refapizza.blogg.se - Dbt redshift materialized view

Dbt redshift materialized view how to#
Dbt redshift materialized view update#
Dbt redshift materialized view code#

source data tables have millions, or even billions, of rows.

These runs can become problematic in that they use a lot of compute when either: While the table materialization also creates your models as tables, it rebuilds the table on each dbt run. It's often desirable to build models as tables in your data warehouse since downstream queries are more performant.

Understanding incremental models When should I use an incremental model? It's also advisable to rebuild any downstream models, as indicated by the trailing +.įor detailed usage instructions, check out the dbt run documentation. You can learn more about surrogate keys here. Your database and incremental strategy will determine the specific error that you see, so if you're having issues running an incremental model, it's a good idea to double check that the unique key is truly unique in both your existing database table and your new incremental rows. Please note that if there's a unique_key with more than one row in either the existing target table or the new incremental rows, the incremental model run will fail. If the unique_key is not present in the "old" data, dbt will insert the entire row into the table.The exact mechanics of how that update/replace takes place will vary depending on your database, incremental strategy, and strategy specific configs. If the same unique_key is present in the "new" and "old" model data, dbt will update/replace the old row with the new row of data.When you define a unique_key, you'll see this behavior for each row of "new" data returned by your dbt model: Not specifying a unique_key will result in append-only behavior, which means dbt inserts all rows returned by the model's SQL into the preexisting target table without regard for whether the rows represent duplicates.

Dbt redshift materialized view update#

Refer to strategy specific configs for more options on managing this update behavior, like choosing only specific columns to update.

If a duplicate row arrives, it can be ignored. If new information arrives for an existing unique_key, that new information can replace the current information instead of being appended to the table. In some warehouses, filtering your records early can vastly improve the run time of your query! Defining a unique key (optional) Ī unique_key enables updating existing rows instead of just appending new rows.

Dbt redshift materialized view code#

Your is_incremental() code will check for rows created or modified since the last time dbt ran this model.įor example, a model that includes a computationally slow transformation on a column can be built incrementally, as follows:įor more complex incremental models that make use of Common Table Expressions (CTEs), you should consider the impact of the position of the is_incremental() macro on query performance. For updated records, you'll need to define a unique key to ensure you don't bring in modified records as duplicates. dbt makes it easy to query your target table by using the " " variable.Īlso common is wanting to capture both new and updated records. The best way to find the timestamp of the most recent run of this model is by checking the most recent timestamp in your target table. Often, you'll want to filter for "new" rows, as in, rows that have been created since the last time dbt ran this model. To tell dbt which rows it should transform on an incremental run, wrap valid SQL that filters for these rows in the is_incremental() macro.

Dbt redshift materialized view how to#

How to filter the rows on an incremental run.

To use incremental models, you also need to tell dbt: