![]() ❗ If a column in the underlying table renamed, or removed and readded (e.g.There's another Redshift system table, stv_mv_info, but it can't effectively be joined with pg_views because they're different types of system tables. ❗ MVs do appear in pg_views, but the only way we can know that they're materialized views is that the create materialized view DDL appear in their definition, instead of just the SQL without DDL (standard views).This package requires an override of load_relation() to perform a "hard" check (database query of stv_mv_info) every time dbt's cache thinks a materializedview relation may already exist. As such, dbt's runtime cache is unable to reliably know if a MV has been dropped when it cascade-drops the underlying table. Instead, a materialized view claims to depend on a table relation called mv_tbl_0, in place of the name of the true underlying table ( awslabs/amazon-redshift-utils#499). This would be fine, except that MVs don't include their "true" dependencies in pg_depend. ❗ MVs do not support late binding, so if an underlying table is cascade-dropped, the MV will be dropped as well. ![]() ![]() Materialized views can significantly boost query performance for repeated and predictable analytical workloads such as dash-boarding, queries from BI tools, and extract, load, transform (ELT) data processing. By contrast, auto_refresh runs in the background, with minimal disruption to other workloads, at the risk of some small potential latency. Tip 1: Precomputing results with Amazon Redshift materialized views. Anecdotally, refresh materialized view.Supported model configs: sort, dist, auto_refresh.If you've installed dbt_labs_materialized_views as a local package, you can achieve this override by creating a file macros/*.sql in your project with the following contents: The Postgres and Redshift implementations both require overriding the builtin versions of some adapter macros. Copy-paste the files from macros/ (specifically default and your adapter) into your own project.Įxtra installation steps for Postgres and Redshift.Git package using project subdirectories: again by referencing the materialized-views folder.Local package: by referencing the materialized-views folder.Install this project as a package ( package-management docs).You can install the materialized-view funcionality using one of the following methods. If you're here, you may also like the dbt-materialize plugin, which enables dbt to materialize models as materialized views in Materialize. Be sure to read the documentation for your adapter. Materialized views vary significantly across databases, as do their current limitations. Depending on the database, that could require DML ( refresh) or no action.Īt any point, if the database object corresponding to a MV model exists instead as a table or standard view, dbt will attempt to drop it and recreate the model from scratch as a materialized view. Otherwise, "refresh" the MV as appropriate.In a "full refresh" run, drop and recreate the MV from scratch.It takes a conceptual approach similar to that of the existing incremental materialization: Dbt_labs_materialized_views is a dbt project containing materializations, helper macros, and some builtin macro overrides that enable use of materialized views in your dbt project.
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