However, materialized views still have to be manually created, monitored, and maintained by data engineers or DBAs. In addition, auto refresh keeps materialized views up to date when base table data is changed, and there are available cluster resources for the materialized view maintenance. The auto rewrite feature enables this by rewriting queries to use materialized views without the query needing to explicitly reference them. The analyst may not even be aware the materialized views exist. With materialized view-aware automatic rewriting, data analysts get the benefit of materialized views for their queries and dashboards without having to query the materialized view directly. In November 2020, materialized view automatic refresh and query rewrite features were added. However, this approach required you to be aware of what materialized views were available on the cluster, and if they were up to date. With these releases, you could use materialized views on both local and external tables to deliver low-latency performance by using precomputed views in your queries. In June 2020, support for external tables was added. Unlike a simple cache, many materialized views can be incrementally refreshed when DML changes are applied on the underlying (base) tables and can be used by other similar queries, not just the query used to create the materialized view.Īmazon Redshift introduced materialized views in March 2020. This improves query performance because many computation steps can be skipped and the precomputed results returned directly. Materialized views store precomputed query results that future similar queries can use. Amazon Redshift now automates this tuning with the automatic table optimization (ATO) feature.Īnother optimization for reducing query runtime is to precompute query results in the form of a materialized view. Amazon Redshift allows you to analyze structured and semi-structured data and seamlessly query data lakes and operational databases, using AWS designed hardware and automated machine learning (ML)-based tuning to deliver top-tier price-performance at scale.Īlthough Amazon Redshift provides excellent price performance out of the box, it offers additional optimizations that can improve this performance and allow you to achieve even faster query response times from your data warehouse.įor example, you can physically tune tables in a data model to minimize the amount of data scanned and distributed within a cluster, which speeds up operations such as table joins and range-bound scans.

Amazon Redshift is a fast, fully managed cloud data warehouse database that makes it cost-effective to analyze your data using standard SQL and business intelligence tools.

Redshift materialized view auto refresh