Adding replace_where incremental strategy (#293)#310
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This PR adds a new incremental strategy replace_where. The strategy resolves to an INSERT INTO ... REPLACE WHERE statement. This completes the feature set explained here: https://docs.databricks.com/delta/selective-overwrite.html#replace-where&language-python A lot of the code change is to bring macros from dbt-spark over to dbt-databricks. The only real code change was in validating incremental strategies and adding the replace_where strategy. **Why do we need it?** It enables use cases where part of the data is always replaced and where MERGE is not possible, such as when there is no primary key. E.g.: events table where we want to always replace the last 3 days. **Difference from insert_overwrite** Insert overwrite only works with dynamic partition pruning spark setting, which is not available in sql warehouses or any Unity Catalog-enabled cluster. It also only works with whole partitions, making it difficult to set up and assure that the correct data is dropped. Signed-off-by: Andre Furlan <andre.furlan@databricks.com>
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This PR adds a new incremental strategy `replace_where`. The strategy resolves to an `INSERT INTO ... REPLACE WHERE` statement. It completes the feature set explained here: https://docs.databricks.com/delta/selective-overwrite.html#replace-where&language-python A lot of the code change is to bring macros from dbt-spark over to dbt-databricks. The only real code change was in validating incremental strategies and adding the replace_where strategy. #### Why do we need it? It enables use cases where part of the data is always replaced and where MERGE is not possible, such as when there is no primary key. E.g.: events table where we want to always replace the last 3 days. #### Difference from insert_overwrite Insert overwrite only works with dynamic partition pruning spark setting, which is not available in sql warehouses or any Unity Catalog-enabled cluster. It also only works with whole partitions, making it difficult to set up and assure that the correct data is dropped. Signed-off-by: Andre Furlan <andre.furlan@databricks.com>
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Description
This PR adds a new incremental strategy
replace_where. The strategy resolves to anINSERT INTO ... REPLACE WHEREstatement. This completes the feature set explained here: https://docs.databricks.com/delta/selective-overwrite.html#replace-where&language-pythonA lot of the code change is to bring macros from dbt-spark over to dbt-databricks. The only real code change was in validating incremental strategies and adding the replace_where strategy.
Why do we need it?
It enables use cases where part of the data is always replaced and where MERGE is not possible, such as when there is no primary key. E.g.: events table where we want to always replace the last 3 days.
Difference from insert_overwrite
Insert overwrite only works with dynamic partition pruning spark setting, which is not available in sql warehouses or any Unity Catalog-enabled cluster. It also only works with whole partitions, making it difficult to set up and assure that the correct data is dropped.
Checklist
CHANGELOG.mdand added information about my change to the "dbt-databricks next" section.