BigQuery configurations

Use project and dataset in configurations

• schema is interchangeable with the BigQuery concept dataset
• database is interchangeable with the BigQuery concept of project

For our reference documentation, you can declare project in place of database. This will allow you to read and write from multiple BigQuery projects. Same for dataset.

Using table partitioning and clustering

Partition clause

BigQuery supports the use of a partition by clause to easily partition a table by a column or expression. This option can help decrease latency and cost when querying large tables. Note that partition pruning only works when partitions are filtered using literal values (so selecting partitions using a subquery won't improve performance).

The partition_by config can be supplied as a dictionary with the following format:

{
  "field": "<field name>",
  "data_type": "<timestamp | date | datetime | int64>",
  "granularity": "<hour | day | month | year>"

  # Only required if data_type is "int64"
  "range": {
    "start": <int>,
    "end": <int>,
    "interval": <int>
  }
}

Partitioning by a date or timestamp

When using a datetime or timestamp column to partition data, you can create partitions with a granularity of hour, day, month, or year. A date column supports granularity of day, month and year. Daily partitioning is the default for all column types.

If the data_type is specified as a date and the granularity is day, dbt will supply the field as-is when configuring table partitioning.

Source code

bigquery_table.sql

{{ config(
    materialized='table',
    partition_by={
      "field": "created_at",
      "data_type": "timestamp",
      "granularity": "day"
    }
)}}

select
  user_id,
  event_name,
  created_at

from {{ ref('events') }}

Compiled code

bigquery_table.sql

create table `projectname`.`analytics`.`bigquery_table`
partition by timestamp_trunc(created_at, day)
as (

  select
    user_id,
    event_name,
    created_at

  from `analytics`.`events`

)

Partitioning by an "ingestion" date or timestamp

BigQuery supports an older mechanism of partitioning based on the time when each row was ingested. While we recommend using the newer and more ergonomic approach to partitioning whenever possible, for very large datasets, there can be some performance improvements to using this older, more mechanistic approach. Read more about the insert_overwrite incremental strategy below.

dbt will always instruct BigQuery to partition your table by the values of the column specified in partition_by.field. By configuring your model with partition_by.time_ingestion_partitioning set to True, dbt will use that column as the input to a _PARTITIONTIME pseudocolumn. Unlike with newer column-based partitioning, you must ensure that the values of your partitioning column match exactly the time-based granularity of your partitions.

Source code

bigquery_table.sql

{{ config(
    materialized="incremental",
    partition_by={
      "field": "created_date",
      "data_type": "timestamp",
      "granularity": "day",
      "time_ingestion_partitioning": true
    }
) }}

select
  user_id,
  event_name,
  created_at,
  -- values of this column must match the data type + granularity defined above
  timestamp_trunc(created_at, day) as created_date

from {{ ref('events') }}

Compiled code

bigquery_table.sql

create table `projectname`.`analytics`.`bigquery_table` (`user_id` INT64, `event_name` STRING, `created_at` TIMESTAMP)
partition by timestamp_trunc(_PARTITIONTIME, day);

insert into `projectname`.`analytics`.`bigquery_table` (_partitiontime, `user_id`, `event_name`, `created_at`)
select created_date as _partitiontime, * EXCEPT(created_date) from (
    select
      user_id,
      event_name,
      created_at,
      -- values of this column must match granularity defined above
      timestamp_trunc(created_at, day) as created_date

    from `projectname`.`analytics`.`events`
);

Partitioning with integer buckets

If the data_type is specified as int64, then a range key must also be provided in the partition_by dict. dbt will use the values provided in the range dict to generate the partitioning clause for the table.

Source code

bigquery_table.sql

{{ config(
    materialized='table',
    partition_by={
      "field": "user_id",
      "data_type": "int64",
      "range": {
        "start": 0,
        "end": 100,
        "interval": 10
      }
    }
)}}

select
  user_id,
  event_name,
  created_at

from {{ ref('events') }}

Compiled code

bigquery_table.sql

create table analytics.bigquery_table
partition by range_bucket(
  customer_id,
  generate_array(0, 100, 10)
)
as (

  select
    user_id,
    event_name,
    created_at

  from analytics.events

)

Additional partition configs

If your model has partition_by configured, you may optionally specify two additional configurations:

• require_partition_filter (boolean): If set to true, anyone querying this model must specify a partition filter, otherwise their query will fail. This is recommended for very large tables with obvious partitioning schemes, such as event streams grouped by day. Note that this will affect other dbt models or tests that try to select from this model, too.

• partition_expiration_days (integer): If set for date- or timestamp-type partitions, the partition will expire that many days after the date it represents. E.g. A partition representing 2021-01-01, set to expire after 7 days, will no longer be queryable as of 2021-01-08, its storage costs zeroed out, and its contents will eventually be deleted. Note that table expiration will take precedence if specified.

bigquery_table.sql

{{ config(
    materialized = 'table',
    partition_by = {
      "field": "created_at",
      "data_type": "timestamp",
      "granularity": "day"
    },
    require_partition_filter = true,
    partition_expiration_days = 7
)}}

Clustering clause

BigQuery tables can be clustered to colocate related data.

Clustering on a single column:

bigquery_table.sql

{{
  config(
    materialized = "table",
    cluster_by = "order_id",
  )
}}

select * from ...

Clustering on multiple columns:

bigquery_table.sql

{{
  config(
    materialized = "table",
    cluster_by = ["customer_id", "order_id"],
  )
}}

select * from ...

Managing KMS encryption

Customer managed encryption keys can be configured for BigQuery tables using the kms_key_name model configuration.

Using KMS encryption

To specify the KMS key name for a model (or a group of models), use the kms_key_name model configuration. The following example sets the kms_key_name for all of the models in the encrypted/ directory of your dbt project.

dbt_project.yml

name: my_project
version: 1.0.0

...

models:
  my_project:
    encrypted:
      +kms_key_name: 'projects/PROJECT_ID/locations/global/keyRings/test/cryptoKeys/quickstart'

Labels and tags

Specifying labels

dbt supports the specification of BigQuery labels for the tables and views that it creates. These labels can be specified using the labels model config.

The labels config can be provided in a model config, or in the dbt_project.yml file, as shown below.

BigQuery key-value pair entries for labels larger than 63 characters are truncated.

Configuring labels in a model file

model.sql

{{
  config(
    materialized = "table",
    labels = {'contains_pii': 'yes', 'contains_pie': 'no'}
  )
}}

select * from {{ ref('another_model') }}

Configuring labels in dbt_project.yml

dbt_project.yml

models:
  my_project:
    snowplow:
      +labels:
        domain: clickstream
    finance:
      +labels:
        domain: finance

Viewing labels in the BigQuery console

Specifying tags

BigQuery table and view tags can be created by supplying an empty string for the label value.

model.sql

{{
  config(
    materialized = "table",
    labels = {'contains_pii': ''}
  )
}}

select * from {{ ref('another_model') }}

You can create a new label with no value or remove a value from an existing label key.

A label with a key that has an empty value can also be referred to as a tag in BigQuery. However, this should not be confused with a tag resource, which conditionally applies IAM policies to BigQuery tables and datasets. Find out more in labels and tags.

Currently, it's not possible to apply IAM tags in BigQuery, however, you can weigh in by upvoting GitHub issue 1134.

Policy tags

BigQuery enables column-level security by setting policy tags on specific columns.

dbt enables this feature as a column resource property, policy_tags (not a node config).

models/<filename>.yml

version: 2

models:
- name: policy_tag_table
  columns:
    - name: field
      policy_tags:
        - 'projects/<gcp-project>/locations/<location>/taxonomies/<taxonomy>/policyTags/<tag>'

Please note that in order for policy tags to take effect, column-level persist_docs must be enabled for the model, seed, or snapshot. Consider using variables to manage taxonomies and make sure to add the required security roles to your BigQuery service account key.

Merge behavior (incremental models)

The incremental_strategy config controls how dbt builds incremental models. dbt uses a merge statement on BigQuery to refresh incremental tables.

The incremental_strategy config can be set to one of the following values:

• merge (default)
• insert_overwrite
• microbatch

Performance and cost

The operations performed by dbt while building a BigQuery incremental model can be made cheaper and faster by using a clustering clause in your model configuration. See this guide for more information on performance tuning for BigQuery incremental models.

Note: These performance and cost benefits are applicable to incremental models built with either the merge or the insert_overwrite incremental strategy.

The merge strategy

The merge incremental strategy will generate a merge statement that looks something like:

merge into {{ destination_table }} DEST
using ({{ model_sql }}) SRC
on SRC.{{ unique_key }} = DEST.{{ unique_key }}

when matched then update ...
when not matched then insert ...

The 'merge' approach automatically updates new data in the destination incremental table but requires scanning all source tables referenced in the model SQL, as well as destination tables. This can be slow and expensive for large data volumes. Partitioning and clustering techniques mentioned earlier can help mitigate these issues.

Note: The unique_key configuration is required when the merge incremental strategy is selected.

The insert_overwrite strategy

The insert_overwrite strategy generates a merge statement that replaces entire partitions in the destination table. Note: this configuration requires that the model is configured with a Partition clause. The merge statement that dbt generates when the insert_overwrite strategy is selected looks something like:

/*
  Create a temporary table from the model SQL
*/
create temporary table {{ model_name }}__dbt_tmp as (
  {{ model_sql }}
);

/*
  If applicable, determine the partitions to overwrite by
  querying the temp table.
*/

declare dbt_partitions_for_replacement array<date>;
set (dbt_partitions_for_replacement) = (
    select as struct
        array_agg(distinct date(max_tstamp))
    from `my_project`.`my_dataset`.{{ model_name }}__dbt_tmp
);

/*
  Overwrite partitions in the destination table which match
  the partitions in the temporary table
*/
merge into {{ destination_table }} DEST
using {{ model_name }}__dbt_tmp SRC
on FALSE

when not matched by source and {{ partition_column }} in unnest(dbt_partitions_for_replacement)
then delete

when not matched then insert ...

For a complete writeup on the mechanics of this approach, see this explainer post.

Determining partitions to overwrite

dbt is able to determine the partitions to overwrite dynamically from the values present in the temporary table, or statically using a user-supplied configuration.

The "dynamic" approach is simplest (and the default), but the "static" approach will reduce costs by eliminating multiple queries in the model build script.

Static partitions

To supply a static list of partitions to overwrite, use the partitions configuration.

models/session.sql

{% set partitions_to_replace = [
  'timestamp(current_date)',
  'timestamp(date_sub(current_date, interval 1 day))'
] %}

{{
  config(
    materialized = 'incremental',
    incremental_strategy = 'insert_overwrite',
    partition_by = {'field': 'session_start', 'data_type': 'timestamp'},
    partitions = partitions_to_replace
  )
}}

with events as (

    select * from {{ref('events')}}

    {% if is_incremental() %}
        -- recalculate yesterday + today
        where timestamp_trunc(event_timestamp, day) in ({{ partitions_to_replace | join(',') }})
    {% endif %}

),

... rest of model ...

This example model serves to replace the data in the destination table for both today and yesterday every day that it is run. It is the fastest and cheapest way to incrementally update a table using dbt. If we wanted this to run more dynamically— let’s say, always for the past 3 days—we could leverage dbt’s baked-in datetime macros and write a few of our own.

Think of this as "full control" mode. You must ensure that expressions or literal values in the the partitions config have proper quoting when templated, and that they match the partition_by.data_type (timestamp, datetime, date, or int64). Otherwise, the filter in the incremental merge statement will raise an error.

Dynamic partitions

If no partitions configuration is provided, dbt will instead:

1. Create a temporary table for your model SQL
2. Query the temporary table to find the distinct partitions to be overwritten
3. Query the destination table to find the max partition in the database

When building your model SQL, you can take advantage of the introspection performed by dbt to filter for only new data. The max partition in the destination table will be available using the _dbt_max_partition BigQuery scripting variable. Note: this is a BigQuery SQL variable, not a dbt Jinja variable, so no jinja brackets are required to access this variable.

Example model SQL:

{{
  config(
    materialized = 'incremental',
    partition_by = {'field': 'session_start', 'data_type': 'timestamp'},
    incremental_strategy = 'insert_overwrite'
  )
}}

with events as (

  select * from {{ref('events')}}

  {% if is_incremental() %}

    -- recalculate latest day's data + previous
    -- NOTE: The _dbt_max_partition variable is used to introspect the destination table
    where date(event_timestamp) >= date_sub(date(_dbt_max_partition), interval 1 day)

{% endif %}

),

... rest of model ...

Copying partitions

If you are replacing entire partitions in your incremental runs, you can opt to do so with the copy table API and partition decorators rather than a merge statement. While this mechanism doesn't offer the same visibility and ease of debugging as the SQL merge statement, it can yield significant savings in time and cost for large datasets because the copy table API does not incur any costs for inserting the data - it's equivalent to the bq cp gcloud command line interface (CLI) command.

You can enable this by switching on copy_partitions: True in the partition_by configuration. This approach works only in combination with "dynamic" partition replacement.

bigquery_table.sql

{{ config(
    materialized="incremental",
    incremental_strategy="insert_overwrite",
    partition_by={
      "field": "created_date",
      "data_type": "timestamp",
      "granularity": "day",
      "time_ingestion_partitioning": true,
      "copy_partitions": true
    }
) }}

select
  user_id,
  event_name,
  created_at,
  -- values of this column must match the data type + granularity defined above
  timestamp_trunc(created_at, day) as created_date

from {{ ref('events') }}

logs/dbt.log

...
[0m16:03:13.017641 [debug] [Thread-3 (]: BigQuery adapter: Copying table(s) "/projects/projectname/datasets/analytics/tables/bigquery_table__dbt_tmp$20230112" to "/projects/projectname/datasets/analytics/tables/bigquery_table$20230112" with disposition: "WRITE_TRUNCATE"
...

Controlling table expiration

By default, dbt-created tables never expire. You can configure certain model(s) to expire after a set number of hours by setting hours_to_expiration.

Note

The hours_to_expiration only applies to initial creation of the underlying table. It doesn't reset for incremental models when they do another run.

dbt_project.yml

models:
  <resource-path>:
    +hours_to_expiration: 6

models/<modelname>.sql

{{ config(
    hours_to_expiration = 6
) }}

select ...

Authorized views

If the grant_access_to config is specified for a model materialized as a view, dbt will grant the view model access to select from the list of datasets provided. See BQ docs on authorized views for more details.

Note

The grants config and the grant_access_to config are distinct.

• grant_access_to: Enables you to set up authorized views. When configured, dbt provides an authorized view access to show partial information from other datasets, without providing end users with full access to those underlying datasets. For more information, see "BigQuery configurations: Authorized views"
• grants: Provides specific permissions to users, groups, or service accounts for managing access to datasets you're producing with dbt. For more information, see "Resource configs: grants"

You can use the two features together: "authorize" a view model with the grants_access_to configuration, and then add grants to that view model to share its query results (and only its query results) with other users, groups, or service accounts.

dbt_project.yml

models:
  <resource-path>:
    +grant_access_to:
      - project: project_1
        dataset: dataset_1
      - project: project_2
        dataset: dataset_2

models/<modelname>.sql

{{ config(
    grant_access_to=[
      {'project': 'project_1', 'dataset': 'dataset_1'},
      {'project': 'project_2', 'dataset': 'dataset_2'}
    ]
) }}

Views with this configuration will be able to select from objects in project_1.dataset_1 and project_2.dataset_2, even when they are located elsewhere and queried by users who do not otherwise have access to project_1.dataset_1 and project_2.dataset_2.

Materialized views

The BigQuery adapter supports materialized views with the following configuration parameters:

Parameter	Type	Required	Default	Change Monitoring Support
on_configuration_change	<string>	no	apply	n/a
cluster_by	[<string>]	no	none	drop/create
partition_by	{<dictionary>}	no	none	drop/create
enable_refresh	<boolean>	no	true	alter
refresh_interval_minutes	<float>	no	30	alter
max_staleness (in Preview)	<interval>	no	none	alter
description	<string>	no	none	alter
labels	{<string>: <string>}	no	none	alter
hours_to_expiration	<integer>	no	none	alter
kms_key_name	<string>	no	none	alter

Project file

dbt_project.yml

models:
  <resource-path>:
    +materialized: materialized_view
    +on_configuration_change: apply | continue | fail
    +cluster_by: <field-name> | [<field-name>]
    +partition_by:
      - field: <field-name>
      - data_type: timestamp | date | datetime | int64
        # only if `data_type` is not 'int64'
      - granularity: hour | day | month | year
        # only if `data_type` is 'int64'
      - range:
        - start: <integer>
        - end: <integer>
        - interval: <integer>
    +enable_refresh: true | false
    +refresh_interval_minutes: <float>
    +max_staleness: <interval>
    +description: <string>
    +labels: {<label-name>: <label-value>}
    +hours_to_expiration: <integer>
    +kms_key_name: <path-to-key>

Property file

models/properties.yml

version: 2

models:
  - name: [<model-name>]
    config:
      materialized: materialized_view
      on_configuration_change: apply | continue | fail
      cluster_by: <field-name> | [<field-name>]
      partition_by:
        - field: <field-name>
        - data_type: timestamp | date | datetime | int64
          # only if `data_type` is not 'int64'
        - granularity: hour | day | month | year
          # only if `data_type` is 'int64'
        - range:
          - start: <integer>
          - end: <integer>
          - interval: <integer>
      enable_refresh: true | false
      refresh_interval_minutes: <float>
      max_staleness: <interval>
      description: <string>
      labels: {<label-name>: <label-value>}
      hours_to_expiration: <integer>
      kms_key_name: <path-to-key>

Config block

models/<model_name>.sql

{{ config(
    materialized='materialized_view',
    on_configuration_change="apply" | "continue" | "fail",
    cluster_by="<field-name>" | ["<field-name>"],
    partition_by={
        "field": "<field-name>",
        "data_type": "timestamp" | "date" | "datetime" | "int64",

        # only if `data_type` is not 'int64'
        "granularity": "hour" | "day" | "month" | "year,

        # only if `data_type` is 'int64'
        "range": {
            "start": <integer>,
            "end": <integer>,
            "interval": <integer>,
        }
    },

    # auto-refresh options
    enable_refresh= true | false,
    refresh_interval_minutes=<float>,
    max_staleness="<interval>",

    # additional options
    description="<description>",
    labels={
        "<label-name>": "<label-value>",
    },
    hours_to_expiration=<integer>,
    kms_key_name="<path_to_key>",
) }}

Many of these parameters correspond to their table counterparts and have been linked above. The set of parameters unique to materialized views covers auto-refresh functionality.

Learn more about these parameters in BigQuery's docs:

• CREATE MATERIALIZED VIEW statement
• materialized_view_option_list

Auto-refresh

Parameter	Type	Required	Default	Change Monitoring Support
enable_refresh	<boolean>	no	true	alter
refresh_interval_minutes	<float>	no	30	alter
max_staleness (in Preview)	<interval>	no	none	alter

BigQuery supports automatic refresh configuration for materialized views. By default, a materialized view will automatically refresh within 5 minutes of changes in the base table, but not more frequently than once every 30 minutes. BigQuery only officially supports the configuration of the frequency (the "once every 30 minutes" frequency); however, there is a feature in preview that allows for the configuration of the staleness (the "5 minutes" refresh). dbt will monitor these parameters for changes and apply them using an ALTER statement.

Learn more about these parameters in BigQuery's docs:

• materialized_view_option_list
• max_staleness

Limitations

As with most data platforms, there are limitations associated with materialized views. Some worth noting include:

• Materialized view SQL has a limited feature set.
• Materialized view SQL cannot be updated; the materialized view must go through a --full-refresh (DROP/CREATE).
• The partition_by clause on a materialized view must match that of the underlying base table.
• While materialized views can have descriptions, materialized view columns cannot.
• Recreating/dropping the base table requires recreating/dropping the materialized view.

Find more information about materialized view limitations in Google's BigQuery docs.

Python models

The BigQuery adapter supports Python models with the following additional configuration parameters:

Parameter	Type	Required	Default	Valid values
enable_list_inference	<boolean>	no	True	True, False
intermediate_format	<string>	no	parquet	parquet, orc

The enable_list_inference parameter

The enable_list_inference parameter enables a PySpark data frame to read multiple records in the same operation. By default, this is set to True to support the default intermediate_format of parquet.

The intermediate_format parameter

The intermediate_format parameter specifies which file format to use when writing records to a table. The default is parquet.