reload lag

reload lag checks

Description
Table level check that calculates maximum difference in days between ingestion timestamp and event timestamp rows. This check should be executed only as a partitioned check because this check finds the longest delay between the time that the row was created in the data source and the timestamp when the row was loaded into its daily or monthly partition. This check will detect that a daily or monthly partition was reloaded, setting also the most recent timestamps in the created_at, loaded_at, inserted_at or other similar columns filled by the data pipeline or an ETL process during data loading.

---

daily partition reload lag

Check description
Daily partitioned check calculating the longest time a row waited to be load

Check name	Check type	Time scale	Sensor definition	Quality rule
daily_partition_reload_lag	partitioned	daily	partition_reload_lag	max_days

Enable check (Shell)
To enable this check provide connection name and check name in check enable command

dqo> check enable -c=connection_name -ch=daily_partition_reload_lag

Run check (Shell)
To run this check provide check name in check run command

dqo> check run -ch=daily_partition_reload_lag

It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below

dqo> check run -c=connection_name -ch=daily_partition_reload_lag

It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below

dqo> check run -c=connection_name -t=table_name -ch=daily_partition_reload_lag

It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below

dqo> check run -c=connection_name -t=table_name -col=column_name -ch=daily_partition_reload_lag

Check structure (Yaml)

  partitioned_checks:
    daily:
      timeliness:
        daily_partition_reload_lag:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  partitioned_checks:
    daily:
      timeliness:
        daily_partition_reload_lag:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            DAY
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    CAST(analyzed_table.`` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`` 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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" 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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    CAST(analyzed_table."" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."" 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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            analyzed_table.[col_inserted_at]
        )
    ) / 24.0 / 3600.0 AS actual_value,
    CAST(analyzed_table.[] AS date) AS time_period,
    CAST((CAST(analyzed_table.[] 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.[] AS date), CAST(analyzed_table.[] AS date)
ORDER BY CAST(analyzed_table.[] AS date)

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  partitioned_checks:
    daily:
      timeliness:
        daily_partition_reload_lag:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            DAY
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    CAST(analyzed_table.`` AS DATE) AS time_period,
    TIMESTAMP(CAST(analyzed_table.`` 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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-%d 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" 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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."" AS date) AS time_period,
    CAST((CAST(analyzed_table."" 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    CAST(analyzed_table."" AS date) AS time_period,
    TO_TIMESTAMP(CAST(analyzed_table."" 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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            analyzed_table.[col_inserted_at]
        )
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    CAST(analyzed_table.[] AS date) AS time_period,
    CAST((CAST(analyzed_table.[] 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.[] AS date), CAST(analyzed_table.[] AS date)
ORDER BY level_1, level_2CAST(analyzed_table.[] AS date)

---

monthly partition reload lag

Check description
Monthly partitioned check calculating the longest time a row waited to be load

Check name	Check type	Time scale	Sensor definition	Quality rule
monthly_partition_reload_lag	partitioned	monthly	partition_reload_lag	max_days

Enable check (Shell)
To enable this check provide connection name and check name in check enable command

dqo> check enable -c=connection_name -ch=monthly_partition_reload_lag

Run check (Shell)
To run this check provide check name in check run command

dqo> check run -ch=monthly_partition_reload_lag

It is also possible to run this check on a specific connection. In order to do this, add the connection name to the below

dqo> check run -c=connection_name -ch=monthly_partition_reload_lag

It is additionally feasible to run this check on a specific table. In order to do this, add the table name to the below

dqo> check run -c=connection_name -t=table_name -ch=monthly_partition_reload_lag

It is furthermore viable to combine run this check on a specific column. In order to do this, add the column name to the below

dqo> check run -c=connection_name -t=table_name -col=column_name -ch=monthly_partition_reload_lag

Check structure (Yaml)

  partitioned_checks:
    monthly:
      timeliness:
        monthly_partition_reload_lag:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  partitioned_checks:
    monthly:
      timeliness:
        monthly_partition_reload_lag:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            DAY
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    DATE_TRUNC(CAST(analyzed_table.`` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`` 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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            analyzed_table.[col_inserted_at]
        )
    ) / 24.0 / 3600.0 AS actual_value,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] 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.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[]), 0)
ORDER BY DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1)

Configuration with data grouping

Click to see more

Sample configuration (Yaml)

# yaml-language-server: $schema=https://cloud.dqo.ai/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
  timestamp_columns:
    event_timestamp_column: col_event_timestamp
    ingestion_timestamp_column: col_inserted_at
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  default_grouping_name: group_by_country_and_state
  groupings:
    group_by_country_and_state:
      level_1:
        source: column_value
        column: country
      level_2:
        source: column_value
        column: state
  partitioned_checks:
    monthly:
      timeliness:
        monthly_partition_reload_lag:
          warning:
            max_days: 1.0
          error:
            max_days: 2.0
          fatal:
            max_days: 1.0
  columns:
    col_event_timestamp:
      labels:
      - optional column that stores the timestamp when the event/transaction happened
    col_inserted_at:
      labels:
      - optional column that stores the timestamp when row was ingested
    country:
      labels:
      - column used as the first grouping key
    state:
      labels:
      - column used as the second grouping key

BigQuery

Sensor template for BigQueryRendered SQL for BigQuery

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMP_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATE_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            DAY
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATETIME_DIFF(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            SAFE_CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMP_DIFF(
            SAFE_CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP),
            SAFE_CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            MILLISECOND
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_TRUNC(CAST(analyzed_table.`` AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(analyzed_table.`` 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

MySQL

Sensor template for MySQLRendered SQL for MySQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
        DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }} AS TIMESTAMP),
            CAST({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
         TIMESTAMPDIFF(
            SECOND,
            CAST(analyzed_table.`col_event_timestamp` AS TIMESTAMP),
            CAST(analyzed_table.`col_inserted_at` AS TIMESTAMP)
        )
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.`country` AS grouping_level_1,
    analyzed_table.`state` AS grouping_level_2,
    DATE_FORMAT(analyzed_table.``, '%Y-%m-01 00:00:00') AS time_period,
    FROM_UNIXTIME(UNIX_TIMESTAMP(DATE_FORMAT(analyzed_table.``, '%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

PostgreSQL

Sensor template for PostgreSQLRendered SQL for PostgreSQL

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

Redshift

Sensor template for RedshiftRendered SQL for Redshift

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        EXTRACT(EPOCH FROM (
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }} - {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}
        ))
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        EXTRACT(EPOCH FROM (
            ({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})::TIMESTAMP - ({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }})::TIMESTAMP
        ))
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        EXTRACT(EPOCH FROM (
            (analyzed_table."col_inserted_at")::TIMESTAMP - (analyzed_table."col_event_timestamp")::TIMESTAMP
        ))
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    CAST((DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

Sensor template for SnowflakeRendered SQL for Snowflake

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- else -%}
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }}),
            TRY_TO_TIMESTAMP({{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }})
        )
    ) / 24.0 / 3600.0 / 1000.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        TIMESTAMPDIFF(
            MILLISECOND,
            TRY_TO_TIMESTAMP(analyzed_table."col_event_timestamp"),
            TRY_TO_TIMESTAMP(analyzed_table."col_inserted_at")
        )
    ) / 24.0 / 3600.0 / 1000.0 AS actual_value,
    analyzed_table."country" AS grouping_level_1,
    analyzed_table."state" AS grouping_level_2,
    DATE_TRUNC('MONTH', CAST(analyzed_table."" AS date)) AS time_period,
    TO_TIMESTAMP(DATE_TRUNC('MONTH', CAST(analyzed_table."" 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

SQL Server

Sensor template for SQL ServerRendered SQL for SQL Server

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

{% macro render_ingestion_event_diff() -%}
    {%- if lib.is_instant(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_instant(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- elif lib.is_local_date(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(
            DAY,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    )
    {%- elif lib.is_local_date_time(table.columns[table.timestamp_columns.ingestion_timestamp_column].type_snapshot.column_type) == 'true'
    and lib.is_local_date_time(table.columns[table.timestamp_columns.event_timestamp_column].type_snapshot.column_type) == 'true' -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- else -%}
    MAX(
        DATEDIFF(SECOND,
            {{ lib.render_column(table.timestamp_columns.event_timestamp_column, 'analyzed_table') }},
            {{ lib.render_column(table.timestamp_columns.ingestion_timestamp_column, 'analyzed_table') }}
        )
    ) / 24.0 / 3600.0
    {%- endif -%}
{%- endmacro -%}

SELECT
    {{ render_ingestion_event_diff() }} 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
    MAX(
        DATEDIFF(SECOND,
            analyzed_table.[col_event_timestamp],
            analyzed_table.[col_inserted_at]
        )
    ) / 24.0 / 3600.0 AS actual_value,
    analyzed_table.[country] AS grouping_level_1,
    analyzed_table.[state] AS grouping_level_2,
    DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1) AS time_period,
    CAST((DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] 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.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1), DATEADD(month, DATEDIFF(month, 0, analyzed_table.[]), 0)
ORDER BY level_1, level_2DATEFROMPARTS(YEAR(CAST(analyzed_table.[] AS date)), MONTH(CAST(analyzed_table.[] AS date)), 1)

---