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Last updated: July 22, 2025

Text length above max length data quality checks, SQL examples

This check finds texts that are longer than the maximum accepted text length. It counts the number of texts that are too long and raises a data quality issue when too many invalid texts are found.


The text length above max length data quality check has the following variants for each type of data quality checks supported by DQOps.

profile text length above max length

Check description

The check counts the number of text values in the column that is above the length defined by the user as a parameter.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
profile_text_length_above_max_length Find text values longer than the maximum accepted length text profiling Reasonableness text_length_above_max_length_count max_count

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the profile text length above max length data quality check.

Managing profile text length above max length check from DQOps shell

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 -col=column_name -ch=profile_text_length_above_max_length --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_* -col=column_name -ch=profile_text_length_above_max_length --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=profile_text_length_above_max_length --enable-warning
                    -Wmax_count=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 -col=column_name -ch=profile_text_length_above_max_length --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_* -col=column_name -ch=profile_text_length_above_max_length --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=profile_text_length_above_max_length --enable-error
                    -Emax_count=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_text_length_above_max_length check on all tables and columns on a single data source.

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

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_text_length_above_max_length

You can also run this check on all tables (and columns) on which the profile_text_length_above_max_length check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=profile_text_length_above_max_length

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:
  columns:
    target_column:
      profiling_checks:
        text:
          profile_text_length_above_max_length:
            parameters:
              max_length: 100
            warning:
              max_count: 0
            error:
              max_count: 10
            fatal:
              max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
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 text_length_above_max_length_count data quality sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM  AS analyzed_table
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_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
  columns:
    target_column:
      profiling_checks:
        text:
          profile_text_length_above_max_length:
            parameters:
              max_length: 100
            warning:
              max_count: 0
            error:
              max_count: 10
            fatal:
              max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
    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 text_length_above_max_length_count sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

daily text length above max length

Check description

The check counts the number of text values in the column that is above the length defined by the user as a parameter. Stores the most recent captured value for each day when the data quality check was evaluated.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
daily_text_length_above_max_length Find text values longer than the maximum accepted length text monitoring daily Reasonableness text_length_above_max_length_count max_count

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily text length above max length data quality check.

Managing daily text length above max length check from DQOps shell

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 -col=column_name -ch=daily_text_length_above_max_length --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_* -col=column_name -ch=daily_text_length_above_max_length --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_text_length_above_max_length --enable-warning
                    -Wmax_count=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 -col=column_name -ch=daily_text_length_above_max_length --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_* -col=column_name -ch=daily_text_length_above_max_length --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_text_length_above_max_length --enable-error
                    -Emax_count=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_text_length_above_max_length check on all tables and columns on a single data source.

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

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_text_length_above_max_length

You can also run this check on all tables (and columns) on which the daily_text_length_above_max_length check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=daily_text_length_above_max_length

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:
  columns:
    target_column:
      monitoring_checks:
        daily:
          text:
            daily_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
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 text_length_above_max_length_count data quality sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM  AS analyzed_table
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_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
  columns:
    target_column:
      monitoring_checks:
        daily:
          text:
            daily_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
    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 text_length_above_max_length_count sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

monthly text length above max length

Check description

The check counts the number of text values in the column that is above the length defined by the user as a parameter. Stores the most recent captured value for each month when the data quality check was evaluated.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
monthly_text_length_above_max_length Find text values longer than the maximum accepted length text monitoring monthly Reasonableness text_length_above_max_length_count max_count

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly text length above max length data quality check.

Managing monthly text length above max length check from DQOps shell

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 -col=column_name -ch=monthly_text_length_above_max_length --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_* -col=column_name -ch=monthly_text_length_above_max_length --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_text_length_above_max_length --enable-warning
                    -Wmax_count=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 -col=column_name -ch=monthly_text_length_above_max_length --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_* -col=column_name -ch=monthly_text_length_above_max_length --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_text_length_above_max_length --enable-error
                    -Emax_count=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_text_length_above_max_length check on all tables and columns on a single data source.

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

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_text_length_above_max_length

You can also run this check on all tables (and columns) on which the monthly_text_length_above_max_length check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=monthly_text_length_above_max_length

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:
  columns:
    target_column:
      monitoring_checks:
        monthly:
          text:
            monthly_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
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 text_length_above_max_length_count data quality sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `your-google-project-id`.`<target_schema>`.`<target_table>` AS analyzed_table
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM  AS analyzed_table
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_table>` AS analyzed_table
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_table>` AS analyzed_table
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_postgresql_database"."<target_schema>"."<target_table>" AS analyzed_table
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
FROM(
    SELECT
        original_table.*
    FROM "<target_table>" original_table
) analyzed_table
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_redshift_database"."<target_schema>"."<target_table>" AS analyzed_table
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "your_snowflake_database"."<target_schema>"."<target_table>" AS analyzed_table
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM `<target_schema>`.`<target_table>` AS analyzed_table
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM [your_sql_server_database].[<target_schema>].[<target_table>] AS analyzed_table
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM "<target_schema>"."<target_table>" AS analyzed_table
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value
FROM (
    SELECT
        original_table.*
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_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
  columns:
    target_column:
      monitoring_checks:
        monthly:
          text:
            monthly_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
    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 text_length_above_max_length_count sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM  AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2
FROM `<target_schema>`.`<target_table>` AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY analyzed_table.[country], analyzed_table.[state]
ORDER BY level_1, level_2
        , 
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2
FROM "<target_schema>"."<target_table>" AS analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2

FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY grouping_level_1, grouping_level_2
ORDER BY grouping_level_1, grouping_level_2

daily partition text length above max length

Check description

The check counts the number of text values in the column that is above the length defined by the user as a parameter. Analyzes every daily partition and creates a separate data quality check result with the time period value that identifies the daily partition.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
daily_partition_text_length_above_max_length Find text values longer than the maximum accepted length text partitioned daily Reasonableness text_length_above_max_length_count max_count

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the daily partition text length above max length data quality check.

Managing daily partition text length above max length check from DQOps shell

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 -col=column_name -ch=daily_partition_text_length_above_max_length --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_* -col=column_name -ch=daily_partition_text_length_above_max_length --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_partition_text_length_above_max_length --enable-warning
                    -Wmax_count=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 -col=column_name -ch=daily_partition_text_length_above_max_length --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_* -col=column_name -ch=daily_partition_text_length_above_max_length --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=daily_partition_text_length_above_max_length --enable-error
                    -Emax_count=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_text_length_above_max_length check on all tables and columns on a single data source.

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

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_text_length_above_max_length

You can also run this check on all tables (and columns) on which the daily_partition_text_length_above_max_length check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=daily_partition_text_length_above_max_length

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
  columns:
    target_column:
      partitioned_checks:
        daily:
          text:
            daily_partition_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
    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 text_length_above_max_length_count data quality sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY CAST(analyzed_table.[date_column] AS date)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_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
  columns:
    target_column:
      partitioned_checks:
        daily:
          text:
            daily_partition_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
    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 text_length_above_max_length_count sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS DATE) AS time_period,
    TO_TIMESTAMP(CAST(original_table."date_column" AS DATE)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(original_table."date_column" AS DATE)) AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(DATE_TRUNC('day', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('day', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_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
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY analyzed_table.[country], analyzed_table.[state], CAST(analyzed_table.[date_column] AS date), CAST(analyzed_table.[date_column] AS date)
ORDER BY level_1, level_2CAST(analyzed_table.[date_column] AS date)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(original_table."date_column" AS date) AS time_period,
    CAST(CAST(original_table."date_column" AS date) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_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 text length above max length

Check description

The check counts the number of text values in the column that is above the length defined by the user as a parameter. Analyzes every monthly partition and creates a separate data quality check result with the time period value that identifies the monthly partition.

Data quality check name Friendly name Category Check type Time scale Quality dimension Sensor definition Quality rule Standard
monthly_partition_text_length_above_max_length Find text values longer than the maximum accepted length text partitioned monthly Reasonableness text_length_above_max_length_count max_count

Command-line examples

Please expand the section below to see the DQOps command-line examples to run or activate the monthly partition text length above max length data quality check.

Managing monthly partition text length above max length check from DQOps shell

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 -col=column_name -ch=monthly_partition_text_length_above_max_length --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_* -col=column_name -ch=monthly_partition_text_length_above_max_length --enable-warning

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_partition_text_length_above_max_length --enable-warning
                    -Wmax_count=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 -col=column_name -ch=monthly_partition_text_length_above_max_length --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_* -col=column_name -ch=monthly_partition_text_length_above_max_length --enable-error

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

dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -col=column_name -ch=monthly_partition_text_length_above_max_length --enable-error
                    -Emax_count=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_text_length_above_max_length check on all tables and columns on a single data source.

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

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_text_length_above_max_length

You can also run this check on all tables (and columns) on which the monthly_partition_text_length_above_max_length check is enabled using patterns to find tables.

dqo> check run -c=connection_name -t=schema_prefix*.fact_* -col=column_name_* -ch=monthly_partition_text_length_above_max_length

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
  columns:
    target_column:
      partitioned_checks:
        monthly:
          text:
            monthly_partition_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
    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 text_length_above_max_length_count data quality sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
ClickHouse
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Databricks
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DB2
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
DuckDB
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
HANA
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
    TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MariaDB
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
MySQL
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Oracle
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
PostgreSQL
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Presto
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
QuestDB
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Redshift
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Snowflake
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Spark
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
SQL Server
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY 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)
ORDER BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
Trino
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_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
  columns:
    target_column:
      partitioned_checks:
        monthly:
          text:
            monthly_partition_text_length_above_max_length:
              parameters:
                max_length: 100
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 100
      labels:
      - This is the column that is analyzed for data quality issues
    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 text_length_above_max_length_count sensor.

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

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table.`target_column`) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(toString(analyzed_table."target_column")) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/db2.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE)) AS time_period,
    TIMESTAMP(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS DATE))) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table') }} AS VARCHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/hana.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN) AS time_period,
    TO_TIMESTAMP(SERIES_ROUND(CAST(original_table."date_column" AS DATE), 'INTERVAL 1 MONTH', ROUND_DOWN)) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/mysql.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS CHAR)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS CHAR)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/oracle.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS time_period,
    CAST(TRUNC(CAST(original_table."date_column" AS DATE), 'MONTH') AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/presto.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_database"."<target_schema>"."<target_table>" original_table
) analyzed_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
{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table')}}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM(
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    COALESCE(SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ), 0.0) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2,
    time_period,
    time_period_utc
FROM(
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    CAST(DATE_TRUNC('month', original_table."date_column") AS DATE) AS time_period,
    CAST((CAST(DATE_TRUNC('month', original_table."date_column") AS DATE)) AS TIMESTAMP WITH TIME ZONE) AS time_period_utc
    FROM "<target_table>" original_table
) analyzed_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
{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_target_column('analyzed_table') }}::VARCHAR) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column"::VARCHAR) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS STRING)) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table.`target_column` AS STRING)) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LEN({{ lib.render_target_column('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LEN(analyzed_table.[target_column]) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
GROUP BY 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)
ORDER BY level_1, level_2DATEFROMPARTS(YEAR(CAST(analyzed_table.[date_column] AS date)), MONTH(CAST(analyzed_table.[date_column] AS date)), 1)
Teradata
{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST({{ lib.render_target_column('analyzed_table')}} AS VARCHAR(4096))) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(CAST(analyzed_table."target_column" AS VARCHAR(4096))) > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,
    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
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
{% import '/dialects/trino.sql.jinja2' as lib with context -%}

SELECT
    SUM(
        CASE
            WHEN LENGTH({{ lib.render_column_cast_to_string('analyzed_table')}}) > {{(parameters.max_length)}}
                THEN 1
            ELSE 0
        END
    ) AS actual_value
    {{- lib.render_data_grouping_projections_reference('analyzed_table') }}
    {{- lib.render_time_dimension_projection_reference('analyzed_table') }}
FROM (
    SELECT
        original_table.*
        {{- lib.render_data_grouping_projections('original_table') }}
        {{- lib.render_time_dimension_projection('original_table') }}
    FROM {{ lib.render_target_table() }} original_table
) analyzed_table
{{- lib.render_where_clause() -}}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}
SELECT
    SUM(
        CASE
            WHEN LENGTH(analyzed_table."target_column") > 100
                THEN 1
            ELSE 0
        END
    ) AS actual_value,

                analyzed_table.grouping_level_1,

                analyzed_table.grouping_level_2
,
    time_period,
    time_period_utc
FROM (
    SELECT
        original_table.*,
    original_table."country" AS grouping_level_1,
    original_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS time_period,
    CAST(DATE_TRUNC('MONTH', CAST(original_table."date_column" AS date)) AS TIMESTAMP) AS time_period_utc
    FROM "your_trino_catalog"."<target_schema>"."<target_table>" original_table
) analyzed_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