Question: How do you partition a materialized view in PostgreSQL?


Partitioning a materialized view in PostgreSQL is not directly supported as materialized views cannot be partitioned in the same way that regular tables can. However, you can achieve similar functionality through a combination of techniques involving the creation of a series of regular tables (as partitions), triggers or event-based refresh mechanisms, and potentially a union view to encapsulate the partitioned data. Below is a high-level approach to simulate partitioning for a materialized view:

  1. Create Partition Tables: First, create several tables that will act as partitions. These tables should have the same schema.
CREATE TABLE partition_1 (LIKE your_materialized_view INCLUDING ALL); CREATE TABLE partition_2 (LIKE your_materialized_view INCLUDING ALL); -- Add more partitions as needed
  1. Insert or Refresh Data: Depending on your application's logic, insert or refresh data into these partition tables. This step could be manual or automated through application logic or database triggers.

  2. Create or Refresh a Materialized View: Create a materialized view that unions all the partition tables. This provides a single interface to query all the data.

CREATE MATERIALIZED VIEW partitioned_materialized_view AS SELECT * FROM partition_1 UNION ALL SELECT * FROM partition_2; -- Include all partition tables in the UNION ALL clause
  1. Schedule Refreshes: Since PostgreSQL doesn't directly support refreshing parts of a materialized view, itโ€™s important to manage the refresh process. You can refresh the entire materialized view or selectively refresh the underlying partition tables based on your requirements.

To partially refresh the materialized view, you would truncate and repopulate the relevant partition table(s) and then refresh the materialized view using REFRESH MATERIALIZED VIEW.

  1. Query the Materialized View: Query the partitioned_materialized_view as you would with any other table or view in PostgreSQL.

This approach requires careful planning, especially around data management and refresh strategies, to ensure that the system remains performant and the data stays consistent and up-to-date.

Was this content helpful?

White Paper

Free System Design on AWS E-Book

Download this early release of O'Reilly's latest cloud infrastructure e-book: System Design on AWS.

Free System Design on AWS E-Book
Start building today

Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.