Databricks configurations

Configuring tables

When materializing a model as table, you may include several optional configs that are specific to the dbt-databricks plugin, in addition to the standard model configs.

Option	Description	Required?	Example
file_format	The file format to use when creating tables (parquet, delta, hudi, csv, json, text, jdbc, orc, hive or libsvm).	Optional	delta
location_root	The created table uses the specified directory to store its data. The table alias is appended to it.	Optional	/mnt/root
partition_by	Partition the created table by the specified columns. A directory is created for each partition.	Optional	date_day
clustered_by	Each partition in the created table will be split into a fixed number of buckets by the specified columns.	Optional	country_code
buckets	The number of buckets to create while clustering	Required if clustered_by is specified	8

Incremental models

dbt-databricks plugin leans heavily on the incremental_strategy config. This config tells the incremental materialization how to build models in runs beyond their first. It can be set to one of three values:

• append (default): Insert new records without updating or overwriting any existing data.
• insert_overwrite: If partition_by is specified, overwrite partitions in the table with new data. If no partition_by is specified, overwrite the entire table with new data.
• merge (Delta and Hudi file format only): Match records based on a unique_key; update old records, insert new ones. (If no unique_key is specified, all new data is inserted, similar to append.)

Each of these strategies has its pros and cons, which we'll discuss below. As with any model config, incremental_strategy may be specified in dbt_project.yml or within a model file's config() block.

The append strategy

Following the append strategy, dbt will perform an insert into statement with all new data. The appeal of this strategy is that it is straightforward and functional across all platforms, file types, connection methods, and Apache Spark versions. However, this strategy cannot update, overwrite, or delete existing data, so it is likely to insert duplicate records for many data sources.

Specifying append as the incremental strategy is optional, since it's the default strategy used when none is specified.

Source code

databricks_incremental.sql

{{ config(
    materialized='incremental',
    incremental_strategy='append',
) }}

--  All rows returned by this query will be appended to the existing table

select * from {{ ref('events') }}
{% if is_incremental() %}
  where event_ts > (select max(event_ts) from {{ this }})
{% endif %}

Run code

databricks_incremental.sql

create temporary view databricks_incremental__dbt_tmp as

    select * from analytics.events

    where event_ts >= (select max(event_ts) from {{ this }})

;

insert into table analytics.databricks_incremental
    select `date_day`, `users` from databricks_incremental__dbt_tmp

The insert_overwrite strategy

This strategy is most effective when specified alongside a partition_by clause in your model config. dbt will run an atomic insert overwrite statement that dynamically replaces all partitions included in your query. Be sure to re-select all of the relevant data for a partition when using this incremental strategy.

If no partition_by is specified, then the insert_overwrite strategy will atomically replace all contents of the table, overriding all existing data with only the new records. The column schema of the table remains the same, however. This can be desirable in some limited circumstances, since it minimizes downtime while the table contents are overwritten. The operation is comparable to running truncate + insert on other databases. For atomic replacement of Delta-formatted tables, use the table materialization (which runs create or replace) instead.

Source code

databricks_incremental.sql

{{ config(
    materialized='incremental',
    partition_by=['date_day'],
    file_format='parquet'
) }}

/*
  Every partition returned by this query will be overwritten
  when this model runs
*/

with new_events as (

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

    {% if is_incremental() %}
    where date_day >= date_add(current_date, -1)
    {% endif %}

)

select
    date_day,
    count(*) as users

from events
group by 1

Run code

databricks_incremental.sql

create temporary view databricks_incremental__dbt_tmp as

    with new_events as (

        select * from analytics.events


        where date_day >= date_add(current_date, -1)


    )

    select
        date_day,
        count(*) as users

    from events
    group by 1

;

insert overwrite table analytics.databricks_incremental
    partition (date_day)
    select `date_day`, `users` from databricks_incremental__dbt_tmp

The merge strategy

The merge incremental strategy requires:

• file_format: delta or hudi
• Databricks Runtime 5.1 and above for delta file format
• Apache Spark for hudi file format

dbt will run an atomic merge statement which looks nearly identical to the default merge behavior on Snowflake and BigQuery. If a unique_key is specified (recommended), dbt will update old records with values from new records that match on the key column. If a unique_key is not specified, dbt will forgo match criteria and simply insert all new records (similar to append strategy).

Source code

merge_incremental.sql

{{ config(
    materialized='incremental',
    file_format='delta', # or 'hudi'
    unique_key='user_id',
    incremental_strategy='merge'
) }}

with new_events as (

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

    {% if is_incremental() %}
    where date_day >= date_add(current_date, -1)
    {% endif %}

)

select
    user_id,
    max(date_day) as last_seen

from events
group by 1

Run code

target/run/merge_incremental.sql

create temporary view merge_incremental__dbt_tmp as

    with new_events as (

        select * from analytics.events


        where date_day >= date_add(current_date, -1)


    )

    select
        user_id,
        max(date_day) as last_seen

    from events
    group by 1

;

merge into analytics.merge_incremental as DBT_INTERNAL_DEST
    using merge_incremental__dbt_tmp as DBT_INTERNAL_SOURCE
    on DBT_INTERNAL_SOURCE.user_id = DBT_INTERNAL_DEST.user_id
    when matched then update set *
    when not matched then insert *

Persisting model descriptions

Relation-level docs persistence is supported in dbt v0.17.0. For more information on configuring docs persistence, see the docs.

When the persist_docs option is configured appropriately, you'll be able to see model descriptions in the Comment field of describe [table] extended or show table extended in [database] like '*'.

Default file format configurations

To access advanced incremental strategies features, such as snapshots and the merge incremental strategy, you will want to use the Delta or Hudi file format as the default file format when materializing models as tables.

It's quite convenient to do this by setting a top-level configuration in your project file:

dbt_project.yml

models:
  +file_format: delta # or hudi
  
seeds:
  +file_format: delta # or hudi
  
snapshots:
  +file_format: delta # or hudi