Automated materialized views - Amazon Redshift
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Automated materialized views

Materialized views are a powerful tool for improving query performance in Amazon Redshift. They do this by storing a precomputed result set. Similar queries don't have to re-run the same logic each time, because they can retrieve records from the existing result set. Developers and analysts create materialized views after analyzing their workloads to determine which queries would benefit, and whether the maintenance cost of each materialized view is worthwhile. As workloads grow or change, these materialized views must be reviewed to ensure they continue to provide tangible performance benefits.

The Automated Materialized Views (AutoMV) feature in Redshift provides the same performance benefits of user-created materialized views. Amazon Redshift continually monitors the workload using machine learning and creates new materialized views when they are beneficial. AutoMV balances the costs of creating and keeping materialized views up to date against expected benefits to query latency. The system also monitors previously created AutoMVs and drops them when they are no longer beneficial.

AutoMV behavior and capabilities are the same as user-created materialized views. They are refreshed automatically and incrementally, using the same criteria and restrictions. Just like materialized views created by users, Automatic query rewriting to use materialized views identifies queries that can benefit from system-created AutoMVs. It automatically rewrites those queries to use the AutoMVs, improving query performance. Developers don't need to revise queries to take advantage of AutoMV.


Automated materialized views are refreshed intermittently. Queries rewritten to use AutoMV always return the latest results. When Redshift detects that data isn't up to date, queries aren't rewritten to read from automated materialized views. Instead, queries select the latest data from base tables.

Any workload with queries that are used repeatedly can benefit from AutoMV. Common use cases include:

  • Dashboards - Dashboards are widely used to provide quick views of key business indicators (KPIs), events, trends, and other metrics. They often have a common layout with charts and tables, but show different views for filtering, or for dimension-selection operations, like drill down. Dashboards often have a common set of queries used repeatedly with different parameters. Dashboard queries can benefit greatly from automated materialized views.

  • Reports - Reporting queries may be scheduled at various frequencies, based on business requirements and the type of report. Additionally, they can be automated or on-demand. A common characteristic of reporting queries is that they can be long running and resource-intensive. With AutoMV, these queries don't need to be recomputed each time they run, which reduces runtime for each query and resource utilization in Redshift.

To turn off automated materialized views, you update the auto_mv parameter group to false. For more information, see Amazon Redshift parameter groups in the Amazon Redshift Cluster Management Guide.

SQL scope and considerations for automated materialized views

  • An automated materialized view can be initiated and created by a query or subquery, provided it contains a GROUP BY clause or one of the following aggregate functions: SUM, COUNT, MIN, MAX or AVG. But it cannot contain any of the following:

    • Left, right, or full outer joins

    • Aggregate functions other than SUM, COUNT, MIN, MAX, and AVG. (These particular functions work with automatic query rewriting.)

    • Any aggregate function that includes DISTINCT

    • Any window functions


    • Other materialized views

    It isn't guaranteed that a query that meets the criteria will initiate the creation of an automated materialized view. The system determines from which candidates to create a view, based on its expected benefit to the workload and cost in resources to maintain, which includes the cost to the system to refresh. Each resulting materialized view is usable by automatic query rewriting.

  • Even though AutoMV might be initiated by a subquery or individual legs of set operators, the resulting materialized view won't contain subqueries or set operators.

  • To determine if AutoMV was used for queries, view the EXPLAIN plan and look for %_auto_mv_% in the output. For more information, see EXPLAIN.

  • Automated materialized views aren't supported on external tables, such as datashares and federated tables.

Automated materialized views limitations

Following are limitations for working with automated materialized views:

  • Maximum number of AutoMVs - The limit of automated materialized views is 200 per database in the cluster.

  • Storage space and capacity - An important characteristic of AutoMV is that it is performed using spare background cycles to help achieve that user workloads are not impacted. If the cluster is busy or running out of storage space, AutoMV ceases its activity. Specifically, at 80% of total cluster capacity, no new automated materialized views are created. At 90% of total capacity, they may be dropped to facilitate that user workloads continue without performance degradation. For more information about determining cluster capacity, see STV_NODE_STORAGE_CAPACITY.

Billing for automated materialized views

Amazon Redshift's automatic optimization capability creates and refreshes automated materialized views. There is no charge for compute resources for this process. Storage of automated materialized views is charged at the regular rate for storage. For more information, see Amazon Redshift pricing.

Additional resources

The following blog post provides further explanation regarding automated materialized views. It details how they’re created, maintained, and dropped. It also explains the underlying algorithms that drive these decisions: Optimize your Amazon Redshift query performance with automated materialized views.

This video begins with an explanation of materialized views and shows how they improve performance and conserve resources. It then provides an in-depth explanation of automated materialized views with a process-flow animation and a live demonstration.