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Last updated: July 22, 2025

DQOps data quality accepted values sensors, SQL examples

All data quality sensors in the accepted values category supported by DQOps are listed below. Those sensors are measured on a column level.

expected numbers in use count

Column level sensor that counts how many expected numeric values are used in a tested column. Finds unique column values from the set of expected numeric values and counts them. This sensor is useful to analyze numeric columns that have a low number of unique values and it should be tested if all possible values from the list of expected values are used in any row. The typical types of tested columns are numeric status or type columns.

Sensor summary

The expected numbers in use count sensor is documented below.

Target Category Full sensor name Source code on GitHub
column accepted_values column/accepted_values/expected_numbers_in_use_count sensors/column/accepted_values

Sensor parameters

Field name Description Allowed data type Required Allowed values
expected_values List of expected numeric values that should be found in the tested column. integer_list

Jinja2 SQL templates

The templates used to generate the SQL query for each data source supported by DQOps is shown below.

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_actual_value() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT_DISTINCT(
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{render_actual_value()}} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT_BIG(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT_BIG(*) = 0 THEN MAX(0)
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values | length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

expected text values in use count

Column level sensor that counts how many expected string values are used in a tested column. Finds unique column values from the set of expected string values and counts them. This sensor is useful to analyze string columns that have a low number of unique values and it should be tested if all possible values from the list of expected values are used in any row. The typical type of columns analyzed using this sensor are currency, country, status or gender columns.

Sensor summary

The expected text values in use count sensor is documented below.

Target Category Full sensor name Source code on GitHub
column accepted_values column/accepted_values/expected_text_values_in_use_count sensors/column/accepted_values

Sensor parameters

Field name Description Allowed data type Required Allowed values
expected_values List of expected string values that should be found in the tested column. string_list

Jinja2 SQL templates

The templates used to generate the SQL query for each data source supported by DQOps is shown below.

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{ lib.make_text_constant(i) }},
        {%- else -%}
            {{ lib.make_text_constant(i) }}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
        COUNT(DISTINCT
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
                ELSE NULL
            END
        )
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{ render_else() }}
    END AS actual_value,
    MAX(CAST({{ parameters.expected_values | length }} AS INT)) AS expected_value_alias
    {{- 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0)
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CAST({{ actual_value() }} AS BIGINT) AS actual_value,
    MAX({{ parameters.expected_values | length }} ) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN MAX(0)
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN MAX(0)
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0)
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CAST({{ actual_value() }} AS BIGINT) AS actual_value,
    MAX({{ parameters.expected_values | length }} ) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_actual_value() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT_DISTINCT(
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    {{render_actual_value()}} AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{ lib.make_text_constant(i) }},
        {%- else -%}
            {{ lib.make_text_constant(i) }}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
        COUNT(DISTINCT
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
                ELSE NULL
            END
        )
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{ render_else() }}
    END AS actual_value,
    MAX(CAST({{ parameters.expected_values | length }} AS INT)) AS expected_value_alias
    {{- 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT_BIG(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CASE
        WHEN COUNT_BIG(*) = 0 THEN NULL
        ELSE {{render_else()}}
    END AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{ lib.make_text_constant(i) }},
        {%- else -%}
            {{ lib.make_text_constant(i) }}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_else() -%}
    {%- if parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
        COUNT(DISTINCT
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
                ELSE NULL
            END
        )
    {%- endif -%}
{%- endmacro -%}

SELECT
    CASE
        WHEN COUNT(*) = 0 THEN NULL
        ELSE {{ render_else() }}
    END AS actual_value,
    MAX(CAST({{ parameters.expected_values | length }} AS INT)) AS expected_value_alias
    {{- 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    0
    {%- else -%}
    COUNT(DISTINCT
        CASE
            WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                THEN {{ lib.render_target_column('analyzed_table') }}
            ELSE NULL
        END
    )
    {%- endif -%}
{% endmacro -%}

SELECT
    CAST({{ actual_value() }} AS BIGINT) AS actual_value,
    MAX({{ parameters.expected_values | length }} ) AS expected_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() -}}

expected texts in top values count

Column level sensor that counts how many expected string values are among the TOP most popular values in the column. The sensor will first count the number of occurrences of each column's value and will pick the TOP X most popular values (configurable by the 'top' parameter). Then, it will compare the list of most popular values to the given list of expected values that should be most popular. This sensor will return the number of expected values that were found within the 'top' most popular column values. This sensor is useful in analyzing string columns with frequently occurring values, such as country codes for countries with the most customers. The sensor can detect if any of the most popular value (an expected value) is no longer one of the top X most popular values.

Sensor summary

The expected texts in top values count sensor is documented below.

Target Category Full sensor name Source code on GitHub
column accepted_values column/accepted_values/expected_texts_in_top_values_count sensors/column/accepted_values

Sensor parameters

Field name Description Allowed data type Required Allowed values
expected_values List of expected string values that should be found in the tested column among the TOP most popular (highest distinct count) column values. string_list
top The number of the most popular values (with the highest distinct count) that are analyzed to find the expected values. long

Jinja2 SQL templates

The templates used to generate the SQL query for each data source supported by DQOps is shown below.

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value AS top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period AS time_period,
        top_col_values.time_period_utc AS time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) AS top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections_reference('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table', indentation = '            ') }}
        FROM (
                SELECT
                    original_table.*,
                    {{ lib.render_target_column('original_table') }} AS top_value
                    {{- lib.render_data_grouping_projections('original_table') }}
                    {{- lib.render_time_dimension_projection('original_table') }}
                FROM {{ lib.render_target_table() }} original_table
                {{- lib.render_where_clause(extra_filter = lib.render_target_column('original_table') ~ ' IS NOT NULL', table_alias_prefix='original_table') }}
            ) analyzed_table
        {{- lib.render_where_clause(indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true and lib.data_groupings is not none -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections_reference('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table', indentation = '            ') }}
        FROM (
                SELECT
                    original_table.*,
                    {{ lib.render_target_column('original_table') }} AS top_value
                    {{- lib.render_data_grouping_projections('original_table') }}
                    {{- lib.render_time_dimension_projection('original_table') }}
                FROM {{ lib.render_target_table() }} original_table
                {{- lib.render_where_clause(extra_filter = lib.render_target_column('original_table') ~ ' IS NOT NULL', table_alias_prefix='original_table') }}
            ) analyzed_table
        {{- lib.render_where_clause(indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true and lib.data_groupings is not none -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
        FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
        FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period time_period,
        top_col_values.time_period_utc time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} top_value,
            COUNT(*) total_values
            {{- lib.render_data_grouping_projections_reference('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table', indentation = '            ') }}
        FROM
        (
            SELECT
            additional_table.*,
            {{ lib.render_target_column('additional_table') }} top_value
            {{- lib.render_data_grouping_projections('additional_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('additional_table', indentation = '            ') }}
            FROM {{ lib.render_target_table() }} additional_table) analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) top_col_values
) top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL actual_value,
    MAX(0) expected_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
    {{- lib.render_where_clause(table_alias_prefix='original_table') }}) analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) actual_value,
    MAX({{ parameters.expected_values | length }}) expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections_reference('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table', indentation = '            ') }}
        FROM (
                SELECT
                    original_table.*,
                    {{ lib.render_target_column('original_table') }} AS top_value
                    {{- lib.render_data_grouping_projections('original_table') }}
                    {{- lib.render_time_dimension_projection('original_table') }}
                FROM {{ lib.render_target_table() }} original_table
                {{- lib.render_where_clause(extra_filter = lib.render_target_column('original_table') ~ ' IS NOT NULL', table_alias_prefix='original_table') }}
            ) analyzed_table
        {{- lib.render_where_clause(indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT() AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_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
{%- else %}
    COUNT_DISTINCT(
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

{% import '/dialects/spark.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT_BIG(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} {{ lib.render_target_column('analyzed_table') }}
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    NULL AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT_BIG(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{% if lib.time_series is not none -%}
GROUP BY time_period, time_period_utc
{%- endif -%}
{%- if (lib.data_groupings is not none and (lib.data_groupings | length) > 0) -%}
    {% if lib.time_series is none %}GROUP BY {% endif -%}
    {%- for attribute in lib.data_groupings -%}
    {{ ', ' if lib.time_series is not none and loop.index == 1 else "" }}top_values.grouping_{{ attribute }}
    {%- endfor -%}
{%- endif -%}

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            {{ lib.render_target_column('analyzed_table') }} AS top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection('analyzed_table', indentation = '            ') }}
        FROM
            {{ lib.render_target_table() }} AS analyzed_table
        {{- lib.render_where_clause(extra_filter = lib.render_target_column('analyzed_table') ~ ' IS NOT NULL', indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{%- endmacro -%}

{%- macro render_from_subquery() -%}
FROM
(
    SELECT
        top_col_values.top_value as top_value,
        {% if lib.time_series is not none -%}
        top_col_values.time_period as time_period,
        top_col_values.time_period_utc as time_period_utc,
        {% endif -%}
        RANK() OVER({{- render_data_grouping('top_col_values', indentation = ' ', partition_by_enabled=true) }}
            ORDER BY top_col_values.total_values DESC) as top_values_rank {{- render_data_grouping('top_col_values', indentation = ' ') }}
    FROM
    (
        SELECT
            top_value,
            COUNT(*) AS total_values
            {{- lib.render_data_grouping_projections_reference('analyzed_table', indentation = '            ') }}
            {{- lib.render_time_dimension_projection_reference('analyzed_table', indentation = '            ') }}
        FROM (
                SELECT
                    original_table.*,
                    {{ lib.render_target_column('original_table') }} AS top_value
                    {{- lib.render_data_grouping_projections('original_table') }}
                    {{- lib.render_time_dimension_projection('original_table') }}
                FROM {{ lib.render_target_table() }} original_table
                {{- lib.render_where_clause(extra_filter = lib.render_target_column('original_table') ~ ' IS NOT NULL', table_alias_prefix='original_table') }}
            ) analyzed_table
        {{- lib.render_where_clause(indentation = '        ') }}
        GROUP BY {{ render_grouping_columns() -}} top_value
        ORDER BY {{ render_grouping_columns() -}} total_values DESC
    ) AS top_col_values
) AS top_values
WHERE top_values_rank <= {{ parameters.top }}
{%- endmacro -%}

{% macro render_grouping_columns() %}
    {%- if (lib.data_groupings is not none and (lib.data_groupings | length()) > 0) or lib.time_series is not none -%}
        {{ lib.render_grouping_column_names() }} {{- ', ' -}}
    {%- endif -%}
{% endmacro %}

{%- macro render_data_grouping(table_alias_prefix = '', indentation = '', partition_by_enabled = false) -%}

    {%- if partition_by_enabled == true -%}PARTITION BY
        {%- if lib.time_series is not none -%}
            {{" "}}top_col_values.time_period
        {%- elif lib.data_groupings is none -%}
            {{" "}}NULL
        {%- endif -%}
    {%- endif -%}

    {%- if lib.data_groupings is not none and (lib.data_groupings | length()) > 0 -%}
        {%- for attribute in lib.data_groupings -%}
            {{- "" if loop.first and lib.time_series is none and partition_by_enabled else "," -}}
            {%- with data_grouping_level = lib.data_groupings[attribute] -%}
                {%- if data_grouping_level.source == 'tag' -%}
                    {{ indentation }}{{ lib.make_text_constant(data_grouping_level.tag) }}
                {%- elif data_grouping_level.source == 'column_value' -%}
                    {{ indentation }}{{ table_alias_prefix }}.grouping_{{ attribute }}
                {%- endif -%}
            {%- endwith %}
        {%- endfor -%}
    {%- endif -%}
{%- endmacro -%}

SELECT
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 %}
    MAX(1 + NULL) AS actual_value,
    MAX(0) AS expected_value
    {{- lib.render_data_grouping_projections('analyzed_table') }}
    {{- lib.render_time_dimension_projection('analyzed_table') }}
FROM {{ lib.render_target_table() }} AS analyzed_table
{%- else %}
    COUNT(DISTINCT
        CASE
            WHEN top_values.top_value IN ({{ extract_in_list(parameters.expected_values) }}) THEN top_values.top_value
            ELSE NULL
        END
    ) AS actual_value,
    MAX({{ parameters.expected_values | length }}) AS expected_value
    {%- if lib.time_series is not none -%} {{- "," }}
    top_values.time_period,
    top_values.time_period_utc
    {%- endif -%}
    {{- render_data_grouping('top_values', indentation = lib.eol() ~ '    ') }}
{{ render_from_subquery() }}
{%- endif -%}
{{- lib.render_group_by() -}}
{{- lib.render_order_by() -}}

number found in set percent

Column level sensor that calculates the percentage of rows for which the tested numeric column contains a value from the list of expected values. Columns with null values are also counted as a passing value (the sensor assumes that a 'null' is also an expected and accepted value). This sensor is useful for checking numeric columns that store numeric codes (such as status codes) that the only values found in the column are from a set of expected values.

Sensor summary

The number found in set percent sensor is documented below.

Target Category Full sensor name Source code on GitHub
column accepted_values column/accepted_values/number_found_in_set_percent sensors/column/accepted_values

Sensor parameters

Field name Description Allowed data type Required Allowed values
expected_values A list of expected values that must be present in a numeric column, only values from this list are accepted and rows having these values in the tested column are counted as valid rows. integer_list

Jinja2 SQL templates

The templates used to generate the SQL query for each data source supported by DQOps is shown below.

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN ({{lib.render_target_column('analyzed_table')}} IN ({{extract_in_list(parameters.expected_values)}}))
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro actual_value() -%}
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN ({{lib.render_target_column('analyzed_table')}} IN ({{extract_in_list(parameters.expected_values)}}))
                    THEN 1
                ELSE 0
            END
          ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(CAST(0.0 AS DOUBLE))
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro actual_value() -%}
{%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT() = 0 AND value set is empty? This solution is the most convenient. -#}
    COALESCE(MAX(0.0), 0.0)
    {%- else -%}
    COALESCE(100.0 * SUM(
            CASE
                WHEN ({{lib.render_target_column('analyzed_table')}} IN ({{extract_in_list(parameters.expected_values)}}))
                    THEN 1
                ELSE 0
            END
          ) / COUNT({{ lib.render_target_column('analyzed_table') }}), 100.0)
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
        {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
        MAX(0.0)
        {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN ({{lib.render_target_column('analyzed_table')}} IN ({{extract_in_list(parameters.expected_values)}}))
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN ({{lib.render_target_column('analyzed_table')}} IN ({{extract_in_list(parameters.expected_values)}}))
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{values_list|join(', ')}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN ({{lib.render_target_column('analyzed_table')}} IN ({{extract_in_list(parameters.expected_values)}}))
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {{ values_list|join(', ') -}}
{% endmacro %}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    {#- Two approaches can be taken here. What if COUNT(*) = 0 AND value set is empty? This solution is the most convenient. -#}
    MAX(CAST(0.0 AS DOUBLE))
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

text found in set percent

Column level sensor that calculates the percentage of rows for which the tested string (text) column contains a value from the list of expected values. Columns with null values are also counted as a passing value (the sensor assumes that a 'null' is also an expected and accepted value). This sensor is useful for testing that a string column with a low number of unique values (country, currency, state, gender, etc.) contains only values from a set of expected values.

Sensor summary

The text found in set percent sensor is documented below.

Target Category Full sensor name Source code on GitHub
column accepted_values column/accepted_values/text_found_in_set_percent sensors/column/accepted_values

Sensor parameters

Field name Description Allowed data type Required Allowed values
expected_values A list of expected values that must be present in a string column, only values from this list are accepted and rows having these values in the tested column are counted as valid rows. string_list

Jinja2 SQL templates

The templates used to generate the SQL query for each data source supported by DQOps is shown below.

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}


{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}


{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(CAST(0.0 AS DOUBLE))
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    COALESCE(100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }}), 100.0)
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

{% import '/dialects/spark.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(0.0)
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

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

{%- macro extract_in_list(values_list) -%}
    {%- for i in values_list -%}
        {%- if not loop.last -%}
            {{lib.make_text_constant(i)}}{{", "}}
        {%- else -%}
            {{lib.make_text_constant(i)}}
        {%- endif -%}
    {%- endfor -%}
{% endmacro -%}

{%- macro actual_value() -%}
    {%- if 'expected_values' not in parameters or parameters.expected_values|length == 0 -%}
    MAX(CAST(0.0 AS DOUBLE))
    {%- else -%}
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN {{ lib.render_target_column('analyzed_table') }} IN ({{ extract_in_list(parameters.expected_values) }})
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    END
    {%- endif -%}
{% endmacro -%}

SELECT
    {{ actual_value() }} 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() -}}

text valid country code percent

Column level sensor that calculates the percentage of rows with text values with a valid country codes in an analyzed column.

Sensor summary

The text valid country code percent sensor is documented below.

Target Category Full sensor name Source code on GitHub
column accepted_values column/accepted_values/text_valid_country_code_percent sensors/column/accepted_values

Jinja2 SQL templates

The templates used to generate the SQL query for each data source supported by DQOps is shown below.

{% import '/dialects/bigquery.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/clickhouse.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN upperUTF8({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/db2.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/hana.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/mariadb.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/mysql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/oracle.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/presto.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT COALESCE(100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }}), 100.0)
    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() -}}

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/trino.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('AF',   'AL',   'DZ',   'AS',   'AD',   'AO',   'AI',   'AQ',   'AG',   'AR',   'AM',   'AW',   'AU',   'AT',   'AZ',   'BS',   'BH',   'BD',   'BB',   'BY',   'BE',   'BZ',   'BJ',   'BM',   'BT',   'BO',   'BA',   'BW',   'BR',   'IO',   'VG',   'BN',   'BG',   'BF',   'BI',   'KH',   'CM',   'CA',   'CV',   'KY',   'CF',   'TD',   'CL',   'CN',   'CX',   'CC',   'CO',   'KM',   'CK',   'CR',   'HR',   'CU',   'CW',   'CY',   'CZ',   'CD',   'DK',   'DJ',   'DM',   'DO',   'TL',   'EC',   'EG',   'SV',   'GQ',   'ER',   'EE',   'ET',   'FK',   'FO',   'FJ',   'FI',   'FR',   'PF',   'GA',   'GM',   'GE',   'DE',   'GH',   'GI',   'GR',   'GL',   'GD',   'GU',   'GT',   'GG',   'GN',   'GW',   'GY',   'HT',   'HN',   'HK',   'HU',   'IS',   'IN',   'ID',   'IR',   'IQ',   'IE',   'IM',   'IL',   'IT',   'CI',   'JM',   'JP',   'JE',   'JO',   'KZ',   'KE',   'KI',   'XK',   'KW',   'KG',   'LA',   'LV',   'LB',   'LS',   'LR',   'LY',   'LI',   'LT',   'LU',   'MO',   'MK',   'MG',   'MW',   'MY',   'MV',   'ML',   'MT',   'MH',   'MR',   'MU',   'YT',   'MX',   'FM',   'MD',   'MC',   'MN',   'ME',   'MS',   'MA',   'MZ',   'MM',   'NA',   'NR',   'NP',   'NL',   'AN',   'NC',   'NZ',   'NI',   'NE',   'NG',   'NU',   'KP',   'MP',   'NO',   'OM',   'PK',   'PW',   'PS',   'PA',   'PG',   'PY',   'PE',   'PH', 'PN', 'PL',   'PT',   'PR',   'QA',   'CG',   'RE',   'RO',   'RU',   'RW',   'BL',   'SH',   'KN',   'LC',   'MF',   'PM',   'VC',   'WS',   'SM',   'ST',   'SA',   'SN',   'RS',   'SC',   'SL',   'SG',   'SX',   'SK',   'SI',   'SB',   'SO',   'ZA',   'KR',   'SS',   'ES',   'LK',   'SD',   'SR',   'SJ',   'SZ',   'SE',   'CH',   'SY',   'TW',   'TJ',   'TZ',   'TH',   'TG',   'TK',   'TO',   'TT',   'TN',   'TR',   'TM',   'TC',   'TV',   'VI',   'UG',   'UA',   'AE',   'GB',   'US',   'UY',   'UZ',   'VU',   'VA',   'VE',   'VN',   'WF',   'EH',   'YE',   'ZM',   'ZW')
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

text valid currency code percent

Column level sensor that calculates the percentage of rows with text values that are valid currency codes in an analyzed column.

Sensor summary

The text valid currency code percent sensor is documented below.

Target Category Full sensor name Source code on GitHub
column accepted_values column/accepted_values/text_valid_currency_code_percent sensors/column/accepted_values

Jinja2 SQL templates

The templates used to generate the SQL query for each data source supported by DQOps is shown below.

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_column_cast_to_string('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN upperUTF8({{ lib.render_column_cast_to_string('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/databricks.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table') }}) IN ('ALL', 'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/duckdb.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_column_cast_to_string('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table') }}) IN ('ALL', 'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table') }}) IN ('ALL', 'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table') }}) IN ('ALL', 'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/postgresql.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_column_cast_to_string('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/questdb.sql.jinja2' as lib with context -%}
SELECT COALESCE(100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }}), 100.0)
    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() -}}

{% import '/dialects/redshift.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/snowflake.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/spark.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/sqlserver.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT_BIG({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT_BIG({{ lib.render_target_column('analyzed_table') }})
    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() -}}

{% import '/dialects/teradata.sql.jinja2' as lib with context -%}
SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_target_column('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

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

SELECT
    CASE
        WHEN COUNT({{ lib.render_target_column('analyzed_table') }}) = 0 THEN 100.0
        ELSE CAST(100.0 * SUM(
            CASE
                WHEN UPPER({{ lib.render_column_cast_to_string('analyzed_table')}}) IN ('ALL',  'AFN',  'ARS',  'AWG',  'AUD',  'AZN',  'BSD',  'BBD',  'BYN',  'BZD',  'BMD',  'BOB',  'BAM',  'BWP',  'BGN',  'BRL',  'BND',  'KHR',  'CAD',  'KYD',  'CLP',  'CNY',  'COP',  'CRC',  'HRK',  'CUP',  'CZK',  'DKK',  'DOP',  'XCD',  'EGP',  'SVC',  'EUR',  'FKP',  'FJD',  'GHS',  'GIP',  'GTQ',  'GGP',  'GYD',  'HNL',  'HKD',  'HUF',  'ISK',  'INR',  'IDR',  'IRR',  'IMP',  'ILS',  'JMD',  'JPY',  'JEP',  'KZT',  'KPW',  'KRW',  'KGS',  'LAK',  'LBP',  'LRD',  'MKD',  'MYR',  'MUR',  'MXN',  'MNT',  'MZN',  'NAD',  'NPR',  'ANG',  'NZD',  'NIO',  'NGN',  'NOK',  'OMR',  'PKR',  'PAB',  'PYG',  'PEN',  'PHP',  'PLN',  'QAR',  'RON',  'RUB',  'SHP',  'SAR',  'RSD',  'SCR',  'SGD',  'SBD',  'SOS',  'ZAR',  'LKR',  'SEK',  'CHF',  'SRD',  'SYP',  'TWD',  'THB',  'TTD',  'TRY',  'TVD',  'UAH',  'AED',  'GBP',  'USD',  'UYU',  'UZS',  'VEF',  'VND',  'YER',  'ZWD',  'LEK',  '؋',    '$',    'Ƒ',    '₼',    'BR',   'BZ$',  '$B',   'KM',   'P',    'ЛВ',   'R$',   '៛',    '¥',    '₡',    'KN',   '₱',    'KČ',   'KR',   'RD$', '£', '€',    '¢',    'Q',    'L',    'FT',   '₹',    'RP',   '﷼',    '₪',    'J$',   '₩',    '₭',    'ДЕН',  'RM',   '₨',    '₮',    'د.إ',  'MT',   'C$',   '₦',    'B/.',  'GS',   'S/.', 'ZŁ',    'LEI',  'ДИН.', 'S',    'R',    'NT$',  '฿',    'TT$',  '₺',    '₴',    '$U',   'BS',   '₫', 'Z$')
                    THEN 1
                ELSE 0
            END
        ) AS DOUBLE) / COUNT({{ lib.render_target_column('analyzed_table') }})
    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() -}}

What's next