Redshift materialized view limitations8/26/2023 ![]() The following query shows the output of a regular view that is supported with data sharing. Now we can query the materialized view just like a regular view or table and issue statements like SELECT city, totalsales FROM citysales to get the following results. The following table shows how views are supported with data sharing. When sharing regular or late-binding views, you don't have to share the base tables. It also speeds up and simplifies extract, load, and transform (ELT) data processing. A producer cluster can share regular, late-binding, and materialized views. I've not verified that everything the author says in this article is true, but you can reproduce and verify for yourself by querying from svl_statementtext after performing a full refresh of the materialized view. The Amazon Redshift materialized views function helps you achieve significantly faster query performance on repeated or predictable workloads such as dashboard queries from Business Intelligence (BI) tools, such as Amazon QuickSight. If this is correct, then mv_tbl_lirt_cases_mv_0 would be the source object responsible for create/replacing your materialized view lirt_cases_mv, and I don't think there would be any way around having it. ![]() Most of the tables and views in our db have several columns. The house_price_mvw view is create/replaced based on mv_tbl_house_price_mvw_0 Amazon Redshift Dialect for sqlalchemy Homepage PyPI Python License MIT Install pip install. ![]() Just refreshing a materialized view cannot create this issue by itself. So there is more going on that is important to this issue that isnt described. I would think a table could be even more performant since one could add sortkeys. 1 You need two sessions both with a write to create a serialization error. The _tmp table is renamed to mv_tbl_house_price_mvw_0 3 Conceptually, I understand that materialized views are static representations of computed values, but I don't understand how that is functionally different from creating a table that contains the same pre-computed data.So there is more going on that is important to this issue that isn't described. So there is more going on that is important to this issue that isnt described. 1 You need two sessions both with a write to create a serialization error. The table mv_tbl_house_price_mvw_0 is dropped You need two sessions both with a write to create a serialization error.A view called house_price_mvw is create/replaced using the _tmp table.As part of the procedure, a backup table called mv_tbl_house_price_mvw_0_tmp is created using the materialized view query.A materialized view is the landing area for data read from the stream, which is processed as it arrives. A stored procedure called mv_sp_house_price_mvw_0_0 is invoked An Amazon Redshift provisioned cluster is the stream consumer.I found this article that attempts to explain the process of Materialized View refreshes under the hood in Redshift.
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