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dbt-audit-helper

Useful macros when performing data audits

Contents

• Installation instructions
• Compare Data Outputs
  • Compare and classify:
    • compare_and_classify_query_results
    • compare_and_classify_relation_rows
  • Quick identical check:
    • quick_are_queries_identical
    • quick_are_relations_identical
  • compare_row_counts
• Compare Columns
  • compare_column_values
  • compare_all_columns
  • Compare which columns differ:
    • compare_which_query_columns_differ
    • compare_which_relation_columns_differ
  • compare_relation_columns
• Advanced Usage
  • Print Output To Logs
  • Use Output For Custom Singular Test
• Legacy Macros
  • compare_queries
  • compare_relations
• Internal Macros

Installation instructions

New to dbt packages? Read more about them here.

1. Include this package in your packages.yml file — check here for the latest version number.
2. Run dbt deps to install the package.

Compare Data Outputs

compare_and_classify_query_results (source)

Generates a row-by-row comparison of two queries, as well as summary stats of added, removed, identical and modified records. This prevents you from having to query your comparison tables multiple times to get raw data and summary data.

Output

Note that there are 4 rows with the modified status, but dbt_audit_num_rows_in_status says 2. This is because it is counting each primary key only once.

Arguments

• a_query and b_query: The queries you want to compare.
• primary_key_columns (required): A list of primary key column(s) used to join the queries together for comparison.
• columns (required): The columns present in the two queries you want to compare.
• sample_limit: Number of sample records to return per status. Defaults to 20.

Usage

{% set old_query %}
  select
    id as order_id,
    amount,
    customer_id
  from old_database.old_schema.fct_orders
{% endset %}

{% set new_query %}
  select
    order_id,
    amount,
    customer_id
  from {{ ref('fct_orders') }}
{% endset %}

{{ 
  audit_helper.compare_and_classify_query_results(
    old_query, 
    new_query, 
    primary_key_columns=['order_id'], 
    columns=['order_id', 'amount', 'customer_id']
  )
}}

compare_and_classify_relation_rows (source)

A wrapper to compare_which_query_columns_differ, except it takes two Relations (instead of two queries).

Each relation must have the same columns with the same names, but they do not have to be in the same order.

Arguments

• a_relation and b_relation: The relations you want to compare.
• primary_key_columns (required): A list of primary key column(s) used to join the queries together for comparison.
• columns (optional): The columns present in the two queries you want to compare. Build long lists with a few exclusions with dbt_utils.get_filtered_columns_in_relation, or pass None and the macro will find all intersecting columns automatically.
• sample_limit: Number of sample records to return per status. Defaults to 20.

Usage

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{{ audit_helper.compare_and_classify_relation_rows(
    a_relation = old_relation,
    b_relation = dbt_relation,
    primary_key_columns = ["order_id"],
    columns = None
) }}

quick_are_queries_identical (source)

On supported adapters (currently Snowflake and BigQuery), take a hash of all rows in two queries and compare them.

This can be calculated relatively quickly compared to other macros in this package and can efficiently provide reassurance that a refactor introduced no changes.

Output

Arguments

• a_query and b_query: The queries you want to compare.
• columns (required): The columns present in the two queries you want to compare.

Usage

{% set old_query %}
    select * from old_database.old_schema.dim_product
{% endset %}

{% set new_query %}
    select * from {{ ref('dim_product') }}
{% endset %}

{{ audit_helper.compare_column_values(
    a_query = old_query,
    b_query = new_query,
    columns=['order_id', 'amount', 'customer_id']
  ) 
}}

quick_are_relations_identical (source)

A wrapper to quick_are_queries_identical, except it takes two Relations (instead of two queries).

Each relation must have the same columns with the same names, but they do not have to be in the same order. Build long lists with a few exclusions with dbt_utils.get_filtered_columns_in_relation, or pass None and the macro will find all intersecting columns automatically.

Usage

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{{ audit_helper.quick_are_relations_identical(
    a_relation = old_relation,
    b_relation = dbt_relation,
    columns = None
) }}

compare_row_counts (source)

This macro does a simple comparison of the row counts in two relations.

Output

Calling this macro on two different relations will return a very simple table comparing the row counts in each relation.

Arguments

• a_relation and b_relation: The Relations you want to compare.

Usage

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{{ audit_helper.compare_row_counts(
    a_relation = old_relation,
    b_relation = dbt_relation
) }}

Compare Columns

compare_which_query_columns_differ (source)

This macro generates SQL that can be used to detect which columns returned by two queries contain any value level changes.

It does not return the magnitude of the change, only whether or not a difference has occurred. Only records that exist in both queries (as determined by the primary key) are considered.

Output

The generated query returns whether or not each column has any differences:

Arguments

• a_query and b_query: The queries to compare
• primary_key_columns (required): A list of primary key column(s) used to join the queries together for comparison.
• columns (required): The columns present in the two queries you want to compare.

compare_which_relation_columns_differ (source)

A wrapper to compare_which_query_columns_differ, except it takes two Relations (instead of two queries).

Each relation must have the same columns with the same names, but they do not have to be in the same order. Build long lists with a few exclusions with dbt_utils.get_filtered_columns_in_relation, or pass None and the macro will find all intersecting columns automatically.

Usage

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{{ audit_helper.compare_which_relation_columns_differ(
    a_relation = old_relation,
    b_relation = dbt_relation,
    primary_key_columns = ["order_id"],
    columns = None
) }}

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{% set columns = dbt_utils.get_filtered_columns_in_relation(old_relation, exclude=["loaded_at"]) %}

{{ audit_helper.compare_which_relation_columns_differ(
    a_relation = old_relation,
    b_relation = dbt_relation,
    primary_key_columns = ["order_id"],
    columns = columns
) }}

compare_column_values (source)

This macro generates SQL that can be used to compare a column's values across two queries. This macro is useful when you've used the compare_which_query_columns_differ macro to identify a column with differing values and want to understand how many discrepancies are caused by that column.

Output

The generated query returns a summary of the count of rows where the column's values:

• match perfectly
• differ
• are null in a or b or both
• are missing from a or b

Arguments

• a_query and b_query: The queries you want to compare.
• primary_key: The primary key of the model. Used to sort unmatched results for row-by-row validation. Must be a unique key (unqiue and never null) in both tables, otherwise the join won't work as expected.
• column_to_compare: The column you want to compare.
• emojis (optional): Boolean argument that defaults to true and displays ✅, 🤷 and ❌ for easier visual scanning. If you don't want to include emojis in the output, set it to false.
• a_relation_name and b_relation_name (optional): Names of the queries you want displayed in the output. Default is a and b.

Usage

{% set old_query %}
    select * from old_database.old_schema.dim_product
    where is_latest
{% endset %}

{% set new_query %}
    select * from {{ ref('dim_product') }}
{% endset %}

{{ audit_helper.compare_column_values(
    a_query = old_query,
    b_query = new_query,
    primary_key = "product_id",
    column_to_compare = "status"
) }}

compare_all_columns (source)

Similar to compare_column_values, except it can be used to compare all columns' values across two relations. This macro is useful when you've used the compare_queries macro and found that a significant number of your records don't match and want to understand how many discrepancies are caused by each column.

Output

By default, the generated query returns a summary of the count of rows where the each column's values:

• match perfectly
• differ
• are null in a or b or both
• are missing from a or b

Setting the summarize argument to false lets you check the match status of a specific column value of a specifc row:

Arguments

• a_relation and b_relation: The relations you want to compare. Any two relations that have the same columns can be used.
• primary_key: The primary key of the model (or concatenated sql to create the primary key). Used to sort unmatched results for row-by-row validation. Must be a unique key (unique and never null) in both tables, otherwise the join won't work as expected.
• exclude_columns (optional): Any columns you wish to exclude from the validation.
• summarize (optional): Allows you to switch between a summary or detailed view of the compared data. Defaults to true.

Usage

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{{ audit_helper.compare_all_columns(
    a_relation = old_relation,
    b_relation = dbt_relation,
    primary_key = "order_id"
) }}

compare_relation_columns (source)

This macro generates SQL that can be used to compare the schema (ordinal position and data types of columns) of two relations. This is especially useful when:

• Comparing a new version of a relation with an old one, to make sure that the structure is the same
• Helping figure out why a union of two relations won't work (often because the data types are different)

Output

Note: For adapters other than BigQuery, Postgres, Redshift, and Snowflake, the ordinal position is inferred based on the response from dbt Core's adapter.get_columns_in_relation(), as opposed to being loaded from the information schema.

Arguments

• a_relation and b_relation: The relations you want to compare.

Usage

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{{ audit_helper.compare_relation_columns(
    a_relation=old_relation,
    b_relation=dbt_relation
) }}

Advanced Usage

Print Output To Logs

You may want to print the output of the query generated by an audit helper macro to your logc (instead of previewing the results).

To do so, you can alternatively store the results of your query and print it to the logs.

For example, using the compare_column_values macro:

{% set old_query %}
    select * from old_database.old_schema.dim_product
    where is_latest
{% endset %}

{% set new_query %}
    select * from {{ ref('dim_product') }}
{% endset %}

{% set audit_query = audit_helper.compare_column_values(
    a_query = old_query,
    b_query = new_query,
    primary_key = "product_id",
    column_to_compare = "status"
) %}

{% set audit_results = run_query(audit_query) %}

{% if execute %}
{% do audit_results.print_table() %}
{% endif %}

The .print_table() function is not compatible with dbt Cloud, so an adjustment needs to be made in order to print the results. Add the following code to a new macro file:

{% macro print_audit_output() %}
{%- set columns_to_compare=adapter.get_columns_in_relation(ref('fct_orders'))  -%}

{% set old_etl_relation_query %}
    select * from public.dim_product
{% endset %}

{% set new_etl_relation_query %}
    select * from {{ ref('fct_orders') }}
{% endset %}

{% if execute %}
    {% for column in columns_to_compare %}
        {{ log('Comparing column "' ~ column.name ~'"', info=True) }}
        {% set audit_query = audit_helper.compare_column_values(
                a_query=old_etl_relation_query,
                b_query=new_etl_relation_query,
                primary_key="order_id",
                column_to_compare=column.name
        ) %}

        {% set audit_results = run_query(audit_query) %}

        {% do log(audit_results.column_names, info=True) %}
            {% for row in audit_results.rows %}
                  {% do log(row.values(), info=True) %}
            {% endfor %}
    {% endfor %}
{% endif %}

{% endmacro %}

To run the macro, execute dbt run-operation print_audit_output() in the command bar.

Use Output For Custom Singular Test

If desired, you can use the audit helper macros to add a dbt test to your project to protect against unwanted changes to your data outputs.

For example, using the compare_all_columns macro, you could set up a test that will fail if any column values do not match.

Users can configure what exactly constitutes a value match or failure. If there is a test failure, results can be inspected in the warehouse. The primary key and the column name can be included in the test output that gets written to the warehouse. This enables the user to join test results to relevant tables in your dev or prod schema to investigate the error.

Note: this test should only be used on (and will only work on) models that have a primary key that is reliably unique and not_null. Generic dbt tests should be used to ensure the model being tested meets the requirements of unique and not_null.

To create a test for the stg_customers model, create a custom test in the tests subdirectory of your dbt project that looks like this:

{{ 
  audit_helper.compare_all_columns(
    a_relation=ref('stg_customers'), -- in a test, this ref will compile as your dev or PR schema.
    b_relation=api.Relation.create(database='dbt_db', schema='analytics_prod', identifier='stg_customers'), -- you can explicitly write a relation to select your production schema, or any other db/schema/table you'd like to use for comparison testing.
    exclude_columns=['updated_at'], 
    primary_key='id'
  ) 
}}
where not perfect_match

The where not perfect_match statement is an example of a filter you can apply to define whatconstitutes a test failure. The test will fail if any rows don't meet the requirement of a perfect match. Failures would include:

• If the primary key exists in both relations, but one model has a null value in a column.
• If a primary key is missing from one relation.
• If the primary key exists in both relations, but the value conflicts.

If you'd like the test to only fail when there are conflicting values, you could configure it like this:

{{ 
  audit_helper.compare_all_columns(
    a_relation=ref('stg_customers'), 
    b_relation=api.Relation.create(database='dbt_db', schema='analytics_prod', identifier='stg_customers'),
    primary_key='id'
  ) 
}}
where conflicting_values

If you want to create test results that include columns from the model itself for easier inspection, that can be written into the test:

{{ 
  audit_helper.compare_all_columns(
    a_relation=ref('stg_customers'),
    b_relation=api.Relation.create(database='dbt_db', schema='analytics_prod', identifier='stg_customers'), 
    exclude_columns=['updated_at'], 
    primary_key='id'
  ) 
}}
left join {{ ref('stg_customers') }} using(id)

This structure also allows for the test to group or filter by any attribute in the model or in the macro's output as part of the test, for example:

with base_test_cte as (
  {{ 
    audit_helper.compare_all_columns(
      a_relation=ref('stg_customers'),
      b_relation=api.Relation.create(database='dbt_db', schema='analytics_prod', identifier='stg_customers'), 
      exclude_columns=['updated_at'], 
      primary_key='id'
    ) 
  }}
  left join {{ ref('stg_customers') }} using(id)
  where conflicting_values
)
select
  status, -- assume there's a "status" column in stg_customers
  count(distinct case when conflicting_values then id end) as conflicting_values
from base_test_cte
group by 1

You can write a compare_all_columns test on individual table; and the test will be run as part of a full test suite run - dbt test --select stg_customers.

If you want to store results in the warehouse for further analysis, add the --store-failures flag.

Legacy Macros

compare_queries (source)

[!TIP] Consider compare_and_classify_query_results instead

This macro generates SQL that can be used to do a row-by-row comparison of two queries. This macro is particularly useful when you want to check that a refactored model (or a model that you are moving over from a legacy system) are identical. compare_queries provides flexibility when:

• You need to filter out records from one of the relations.
• You need to rename or recast some columns to get them to match up.
• You only want to compare a small number of columns, so it's easier to write the columns you want to compare, rather than the columns you want to exclude.

Output

By default, the generated query returns a summary of the count of rows that are unique to a, unique to b, and identical:

Setting the summarize argument to false lets you check which rows do not match between relations:

Arguments

• a_query and b_query: The queries you want to compare.
• primary_key (optional): The primary key of the model (or concatenated sql to create the primary key). Used to sort unmatched results for row-by-row validation.
• summarize (optional): Allows you to switch between a summary or detailed view of the compared data. Accepts true or false values. Defaults to true.
• limit (optional): Allows you to limit the number of rows returned when summarize = False. Defaults to None (no limit).

Usage

{% set old_query %}
  select
    id as order_id,
    amount,
    customer_id
  from old_database.old_schema.fct_orders
{% endset %}

{% set new_query %}
  select
    order_id,
    amount,
    customer_id
  from {{ ref('fct_orders') }}
{% endset %}

{{ audit_helper.compare_queries(
    a_query = old_query,
    b_query = new_query,
    primary_key = "order_id"
) }}

compare_relations (source)

[!TIP] Consider compare_and_classify_relation_rows instead

A wrapper to compare_queries, except it takes two Relations (instead of two queries).

Each relation must have the same columns with the same names, but they do not have to be in the same order. Use exclude_columns if some columns only exist in one relation.

Arguments

• a_relation and b_relation: The relations you want to compare.
• primary_key (optional): The primary key of the model (or concatenated sql to create the primary key). Used to sort unmatched results for row-by-row validation.
• exclude_columns (optional): Any columns you wish to exclude from the validation.
• summarize (optional): Allows you to switch between a summary or detailed view of the compared data. Accepts true or false values. Defaults to true.
• limit (optional): Allows you to limit the number of rows returned when summarize = False. Defaults to None (no limit).

Usage

{% set old_relation = adapter.get_relation(
      database = "old_database",
      schema = "old_schema",
      identifier = "fct_orders"
) -%}

{% set dbt_relation = ref('fct_orders') %}

{{ audit_helper.compare_relations(
    a_relation = old_relation,
    b_relation = dbt_relation,
    exclude_columns = ["loaded_at"],
    primary_key = "order_id"
) }}

Internal Macros

Macros prefixed with an _ (such as those in the utils/ subdirectory) are for private use. They are not documented or contracted and can change without notice.