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Microsoft Fabric DWH 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.

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') }}

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.

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') }}

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.

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') }}

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 a 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.

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 consoleViewing 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') }}

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 two values:

  • merge (default)
  • insert_overwrite

Performance and cost​

The operations performed by dbt while building a BigQuery incremental model can be made cheaper and faster by using clustering keys 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.

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