
    time_period,
    time_period_utc

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        CAST(analyzed_table."date_column" AS DATE) AS time_period,
        CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            CAST(analyzed_table_nested."date_column" AS date) AS time_period,
            CAST(CAST(analyzed_table_nested."date_column" AS date) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  partitioned_checks:
    daily:
      uniqueness:
        daily_partition_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns:
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        CAST(analyzed_table.`date_column` AS DATE) AS time_period,
        TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(analyzed_table."date_column" AS DATE) AS time_period,
        toDateTime64(CAST(analyzed_table."date_column" AS DATE), 3) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        CAST(analyzed_table.`date_column` AS DATE) AS time_period,
        TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            CAST(analyzed_table_nested."date_column" AS DATE) AS time_period,
            TIMESTAMP(CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            CAST(analyzed_table_nested."date_column" AS DATE) AS time_period,
            TO_TIMESTAMP(CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period_utc
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-%d 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2
,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            TRUNC(CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period,
            CAST(TRUNC(CAST(analyzed_table_nested."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2
,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            CAST(analyzed_table_nested."date_column" AS date) AS time_period,
            CAST(CAST(analyzed_table_nested."date_column" AS date) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(DATE_TRUNC('day', analyzed_table."date_column") AS DATE) AS time_period,
        CAST((CAST(DATE_TRUNC('day', analyzed_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        CAST((CAST(analyzed_table."date_column" AS date)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(analyzed_table."date_column" AS date) AS time_period,
        TO_TIMESTAMP(CAST(analyzed_table."date_column" AS date)) AS time_period_utc
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        CAST(analyzed_table.`date_column` AS DATE) AS time_period,
        TIMESTAMP(CAST(analyzed_table.`date_column` AS DATE)) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records,
        analyzed_table.[country] AS grouping_level_1,
        analyzed_table.[state] AS grouping_level_2,
        CAST(analyzed_table.[date_column] AS date) AS time_period,
        CAST((CAST(analyzed_table.[date_column] AS date)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at], analyzed_table.[country], analyzed_table.[state], CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
) grouping_table
GROUP BY

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(analyzed_table."date_column" AS DATE) AS time_period,
        CAST(CAST(analyzed_table."date_column" AS DATE) AS TIMESTAMP) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2
,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            CAST(analyzed_table_nested."date_column" AS date) AS time_period,
            CAST(CAST(analyzed_table_nested."date_column" AS date) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

---

monthly partition duplicate record percent

Check description

Verifies that the percentage of duplicate record values in a table does not exceed the maximum accepted percentage.

Data quality check name	Friendly name	Category	Check type	Time scale	Quality dimension	Sensor definition	Quality rule	Standard
monthly_partition_duplicate_record_percent	Maximum percentage of duplicate records	uniqueness	partitioned	monthly	Uniqueness	duplicate_record_percent	max_percent

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly partition duplicate record percent data quality check.

Managing monthly partition duplicate record percent check from DQOps shell

Activate the check with a warning ruleActivate the check with an error ruleRun all configured checks

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_partition_duplicate_record_percent --enable-warning

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_duplicate_record_percent --enable-warning

Additional rule parameters are passed using the -Wrule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_duplicate_record_percent --enable-warning
                    -Wmax_percent=value

Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.

dqo> check activate -c=connection_name -t=schema_name.table_name  -ch=monthly_partition_duplicate_record_percent --enable-error

You can also use patterns to activate the check on all matching tables and columns.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_duplicate_record_percent --enable-error

Additional rule parameters are passed using the -Erule_parameter_name=value.

dqo> check activate -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_duplicate_record_percent --enable-error
                    -Emax_percent=value

Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the monthly_partition_duplicate_record_percent check on all tables on a single data source.

dqo> check run -c=data_source_name -ch=monthly_partition_duplicate_record_percent

It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.

dqo> check run -c=connection_name -t=schema_name.table_name -ch=monthly_partition_duplicate_record_percent

You can also run this check on all tables on which the monthly_partition_duplicate_record_percent check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_*  -ch=monthly_partition_duplicate_record_percent

YAML configuration

The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  partitioned_checks:
    monthly:
      uniqueness:
        monthly_partition_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns:
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"

Samples of generated SQL queries for each data source type

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent data quality sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
        TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)) AS time_period,
        toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
        TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period,
            TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS DATE))) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            SERIES_ROUND(CAST(analyzed_table_nested."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
            TO_TIMESTAMP(SERIES_ROUND(CAST(analyzed_table_nested."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            TRUNC(CAST(analyzed_table_nested."date_column" AS DATE), 'MONTH') AS time_period,
            CAST(TRUNC(CAST(analyzed_table_nested."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS time_period,
            CAST(DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records,
        CAST(DATE_TRUNC('month', analyzed_table."date_column") AS DATE) AS time_period,
        CAST((CAST(DATE_TRUNC('month', analyzed_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS time_period_utc
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
        TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records,
        DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
        CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at], DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
) grouping_table
GROUP BY
    time_period,
    time_period_utc

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
        CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS time_period,
            CAST(DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", time_period, time_period_utc
) grouping_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc

Expand the Configure with data grouping section to see additional examples for configuring this data quality checks to use data grouping (GROUP BY).

Configuration with data grouping

Sample configuration with data grouping enabled (YAML) The sample below shows how to configure the data grouping and how it affects the generated SQL query.

# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    partition_by_column: date_column
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  partitioned_checks:
    monthly:
      uniqueness:
        monthly_partition_duplicate_record_percent:
          parameters:
            columns:
            - id
            - created_at
          warning:
            max_percent: 0.0
          error:
            max_percent: 1.0
          fatal:
            max_percent: 5.0
  columns:
    date_column:
      labels:
      - "date or datetime column used as a daily or monthly partitioning key, dates\
        \ (and times) are truncated to a day or a month by the sensor's query for\
        \ partitioned checks"
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the duplicate_record_percent sensor.

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH) AS time_period,
        TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`date_column` AS DATE), MONTH)) AS time_period_utc
    FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

ClickHouse

Sensor template for ClickHouseRendered SQL for ClickHouse

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='toString(', column_suffix=')') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN sumOrNull(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)) AS time_period,
        toDateTime64(DATE_TRUNC('month', CAST(analyzed_table."date_column" AS DATE)), 3) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(toString("id"), toString("created_at")) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Databricks

Sensor template for DatabricksRendered SQL for Databricks

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,
    grouping_table.grouping_level_1,
    grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
        TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DB2

Sensor template for DB2Rendered SQL for DB2

{% import '/dialects/db2.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS DATE)) AS time_period,
            TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS DATE))) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

DuckDB

Sensor template for DuckDBRendered SQL for DuckDB

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST( ', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM  AS analyzed_table
    WHERE (COALESCE(CAST( "id" AS VARCHAR), CAST( "created_at" AS VARCHAR)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

HANA

Sensor template for HANARendered SQL for HANA

{% import '/dialects/hana.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            SERIES_ROUND(CAST(analyzed_table_nested."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
            TO_TIMESTAMP(SERIES_ROUND(CAST(analyzed_table_nested."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
        FROM "<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MariaDB

Sensor template for MariaDBRendered SQL for MariaDB

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

MySQL

Sensor template for MySQLRendered SQL for MySQL

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00') AS time_period,
        FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.`date_column`, '%Y-%m-01 00:00:00'))) AS time_period_utc
    FROM `<target_table>` AS analyzed_table
    WHERE (COALESCE(`id`, `created_at`) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Oracle

Sensor template for OracleRendered SQL for Oracle

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
            {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='            ') }}
        FROM (
            SELECT
                {{ extract_in_list(parameters.columns) -}}
                {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
                {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
            FROM {{ lib.render_target_table() }} analyzed_table_nested
            {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='            ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4000))') ~ ') IS NOT NULL') }}
    ) analyzed_table
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
,
    time_period,
    time_period_utc
    FROM (
        SELECT COUNT(*) AS records_number,
            COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                        analyzed_table_nested.grouping_level_1,

                        analyzed_table_nested.grouping_level_2
,
            time_period,
            time_period_utc
        FROM (
            SELECT
                "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            TRUNC(CAST(analyzed_table_nested."date_column" AS DATE), 'MONTH') AS time_period,
            CAST(TRUNC(CAST(analyzed_table_nested."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
            FROM "<target_schema>"."<target_table>" analyzed_table_nested
            WHERE (COALESCE(CAST("id" AS VARCHAR(4000)), CAST("created_at" AS VARCHAR(4000))) IS NOT NULL)
    ) analyzed_table
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Presto

Sensor template for PrestoRendered SQL for Presto

{% import '/dialects/presto.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
        {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2
,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS time_period,
            CAST(DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_database"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

QuestDB

Sensor template for QuestDBRendered SQL for QuestDB

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}}
        {%- if not loop.last -%}
            {{- ", " if separate_by_comma else " || " -}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro %}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT({{ extract_in_list(parameters.columns) -}}) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR', separate_by_comma=true) ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns, separate_by_comma=true) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    COALESCE(
        (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0
        , 0) AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT() AS records_number,
        COUNT_DISTINCT("id" || "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        CAST(DATE_TRUNC('month', analyzed_table."date_column") AS DATE) AS time_period,
        CAST((CAST(DATE_TRUNC('month', analyzed_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_suffix='::VARCHAR') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE("id"::VARCHAR, "created_at"::VARCHAR) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Snowflake

Sensor template for SnowflakeRendered SQL for Snowflake

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date)) AS time_period,
        TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."date_column" AS date))) AS time_period_utc
    FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS STRING), CAST("created_at" AS STRING)) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Spark

Sensor template for SparkRendered SQL for Spark

{% import '/dialects/spark.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS STRING)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY `id`, `created_at`) AS distinct_records,
        analyzed_table.`country` AS grouping_level_1,
        analyzed_table.`state` AS grouping_level_2,
        DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE)) AS time_period,
        TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table.`date_column` AS DATE))) AS time_period_utc
    FROM `<target_schema>`.`<target_table>` AS analyzed_table
    WHERE (COALESCE(CAST(`id` AS STRING), CAST(`created_at` AS STRING)) IS NOT NULL)
    GROUP BY `id`, `created_at`, grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}

{%- macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro -%}

{% macro render_group_by(table_alias_prefix = 'grouping_table', indentation = '    ') %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
    GROUP BY
    {%- endif -%}
    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- ',' if not loop.first -}}{{- lib.eol() }}
            {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute -}}
        {%- endfor -%}
    {%- endif -%}
    {%- if lib.time_series is not none -%}
        {{ ',' if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -}}{{- lib.eol() -}}
        {{ indentation }}time_period,{{ lib.eol() -}}
        {{ indentation }}time_period_utc
    {%- endif -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{ render_group_by('grouping_table') }}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY [id], [created_at]) AS distinct_records,
        analyzed_table.[country] AS grouping_level_1,
        analyzed_table.[state] AS grouping_level_2,
        DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1) AS time_period,
        CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)) AS DATETIME) AS time_period_utc
    FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
    WHERE (COALESCE(CAST([id] AS VARCHAR), CAST([created_at] AS VARCHAR)) IS NOT NULL)
    GROUP BY [id], [created_at], analyzed_table.[country], analyzed_table.[state], DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[date_column]), 0)
) grouping_table
GROUP BY

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc

Teradata

Sensor template for TeradataRendered SQL for Teradata

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
        {{- lib.render_data_grouping_projections('analyzed_table', indentation='        ') }}
        {{- lib.render_time_dimension_projection('analyzed_table', indentation='        ') }}
    FROM {{ lib.render_target_table() }} AS analyzed_table
    {{- lib.render_where_clause(indentation='    ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR(4096))') ~ ') IS NOT NULL') }}
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) * 1.0 / SUM(records_number)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,
        analyzed_table."country" AS grouping_level_1,
        analyzed_table."state" AS grouping_level_2,
        TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS time_period,
        CAST(TRUNC(CAST(analyzed_table."date_column" AS DATE), 'MM') AS TIMESTAMP) AS time_period_utc
    FROM "<target_schema>"."<target_table>" AS analyzed_table
    WHERE (COALESCE(CAST("id" AS VARCHAR(4096)), CAST("created_at" AS VARCHAR(4096))) IS NOT NULL)
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

Trino

Sensor template for TrinoRendered SQL for Trino

{% import '/dialects/trino.sql.jinja2' as lib with context -%}

{% macro extract_in_list(values_list, column_prefix = none, column_suffix = none, separate_by_comma = false) %}
    {%- set column_names = table.columns if values_list is none or (values_list | length()) == 0 else values_list -%}
    {%- for item in column_names -%}
        {{ (column_prefix) if column_prefix is not none -}} {{- lib.quote_identifier(item) -}} {{- (column_suffix) if column_suffix is not none -}} {{- ", " if not loop.last }} {{- "', ', " if separate_by_comma and not loop.last }}
    {%- endfor -%}
{% endmacro %}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value
    {{- lib.render_data_grouping_projections_reference('grouping_table') }}
    {{- lib.render_time_dimension_projection_reference('grouping_table') }}
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY {{ extract_in_list(parameters.columns) -}} ) AS distinct_records
    {{- lib.render_data_grouping_projections_reference('analyzed_table_nested', indentation='        ') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table_nested', indentation='        ') }}
    FROM (
        SELECT
            {{ extract_in_list(parameters.columns) -}}
            {{- lib.render_data_grouping_projections('analyzed_table_nested', indentation='            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table_nested', indentation='            ') }}
        FROM {{ lib.render_target_table() }} AS analyzed_table_nested
        {{- lib.render_where_clause(table_alias_prefix = 'analyzed_table_nested', indentation='        ', extra_filter = 'COALESCE(' ~ extract_in_list(parameters.columns, column_prefix='CAST(', column_suffix=' AS VARCHAR)') ~ ') IS NOT NULL') }}
    )
    GROUP BY {{ extract_in_list(parameters.columns) -}} {{- (", " ~ lib.render_grouping_column_names()) if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none }}
) grouping_table
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

SELECT
    CASE WHEN SUM(distinct_records) IS NULL THEN 0
        ELSE (1 - SUM(distinct_records) / CAST(SUM(records_number) AS DOUBLE)) * 100.0 END
        AS actual_value,

                grouping_table.grouping_level_1,

                grouping_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT COUNT(*) AS records_number,
        COUNT(*) OVER (PARTITION BY "id", "created_at") AS distinct_records,

                    analyzed_table_nested.grouping_level_1,

                    analyzed_table_nested.grouping_level_2
,
        time_period,
        time_period_utc
    FROM (
        SELECT
            "id", "created_at",
            analyzed_table_nested."country" AS grouping_level_1,
            analyzed_table_nested."state" AS grouping_level_2,
            DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS time_period,
            CAST(DATE_TRUNC('MONTH', CAST(analyzed_table_nested."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
        FROM "your_trino_catalog"."<target_schema>"."<target_table>" AS analyzed_table_nested
        WHERE (COALESCE(CAST("id" AS VARCHAR), CAST("created_at" AS VARCHAR)) IS NOT NULL)
    )
    GROUP BY "id", "created_at", grouping_level_1, grouping_level_2, time_period, time_period_utc
) grouping_table
GROUP BY grouping_level_1, grouping_level_2, time_period, time_period_utc
ORDER BY grouping_level_1, grouping_level_2, time_period, time_period_utc

---

What's next

• Learn how to configure data quality checks in DQOps
• Look at the examples of running data quality checks, targeting tables and columns