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This dbt package contains macros that can be (re)used across dbt projects.

Installation Instructions

Check dbt Hub for the latest installation instructions, or read the docs for more information on installing packages.

Compatibility matrix

For compatibility details between versions of dbt-core and dbt-utils, see this spreadsheet.

Contents

Generic tests

Macros

Materializations:

Generic Tests

equal_rowcount (source)

Asserts that two relations have the same number of rows.

Usage:

version: 2

models:
  - name: model_name
    tests:
      - dbt_utils.equal_rowcount:
          compare_model: ref('other_table_name')

fewer_rows_than (source)

Asserts that the respective model has fewer rows than the model being compared.

Usage:

version: 2

models:
  - name: model_name
    tests:
      - dbt_utils.fewer_rows_than:
          compare_model: ref('other_table_name')

equality (source)

Asserts the equality of two relations. Optionally specify a subset of columns to compare.

Usage:

version: 2

models:
  - name: model_name
    tests:
      - dbt_utils.equality:
          compare_model: ref('other_table_name')
          compare_columns:
            - first_column
            - second_column

expression_is_true (source)

Asserts that a valid SQL expression is true for all records. This is useful when checking integrity across columns. Examples:

  • Verify an outcome based on the application of basic alegbraic operations between columns.
  • Verify the length of a column.
  • Verify the truth value of a column.

Usage:

version: 2

models:
  - name: model_name
    tests:
      - dbt_utils.expression_is_true:
          expression: "col_a + col_b = total"

The macro accepts an optional argument condition that allows for asserting the expression on a subset of all records.

Usage:

version: 2

models:
  - name: model_name
    tests:
      - dbt_utils.expression_is_true:
          expression: "col_a + col_b = total"
          condition: "created_at > '2018-12-31'"

This macro can also be used at the column level. When this is done, the expression is evaluated against the column.

version: 2
models:
  - name: model_name
    columns:
      - name: col_a
        tests:
          - dbt_utils.expression_is_true:
              expression: '>= 1'
      - name: col_b
        tests:
          - dbt_utils.expression_is_true:
              expression: '= 1'
              condition: col_a = 1

recency (source)

Asserts that a timestamp column in the reference model contains data that is at least as recent as the defined date interval.

Usage:

version: 2

models:
  - name: model_name
    tests:
      - dbt_utils.recency:
          datepart: day
          field: created_at
          interval: 1

at_least_one (source)

Asserts that a column has at least one value.

Usage:

version: 2

models:
  - name: model_name
    columns:
      - name: col_name
        tests:
          - dbt_utils.at_least_one

not_constant (source)

Asserts that a column does not have the same value in all rows.

Usage:

version: 2

models:
  - name: model_name
    columns:
      - name: column_name
        tests:
          - dbt_utils.not_constant

cardinality_equality (source)

Asserts that values in a given column have exactly the same cardinality as values from a different column in a different model.

Usage:

version: 2

models:
  - name: model_name
    columns:
      - name: from_column
        tests:
          - dbt_utils.cardinality_equality:
              field: other_column_name
              to: ref('other_model_name')

unique_where (source)

Asserts that there are no duplicate values present in a field for a subset of rows by specifying a where clause.

Warning: This test is no longer supported. Starting in dbt v0.20.0, the built-in unique test supports a where config. See the dbt docs for more details.

Usage:

version: 2

models:
  - name: my_model
    columns:
      - name: id
        tests:
          - dbt_utils.unique_where:
              where: "_deleted = false"

not_null_where (source)

Asserts that there are no null values present in a column for a subset of rows by specifying a where clause.

Warning: This test is no longer supported. Starting in dbt v0.20.0, the built-in not_null test supports a where config. See the dbt docs for more details.

Usage:

version: 2

models:
  - name: my_model
    columns:
      - name: id
        tests:
          - dbt_utils.not_null_where:
              where: "_deleted = false"

not_null_proportion (source)

Asserts that the proportion of non-null values present in a column is between a specified range [at_least, at_most] where at_most is an optional argument (default: 1.0).

Usage:

version: 2

models:
  - name: my_model
    columns:
      - name: id
        tests:
          - dbt_utils.not_null_proportion:
              at_least: 0.95

not_accepted_values (source)

Asserts that there are no rows that match the given values.

Usage:

version: 2

models:
  - name: my_model
    columns:
      - name: city
        tests:
          - dbt_utils.not_accepted_values:
              values: ['Barcelona', 'New York']

relationships_where (source)

Asserts the referential integrity between two relations (same as the core relationships assertions) with an added predicate to filter out some rows from the test. This is useful to exclude records such as test entities, rows created in the last X minutes/hours to account for temporary gaps due to ETL limitations, etc.

Usage:

version: 2

models:
  - name: model_name
    columns:
      - name: id
        tests:
          - dbt_utils.relationships_where:
              to: ref('other_model_name')
              field: client_id
              from_condition: id <> '4ca448b8-24bf-4b88-96c6-b1609499c38b'
              to_condition: created_date >= '2020-01-01'

mutually_exclusive_ranges (source)

Asserts that for a given lower_bound_column and upper_bound_column, the ranges between the lower and upper bounds do not overlap with the ranges of another row.

Usage:

version: 2

models:
  # test that age ranges do not overlap
  - name: age_brackets
    tests:
      - dbt_utils.mutually_exclusive_ranges:
          lower_bound_column: min_age
          upper_bound_column: max_age
          gaps: not_allowed

  # test that each customer can only have one subscription at a time
  - name: subscriptions
    tests:
      - dbt_utils.mutually_exclusive_ranges:
          lower_bound_column: started_at
          upper_bound_column: ended_at
          partition_by: customer_id
          gaps: required

  # test that each customer can have subscriptions that start and end on the same date
  - name: subscriptions
    tests:
      - dbt_utils.mutually_exclusive_ranges:
          lower_bound_column: started_at
          upper_bound_column: ended_at
          partition_by: customer_id
          zero_length_range_allowed: true

Args:

  • lower_bound_column (required): The name of the column that represents the lower value of the range. Must be not null.
  • upper_bound_column (required): The name of the column that represents the upper value of the range. Must be not null.
  • partition_by (optional): If a subset of records should be mutually exclusive (e.g. all periods for a single subscription_id are mutually exclusive), use this argument to indicate which column to partition by. default=none
  • gaps (optional): Whether there can be gaps are allowed between ranges. default='allowed', one_of=['not_allowed', 'allowed', 'required']
  • zero_length_range_allowed (optional): Whether ranges can start and end on the same date. default=False

Note: Both lower_bound_column and upper_bound_column should be not null. If this is not the case in your data source, consider passing a coalesce function to the lower_ and upper_bound_column arguments, like so:

version: 2

models:
  - name: subscriptions
    tests:
      - dbt_utils.mutually_exclusive_ranges:
          lower_bound_column: coalesce(started_at, '1900-01-01')
          upper_bound_column: coalesce(ended_at, '2099-12-31')
          partition_by: customer_id
          gaps: allowed
Additional `gaps` and `zero_length_range_allowed` examples **Understanding the `gaps` argument:**

Here are a number of examples for each allowed gaps argument.

  • gaps: not_allowed: The upper bound of one record must be the lower bound of the next record.
lower_bound upper_bound
0 1
1 2
2 3
  • gaps: allowed (default): There may be a gap between the upper bound of one record and the lower bound of the next record.
lower_bound upper_bound
0 1
2 3
3 4
  • gaps: required: There must be a gap between the upper bound of one record and the lower bound of the next record (common for date ranges).
lower_bound upper_bound
0 1
2 3
4 5

Understanding the zero_length_range_allowed argument: Here are a number of examples for each allowed zero_length_range_allowed argument.

  • zero_length_range_allowed: false: (default) The upper bound of each record must be greater than its lower bound.
lower_bound upper_bound
0 1
1 2
2 3
  • zero_length_range_allowed: true: The upper bound of each record can be greater than or equal to its lower bound.
lower_bound upper_bound
0 1
2 2
3 4

sequential_values (source)

This test confirms that a column contains sequential values. It can be used for both numeric values, and datetime values, as follows:

version: 2

seeds:
  - name: util_even_numbers
    columns:
      - name: i
        tests:
          - dbt_utils.sequential_values:
              interval: 2


  - name: util_hours
    columns:
      - name: date_hour
        tests:
          - dbt_utils.sequential_values:
              interval: 1
              datepart: 'hour'

Args:

  • interval (default=1): The gap between two sequential values
  • datepart (default=None): Used when the gaps are a unit of time. If omitted, the test will check for a numeric gap.

unique_combination_of_columns (source)

Asserts that the combination of columns is unique. For example, the combination of month and product is unique, however neither column is unique in isolation.

We generally recommend testing this uniqueness condition by either:

  • generating a surrogate_key for your model and testing the uniqueness of said key, OR
  • passing the unique test a concatenation of the columns (as discussed here).

However, these approaches can become non-perfomant on large data sets, in which case we recommend using this test instead.

Usage:

- name: revenue_by_product_by_month
  tests:
    - dbt_utils.unique_combination_of_columns:
        combination_of_columns:
          - month
          - product

An optional quote_columns argument (default=false) can also be used if a column name needs to be quoted.

- name: revenue_by_product_by_month
  tests:
    - dbt_utils.unique_combination_of_columns:
        combination_of_columns:
          - month
          - group
        quote_columns: true

accepted_range (source)

Asserts that a column's values fall inside an expected range. Any combination of min_value and max_value is allowed, and the range can be inclusive or exclusive. Provide a where argument to filter to specific records only.

In addition to comparisons to a scalar value, you can also compare to another column's values. Any data type that supports the > or < operators can be compared, so you could also run tests like checking that all order dates are in the past.

Usage:

version: 2

models:
  - name: model_name
    columns:
      - name: user_id
        tests:
          - dbt_utils.accepted_range:
              min_value: 0
              inclusive: false

      - name: account_created_at
        tests:
          - dbt_utils.accepted_range:
              max_value: "getdate()"
              #inclusive is true by default

      - name: num_returned_orders
        tests:
          - dbt_utils.accepted_range:
              min_value: 0
              max_value: "num_orders"

      - name: num_web_sessions
        tests:
          - dbt_utils.accepted_range:
              min_value: 0
              inclusive: false
              where: "num_orders > 0"

Macros

Introspective macros

These macros run a query and return the results of the query as objects. They are typically abstractions over the statement blocks in dbt.

get_column_values (source)

This macro returns the unique values for a column in a given relation as an array.

Args:

  • table (required): a Relation (a ref or source) that contains the list of columns you wish to select from
  • column (required): The name of the column you wish to find the column values of
  • where (optional, default=none): A where clause to filter the column values by.
  • order_by (optional, default='count(*) desc'): How the results should be ordered. The default is to order by count(*) desc, i.e. decreasing frequency. Setting this as 'my_column' will sort alphabetically, while 'min(created_at)' will sort by when thevalue was first observed.
  • max_records (optional, default=none): The maximum number of column values you want to return
  • default (optional, default=[]): The results this macro should return if the relation has not yet been created (and therefore has no column values).

Usage:

-- Returns a list of the payment_methods in the stg_payments model_
{% set payment_methods = dbt_utils.get_column_values(table=ref('stg_payments'), column='payment_method') %}

{% for payment_method in payment_methods %}
    ...
{% endfor %}

...
-- Returns the list sorted alphabetically
{% set payment_methods = dbt_utils.get_column_values(
        table=ref('stg_payments'),
        where="payment_method = 'bank_transfer'",
        column='payment_method',
        order_by='payment_method'
) %}
-- Returns the list sorted my most recently observed
{% set payment_methods = dbt_utils.get_column_values(
        table=ref('stg_payments'),
        column='payment_method',
        order_by='max(created_at) desc',
        max_records=50,
        default=['bank_transfer', 'coupon', 'credit_card']
%}
...

get_filtered_columns_in_relation (source)

This macro returns an iterable Jinja list of columns for a given relation, (i.e. not from a CTE)

  • optionally exclude columns
  • the input values are not case-sensitive (input uppercase or lowercase and it will work!)

Note: The native adapter.get_columns_in_relation macro allows you to pull column names in a non-filtered fashion, also bringing along with it other (potentially unwanted) information, such as dtype, char_size, numeric_precision, etc.

Args:

  • from (required): a Relation (a ref or source) that contains the list of columns you wish to select from
  • except (optional, default=[]): The name of the columns you wish to exclude. (case-insensitive)

Usage:

-- Returns a list of the columns from a relation, so you can then iterate in a for loop
{% set column_names = dbt_utils.get_filtered_columns_in_relation(from=ref('your_model'), except=["field_1", "field_2"]) %}
...
{% for column_name in column_names %}
    max({{ column_name }}) ... as max_'{{ column_name }}',
{% endfor %}
...

get_relations_by_pattern (source)

Returns a list of Relations that match a given schema- or table-name pattern.

This macro is particularly handy when paired with union_relations.

Usage:

-- Returns a list of relations that match schema_pattern%.table
{% set relations = dbt_utils.get_relations_by_pattern('schema_pattern%', 'table_pattern') %}

-- Returns a list of relations that match schema_pattern.table_pattern%
{% set relations = dbt_utils.get_relations_by_pattern('schema_pattern', 'table_pattern%') %}

-- Returns a list of relations as above, excluding any that end in `deprecated`
{% set relations = dbt_utils.get_relations_by_pattern('schema_pattern', 'table_pattern%', '%deprecated') %}

-- Example using the union_relations macro
{% set event_relations = dbt_utils.get_relations_by_pattern('venue%', 'clicks') %}
{{ dbt_utils.union_relations(relations = event_relations) }}

Args:

  • schema_pattern (required): The schema pattern to inspect for relations.
  • table_pattern (required): The name of the table/view (case insensitive).
  • exclude (optional): Exclude any relations that match this table pattern.
  • database (optional, default = target.database): The database to inspect for relations.

Examples: Generate drop statements for all Relations that match a naming pattern:

{% set relations_to_drop = dbt_utils.get_relations_by_pattern(
    schema_pattern='public',
    table_pattern='dbt\_%'
) %}

{% set sql_to_execute = [] %}

{{ log('Statements to run:', info=True) }}

{% for relation in relations_to_drop %}
    {% set drop_command -%}
    -- drop {{ relation.type }} {{ relation }} cascade;
    {%- endset %}
    {% do log(drop_command, info=True) %}
    {% do sql_to_execute.append(drop_command) %}
{% endfor %}

get_relations_by_prefix (source)

This macro will soon be deprecated in favor of the more flexible get_relations_by_pattern macro (above)

Returns a list of Relations that match a given prefix, with an optional exclusion pattern. It's particularly handy paired with union_relations.

Usage:

-- Returns a list of relations that match schema.prefix%
{% set relations = dbt_utils.get_relations_by_prefix('my_schema', 'my_prefix') %}

-- Returns a list of relations as above, excluding any that end in `deprecated`
{% set relations = dbt_utils.get_relations_by_prefix('my_schema', 'my_prefix', '%deprecated') %}

-- Example using the union_relations macro
{% set event_relations = dbt_utils.get_relations_by_prefix('events', 'event_') %}
{{ dbt_utils.union_relations(relations = event_relations) }}

Args:

  • schema (required): The schema to inspect for relations.
  • prefix (required): The prefix of the table/view (case insensitive)
  • exclude (optional): Exclude any relations that match this pattern.
  • database (optional, default = target.database): The database to inspect for relations.

get_query_results_as_dict (source)

This macro returns a dictionary from a sql query, so that you don't need to interact with the Agate library to operate on the result

Usage:

{% set sql_statement %}
    select city, state from {{ ref('users') }}
{% endset %}

{%- set places = dbt_utils.get_query_results_as_dict(sql_statement) -%}

select

    {% for city in places['CITY'] | unique -%}
      sum(case when city = '{{ city }}' then 1 else 0 end) as users_in_{{ dbt_utils.slugify(city) }},
    {% endfor %}

    {% for state in places['STATE'] | unique -%}
      sum(case when state = '{{ state }}' then 1 else 0 end) as users_in_{{ state }},
    {% endfor %}

    count(*) as total_total

from {{ ref('users') }}

SQL generators

These macros generate SQL (either a complete query, or a part of a query). They often implement patterns that should be easy in SQL, but for some reason are much harder than they need to be.

date_spine (source)

This macro returns the sql required to build a date spine. The spine will include the start_date (if it is aligned to the datepart), but it will not include the end_date.

Usage:

{{ dbt_utils.date_spine(
    datepart="day",
    start_date="cast('2019-01-01' as date)",
    end_date="cast('2020-01-01' as date)"
   )
}}

deduplicate (source)

This macro returns the sql required to remove duplicate rows from a model, source, or CTE.

Args:

  • relation (required): a Relation (a ref or source) or string which identifies the model to deduplicate.
  • partition_by (required): column names (or expressions) to use to identify a set/window of rows out of which to select one as the deduplicated row.
  • order_by (required): column names (or expressions) that determine the priority order of which row should be chosen if there are duplicates (comma-separated string). NB. if this order by clause results in ties then which row is returned may be nondeterministic across runs.

Usage:

{{ dbt_utils.deduplicate(
    relation=source('my_source', 'my_table'),
    partition_by='user_id, cast(timestamp as day)',
    order_by="timestamp desc",
   )
}}

{{ dbt_utils.deduplicate(
    relation=ref('my_model'),
    partition_by='user_id',
    order_by='effective_date desc, effective_sequence desc',
   )
}}

with my_cte as (
    select *
    from {{ source('my_source', 'my_table') }}
    where user_id = 1
)

{{ dbt_utils.deduplicate(
    relation='my_cte',
    partition_by='user_id, cast(timestamp as day)',
    order_by='timestamp desc',
   )
}}

haversine_distance (source)

This macro calculates the haversine distance between a pair of x/y coordinates.

Optionally takes a unit string argument ('km' or 'mi') which defaults to miles (imperial system).

Usage:

{{ dbt_utils.haversine_distance(48.864716, 2.349014, 52.379189, 4.899431) }}

{{ dbt_utils.haversine_distance(
    lat1=48.864716,
    lon1=2.349014,
    lat2=52.379189,
    lon2=4.899431,
    unit='km'
) }}

Args:

  • lat1 (required): latitude of first location
  • lon1 (required): longitude of first location
  • lat2 (required): latitude of second location
  • lon3 (required): longitude of second location
  • unit (optional, default='mi'): one of mi (miles) or km (kilometers)

group_by (source)

This macro build a group by statement for fields 1...N

Usage:

{{ dbt_utils.group_by(n=3) }}

Would compile to:

group by 1,2,3

star (source)

This macro generates a comma-separated list of all fields that exist in the from relation, excluding any fields listed in the except argument. The construction is identical to select * from {{ref('my_model')}}, replacing star (*) with the star macro. This macro also has an optional relation_alias argument that will prefix all generated fields with an alias (relation_alias.field_name). The macro also has optional prefix and suffix arguments. When one or both are provided, they will be concatenated onto each field's alias in the output (prefix ~ field_name ~ suffix). NB: This prevents the output from being used in any context other than a select statement.

Args:

  • from (required): a Relation (a ref or source) that contains the list of columns you wish to select from
  • except (optional, default=[]): The name of the columns you wish to exclude. (case-insensitive)
  • relation_alias (optional, default=''): will prefix all generated fields with an alias (relation_alias.field_name).
  • prefix (optional, default=''): will prefix the output field_name (field_name as prefix_field_name).
  • suffix (optional, default=''): will suffix the output field_name (field_name as field_name_suffix).

Usage:

select
  {{ dbt_utils.star(ref('my_model')) }}
from {{ ref('my_model') }}

select
{{ dbt_utils.star(from=ref('my_model'), except=["exclude_field_1", "exclude_field_2"]) }}
from {{ ref('my_model') }}

select
{{ dbt_utils.star(from=ref('my_model'), except=["exclude_field_1", "exclude_field_2"], prefix="max_") }}
from {{ ref('my_model') }}

union_relations (source)

This macro unions together an array of Relations, even when columns have differing orders in each Relation, and/or some columns are missing from some relations. Any columns exclusive to a subset of these relations will be filled with null where not present. A new column (_dbt_source_relation) is also added to indicate the source for each record.

Usage:

{{ dbt_utils.union_relations(
    relations=[ref('my_model'), source('my_source', 'my_table')],
    exclude=["_loaded_at"]
) }}

Args:

  • relations (required): An array of Relations.
  • exclude (optional): A list of column names that should be excluded from the final query.
  • include (optional): A list of column names that should be included in the final query. Note the include and exclude arguments are mutually exclusive.
  • column_override (optional): A dictionary of explicit column type overrides, e.g. {"some_field": "varchar(100)"}.``
  • source_column_name (optional, default="_dbt_source_relation"): The name of the column that records the source of this row.
  • where (optional): Filter conditions to include in the where clause.

generate_series (source)

This macro implements a cross-database mechanism to generate an arbitrarily long list of numbers. Specify the maximum number you'd like in your list and it will create a 1-indexed SQL result set.

Usage:

{{ dbt_utils.generate_series(upper_bound=1000) }}

surrogate_key (source)

Implements a cross-database way to generate a hashed surrogate key using the fields specified.

Usage:

{{ dbt_utils.surrogate_key(['field_a', 'field_b'[,...]]) }}

safe_add (source)

Implements a cross-database way to sum nullable fields using the fields specified.

Usage:

{{ dbt_utils.safe_add('field_a', 'field_b'[,...]) }}

pivot (source)

This macro pivots values from rows to columns.

Usage:

{{ dbt_utils.pivot(<column>, <list of values>) }}

Example:

Input: orders

| size | color |
|------|-------|
| S    | red   |
| S    | blue  |
| S    | red   |
| M    | red   |

select
  size,
  {{ dbt_utils.pivot(
      'color',
      dbt_utils.get_column_values(ref('orders'), 'color')
  ) }}
from {{ ref('orders') }}
group by size

Output:

| size | red | blue |
|------|-----|------|
| S    | 2   | 1    |
| M    | 1   | 0    |

Args:

  • column: Column name, required
  • values: List of row values to turn into columns, required
  • alias: Whether to create column aliases, default is True
  • agg: SQL aggregation function, default is sum
  • cmp: SQL value comparison, default is =
  • prefix: Column alias prefix, default is blank
  • suffix: Column alias postfix, default is blank
  • then_value: Value to use if comparison succeeds, default is 1
  • else_value: Value to use if comparison fails, default is 0
  • quote_identifiers: Whether to surround column aliases with double quotes, default is true

unpivot (source)

This macro "un-pivots" a table from wide format to long format. Functionality is similar to pandas melt function. Boolean values are replaced with the strings 'true'|'false'

Usage:

{{ dbt_utils.unpivot(
  relation=ref('table_name'),
  cast_to='datatype',
  exclude=[<list of columns to exclude from unpivot>],
  remove=[<list of columns to remove>],
  field_name=<column name for field>,
  value_name=<column name for value>
) }}

Usage:

Input: orders

| date       | size | color | status     |
|------------|------|-------|------------|
| 2017-01-01 | S    | red   | complete   |
| 2017-03-01 | S    | red   | processing |

{{ dbt_utils.unpivot(ref('orders'), cast_to='varchar', exclude=['date','status']) }}

Output:

| date       | status     | field_name | value |
|------------|------------|------------|-------|
| 2017-01-01 | complete   | size       | S     |
| 2017-01-01 | complete   | color      | red   |
| 2017-03-01 | processing | size       | S     |
| 2017-03-01 | processing | color      | red   |

Args:

  • relation: The Relation to unpivot.
  • cast_to: The data type to cast the unpivoted values to, default is varchar
  • exclude: A list of columns to exclude from the unpivot operation but keep in the resulting table.
  • remove: A list of columns to remove from the resulting table.
  • field_name: column name in the resulting table for field
  • value_name: column name in the resulting table for value

Web macros

get_url_parameter (source)

This macro extracts a url parameter from a column containing a url.

Usage:

{{ dbt_utils.get_url_parameter(field='page_url', url_parameter='utm_source') }}

get_url_host (source)

This macro extracts a hostname from a column containing a url.

Usage:

{{ dbt_utils.get_url_host(field='page_url') }}

get_url_path (source)

This macro extracts a page path from a column containing a url.

Usage:

{{ dbt_utils.get_url_path(field='page_url') }}

Cross-database macros

These macros make it easier for package authors (especially those writing modeling packages) to implement cross-database compatibility. In general, you should not use these macros in your own dbt project (unless it is a package)

current_timestamp (source)

This macro returns the current timestamp.

Usage:

{{ dbt_utils.current_timestamp() }}

dateadd (source)

This macro adds a time/day interval to the supplied date/timestamp. Note: The datepart argument is database-specific.

Usage:

{{ dbt_utils.dateadd(datepart='day', interval=1, from_date_or_timestamp="'2017-01-01'") }}

datediff (source)

This macro calculates the difference between two dates.

Usage:

{{ dbt_utils.datediff("'2018-01-01'", "'2018-01-20'", 'day') }}

split_part (source)

This macro splits a string of text using the supplied delimiter and returns the supplied part number (1-indexed).

Args:

  • string_text (required): Text to be split into parts.
  • delimiter_text (required): Text representing the delimiter to split by.
  • part_number (required): Requested part of the split (1-based). If the value is negative, the parts are counted backward from the end of the string.

Usage: When referencing a column, use one pair of quotes. When referencing a string, use single quotes enclosed in double quotes.

{{ dbt_utils.split_part(string_text='column_to_split', delimiter_text='delimiter_column', part_number=1) }}
{{ dbt_utils.split_part(string_text="'1|2|3'", delimiter_text="'|'", part_number=1) }}

date_trunc (source)

Truncates a date or timestamp to the specified datepart. Note: The datepart argument is database-specific.

Usage:

{{ dbt_utils.date_trunc(datepart, date) }}

last_day (source)

Gets the last day for a given date and datepart. Notes:

  • The datepart argument is database-specific.
  • This macro currently only supports dateparts of month and quarter.

Usage:

{{ dbt_utils.last_day(date, datepart) }}

width_bucket (source)

This macro is modeled after the width_bucket function natively available in Snowflake.

From the original Snowflake documentation:

Constructs equi-width histograms, in which the histogram range is divided into intervals of identical size, and returns the bucket number into which the value of an expression falls, after it has been evaluated. The function returns an integer value or null (if any input is null). Notes:

Args:

  • expr: The expression for which the histogram is created. This expression must evaluate to a numeric value or to a value that can be implicitly converted to a numeric value.

  • min_value and max_value: The low and high end points of the acceptable range for the expression. The end points must also evaluate to numeric values and not be equal.

  • num_buckets: The desired number of buckets; must be a positive integer value. A value from the expression is assigned to each bucket, and the function then returns the corresponding bucket number.

When an expression falls outside the range, the function returns:

  • 0 if the expression is less than min_value.
  • num_buckets + 1 if the expression is greater than or equal to max_value.

Usage:

{{ dbt_utils.width_bucket(expr, min_value, max_value, num_buckets) }}

listagg (source)

This macro returns the concatenated input values from a group of rows separated by a specified deliminator.

Args:

  • measure (required): The expression (typically a column name) that determines the values to be concatenated. To only include distinct values add keyword DISTINCT to beginning of expression (example: 'DISTINCT column_to_agg').
  • delimiter_text (required): Text representing the delimiter to separate concatenated values by.
  • order_by_clause (optional): An expression (typically a column name) that determines the order of the concatenated values.
  • limit_num (optional): Specifies the maximum number of values to be concatenated.

Note: If there are instances of delimiter_text within your measure, you cannot include a limit_num.

Usage:

{{ dbt_utils.listagg(measure='column_to_agg', delimiter_text="','", order_by_clause="order by order_by_column", limit_num=10) }}

array_construct (source)

This macro returns an array constructed from a set of inputs.

Args:

  • inputs (optional): The list of array contents. If not provided, this macro will create an empty array. All inputs must be the same data type in order to match Postgres functionality and not null to match Bigquery functionality.
  • data_type (optional): Specifies the data type of the constructed array. This is only relevant when creating an empty array (will otherwise use the data type of the inputs). If inputs are data_type are both not provided, this macro will create an empty array of type integer.

Usage:

{{ dbt_utils.array_construct(['column_1', 'column_2', 'column_3']) }}
{{ dbt_utils.array_construct([],'integer') }}

array_append (source)

This macro appends an element to the end of an array and returns the appended array.

Args:

  • array (required): The array to append to.
  • new_element (required): The element to be appended. This element must match the data type of the existing elements in the array in order to match Postgres functionality and not null to match Bigquery functionality.

Usage:

{{ dbt_utils.array_append('array_column', 'element_column') }}

array_concat (source)

This macro returns the concatenation of two arrays.

Args:

  • array_1 (required): The array to append to.
  • array_2 (required): The array to be appended to array_1. This array must match the data type of array_1 in order to match Postgres functionality.

Usage:

{{ dbt_utils.array_concat('array_column_1', 'array_column_2') }}

cast_array_to_string (source)

This macro converts an array to a single string value and returns the resulting string.

Args:

  • array (required): The array to convert to a string.

Usage:

{{ dbt_utils.cast_array_to_string('array_column') }}

Jinja Helpers

pretty_time (source)

This macro returns a string of the current timestamp, optionally taking a datestring format.

{#- This will return a string like '14:50:34' -#}
{{ dbt_utils.pretty_time() }}

{#- This will return a string like '2019-05-02 14:50:34' -#}
{{ dbt_utils.pretty_time(format='%Y-%m-%d %H:%M:%S') }}

pretty_log_format (source)

This macro formats the input in a way that will print nicely to the command line when you log it.

{#- This will return a string like:
"11:07:31 + my pretty message"
-#}

{{ dbt_utils.pretty_log_format("my pretty message") }}

log_info (source)

This macro logs a formatted message (with a timestamp) to the command line.

{{ dbt_utils.log_info("my pretty message") }}
11:07:28 | 1 of 1 START table model analytics.fct_orders........................ [RUN]
11:07:31 + my pretty message

slugify (source)

This macro is useful for transforming Jinja strings into "slugs", and can be useful when using a Jinja object as a column name, especially when that Jinja object is not hardcoded.

For this example, let's pretend that we have payment methods in our payments table like ['venmo App', 'ca$h-money'], which we can't use as a column name due to the spaces and special characters. This macro does its best to strip those out in a sensible way: ['venmo_app', 'cah_money'].

{%- set payment_methods = dbt_utils.get_column_values(
    table=ref('raw_payments'),
    column='payment_method'
) -%}

select
order_id,
{%- for payment_method in payment_methods %}
sum(case when payment_method = '{{ payment_method }}' then amount end)
  as {{ dbt_utils.slugify(payment_method) }}_amount,

{% endfor %}
...
select
order_id,

sum(case when payment_method = 'Venmo App' then amount end)
  as venmo_app_amount,

sum(case when payment_method = 'ca$h money' then amount end)
  as cah_money_amount,
...

Materializations

insert_by_period (source)

insert_by_period allows dbt to insert records into a table one period (i.e. day, week) at a time.

This materialization is appropriate for event data that can be processed in discrete periods. It is similar in concept to the built-in incremental materialization, but has the added benefit of building the model in chunks even during a full-refresh so is particularly useful for models where the initial run can be problematic.

Should a run of a model using this materialization be interrupted, a subsequent run will continue building the target table from where it was interrupted (granted the --full-refresh flag is omitted).

Progress is logged in the command line for easy monitoring.

Usage:

{{
  config(
    materialized = "insert_by_period",
    period = "day",
    timestamp_field = "created_at",
    start_date = "2018-01-01",
    stop_date = "2018-06-01")
}}

with events as (

  select *
  from {{ ref('events') }}
  where __PERIOD_FILTER__ -- This will be replaced with a filter in the materialization code

)

....complex aggregates here....

Configuration values:

  • period: period to break the model into, must be a valid datepart (default='Week')
  • timestamp_field: the column name of the timestamp field that will be used to break the model into smaller queries
  • start_date: literal date or timestamp - generally choose a date that is earlier than the start of your data
  • stop_date: literal date or timestamp (default=current_timestamp)

Caveats:

  • This materialization is compatible with dbt 0.10.1.
  • This materialization has been written for Redshift.
  • This materialization can only be used for a model where records are not expected to change after they are created.
  • Any model post-hooks that use {{ this }} will fail using this materialization. For example:
models:
    project-name:
        post-hook: "grant select on {{ this }} to db_reader"

A useful workaround is to change the above post-hook to:

        post-hook: "grant select on {{ this.schema }}.{{ this.name }} to db_reader"

Reporting bugs and contributing code

  • Want to report a bug or request a feature? Let us know in the #package-ecosystem channel on Slack, or open an issue
  • Want to help us build dbt-utils? Check out the Contributing Guide
    • TL;DR Open a Pull Request with 1) your changes, 2) updated documentation for the README.md file, and 3) a working integration test.

Dispatch macros

Note: This is primarily relevant to:

  • Users and maintainers of community-supported adapter plugins
  • Users who wish to override a low-lying dbt_utils macro with a custom implementation, and have that implementation used by other dbt_utils macros

If you use Postgres, Redshift, Snowflake, or Bigquery, this likely does not apply to you.

dbt v0.18.0 introduced adapter.dispatch(), a reliable way to define different implementations of the same macro across different databases.

dbt v0.20.0 introduced a new project-level dispatch config that enables an "override" setting for all dispatched macros. If you set this config in your project, when dbt searches for implementations of a macro in the dbt_utils namespace, it will search through your list of packages instead of just looking in the dbt_utils package.

Set the config in dbt_project.yml:

dispatch:
  - macro_namespace: dbt_utils
    search_order:
      - first_package_to_search    # likely the name of your root project
      - second_package_to_search   # could be a "shim" package, such as spark_utils
      - dbt_utils                  # always include dbt_utils as the last place to search

If overriding a dispatched macro with a custom implementation in your own project's macros/ directory, you must name your custom macro with a prefix: either default__ (note the two underscores), or the name of your adapter followed by two underscores. For example, if you're running on Postgres and wish to override the behavior of dbt_utils.datediff (such that dbt_utils.date_spine will use your version instead), you can do this by defining a macro called either default__datediff or postgres__datediff.

Let's say we have the config defined above, and we're running on Spark. When dbt goes to dispatch dbt_utils.datediff, it will search for macros the following in order:

first_package_to_search.spark__datediff
first_package_to_search.default__datediff
second_package_to_search.spark__datediff
second_package_to_search.default__datediff
dbt_utils.spark__datediff
dbt_utils.default__datediff

Getting started with dbt

Code of Conduct

Everyone interacting in the dbt project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the PyPA Code of Conduct.