This dbt package contains macros that can be (re)used across dbt projects.
Check dbt Hub for the latest installation instructions, or read the docs for more information on installing packages.
For compatibility details between versions of dbt-core and dbt-utils, see this spreadsheet.
- equal_rowcount
- equality
- expression_is_true
- recency
- at_least_one
- not_constant
- cardinality_equality
- unique_where
- not_null_where
- not_null_proportion
- relationships_where
- mutually_exclusive_ranges
- unique_combination_of_columns
- accepted_range
equal_rowcount (source)
This schema test asserts the 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)
This schema test asserts that this model has fewer rows than the referenced model.
Usage:
version: 2
models:
- name: model_name
tests:
- dbt_utils.fewer_rows_than:
compare_model: ref('other_table_name')equality (source)
This schema test 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_columnexpression_is_true (source)
This schema test asserts that a valid sql expression is true for all records. This is useful when checking integrity across columns, for example, that a total is equal to the sum of its parts, or that at least one column is true.
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 = 1recency (source)
This schema test asserts that there is data in the referenced model at least as recent as the defined interval prior to the current timestamp.
Usage:
version: 2
models:
- name: model_name
tests:
- dbt_utils.recency:
datepart: day
field: created_at
interval: 1at_least_one (source)
This schema test asserts if column has at least one value.
Usage:
version: 2
models:
- name: model_name
columns:
- name: col_name
tests:
- dbt_utils.at_least_onenot_constant (source)
This schema test asserts if column does not have same value in all rows.
Usage:
version: 2
models:
- name: model_name
columns:
- name: column_name
tests:
- dbt_utils.not_constantcardinality_equality (source)
This schema test asserts if 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)
This test validates that there are no duplicate values present in a field for a subset of rows by specifying a where clause.
Usage:
version: 2
models:
- name: my_model
columns:
- name: id
tests:
- dbt_utils.unique_where:
where: "_deleted = false"not_null_where (source)
This test validates that there are no null values present in a column for a subset of rows by specifying a where clause.
Usage:
version: 2
models:
- name: my_model
columns:
- name: id
tests:
- dbt_utils.not_null_where:
where: "_deleted = false"not_null_proportion (source)
This test validates 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: 0.95not_accepted_values (source)
This test validates 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)
This test validates the referential integrity between two relations (same as the core relationships schema test) 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'mutually_exclusive_ranges (source)
This test confirms that for a given lower_bound_column and upper_bound_column, the ranges of 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: trueArgs:
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=nonegaps(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: allowedUnderstanding 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 valuesdatepart(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)
This test confirms 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
uniquetest a coalesce 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
- productAn 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)
This test checks 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"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.
Arguments:
table(required): a Relation (areforsource) that contains the list of columns you wish to select fromcolumn(required): The name of the column you wish to find the column values oforder_by(optional, default='count(*) desc'): How the results should be ordered. The default is to order bycount(*) 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 returndefault(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'),
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_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_patternmacro (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:
-- Returns a dictionary of the users table where the state is California
{% set california_cities = dbt_utils.get_query_results_as_dict("select * from" ~ ref('cities') ~ "where state = 'CA' and city is not null ") %}
select
city,
{% for city in california_cities %}
sum(case when city = {{ city }} then 1 else 0 end) as users_in_{{ city }},
{% endfor %}
count(*) as total
from {{ ref('users') }}
group by 1
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)"
)
}}
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 locationlon1(required): longitude of first locationlat2(required): latitude of second locationlon3(required): longitude of second locationunit(optional, default='mi'): one ofmi(miles) orkm(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 1,2,3star (source)
This macro generates a 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, which will be appropriately concatenated to each field name in the output (prefix ~ 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') }}
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. An 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 theincludeandexcludearguments 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.
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, requiredvalues: List of row values to turn into columns, requiredalias: Whether to create column aliases, default is Trueagg: SQL aggregation function, default is sumcmp: SQL value comparison, default is =prefix: Column alias prefix, default is blanksuffix: Column alias postfix, default is blankthen_value: Value to use if comparison succeeds, default is 1else_value: Value to use if comparison fails, default is 0quote_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 varcharexclude: 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 fieldvalue_name: column name in the resulting table for value
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') }}
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).
Usage:
{{ 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
datepartargument is database-specific. - This macro currently only supports dateparts of
monthandquarter.
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_valueandmax_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:
0if the expression is less than min_value.num_buckets + 1if the expression is greater than or equal to max_value.
Usage:
{{ dbt_utils.width_bucket(expr, min_value, max_value, num_buckets) }}
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$h-money'], which we can't use as a column name due to the spaces and special characters. This macro does its best to strip those out in a sensible way: ['venmo_app', 'cah_money'].
{%- set payment_methods = dbt_utils.get_column_values(
table=ref('raw_payments'),
column='payment_method'
) -%}
select
order_id,
{%- for payment_method in payment_methods %}
sum(case when payment_method = '{{ payment_method }}' then amount end)
as {{ slugify(payment_method) }}_amount,
{% endfor %}
...select
order_id,
sum(case when payment_method = 'Venmo App' then amount end)
as venmo_app_amount,
sum(case when payment_method = 'ca$h money' then amount end)
as cah_money_amount,
...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 queriesstart_date: literal date or timestamp - generally choose a date that is earlier than the start of your datastop_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"Self contained procedures that allow us to affect data or run data-related operations
compare_objects (source)
This macro generates a result set comparing value diffs across two version of the same model.
- The
comparison_schemaargument is required and takes in the schema where your development model deployed. - The
object_nameargument is required and takes in the name of the model in question. - The
primary_keyargument is required and takes in the column name of the primary key (can also be a concatenation of multiple fields) - The
prod_databaseargument is required and defaulted to ANALYTICS_DW, takes in the database name where the original model exists - The
prod_schemaargument is required and defaulted to ANALYSIS, takes in the schema name where the original model exists - The
sql_whereargument is not required, but takes in a condition (or set of conditions) to filter down both the prod and comparison object datasets
Usage:
dbt run-operation compare_objects --args "{comparison_schema : dbt_kevin, object_name: claim_transaction_calendar_date, primary_key : 'claim_uuid || claim_coverage_type || calendar_date', sql_where: calendar_date < '2020-08-15'}"
We welcome contributions to this repo! To contribute a new feature or a fix, please open a Pull Request with 1) your changes, 2) updated documentation for the README.md file, and 3) a working integration test. See this page for more information.
Note: This is primarily relevant to:
- Users and maintainers of community-supported adapter plugins
- Users who wish to override a low-lying
dbt_utilsmacro with a custom implementation, and have that implementation used by otherdbt_utilsmacros
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 searchIf 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
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