PostgreSQL Materialized Views: An Overview

What is a materialized view in PostgreSQL?

Materialized views are the precomputed, stored results of an SQL query, which can be based on one or more tables. They are separate database objects that occupy their own storage space in PostgreSQL.

On the other hand, regular views are virtual tables defined by SELECT statements. They don't store data themselves and are dynamic, reflecting the most recent data from the underlying tables. Whenever queries are executed on a regular view, the database engine needs to parse the view's definition query first.

A materialized view can expedite query performance in analytics by storing the precomputed results of complex queries. This removes the need for expensive computations on the underlying tables, leading to faster response times. However, to retrieve the most recent data from the underlying tables, these views need to be refreshed.

When to use materialized views in PostgreSQL？

In the fast-paced world of data management, quick and efficient data access and processing are key. Materialized Views shine in this aspect. So, when should you opt for them? The materialized view is useful in the below scenario:

The underlying base table of the Materialized View does not change frequently.
The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
Query results contain results that require significant processing, including: analysis of semi-structured data and aggregates that take a long time to calculate.
If a query is based on a large table, but you frequently only need a few rows or columns based on certain criteria, an MV can be beneficial.

How to use materialized views in PostgreSQL?

CREATING A MATERIALIZED VIEW

To create a materialized view, you use the CREATE MATERIALIZED VIEW statement followed by the view's name and the query that defines it. Here's an example:

CREATE MATERIALIZED VIEW view_name
AS
query
WITH [NO] DATA;

Replace view_name with your desired name and customize the SELECT statement to fetch the data you need from your source tables. This query defines the content of your materialized view.

REFRESHING A MATERIALIZED VIEW

Materialized views require periodic refreshing, particularly when underlying data alters. PostgreSQL offers several refreshing options.

Manually: You can manually refresh a materialized view using the REFRESH MATERIALIZED VIEW command.

REFRESH MATERIALIZED VIEW view_name;

Automatically: PostgreSQL enables you to schedule automatic refreshes with the REFRESH MATERIALIZED VIEW command. You can pair this command with a scheduler like cron for Linux-based systems or Task Scheduler for Windows.
On Data Changes: You can set up your materialized view to refresh automatically when particular tables it relies on are changed. This can be done with triggers or rules, helping to keep the materialized view current with the source data.
Refresh Methods: PostgreSQL provides options for determining the refresh method of the materialized view. You can use CONCURRENTLY to allow queries to continue during the refresh process. Alternatively, you can use REFRESH without CONCURRENTLY to implement a lock-based refresh.

Use cases

Business Intelligence (BI) and Reporting:

For a company needing to generate daily, weekly, and monthly sales reports from a large transactional database, materialized views can precompute and store aggregated data. This approach speeds up report generation and gives users quick access to the latest figures.

E-commerce Product Recommendations:

An e-commerce website providing personalized product recommendations can benefit from materialized views. By storing and updating user-product interaction data, the system can quickly generate recommendations without querying the entire user history each time.

Geospatial Data Analysis:

A mapping application providing near real-time traffic updates and route recommendations can use materialized views to store geospatial data indexed for quick retrieval. This ensures updates and recommendations are generated quickly, even with vast amounts of dynamic location data.

Financial Analysis and Risk Management:

Financial institutions can use materialized views to store and aggregate trading data, enabling rapid risk assessment and financial analysis. This is especially crucial in fast-paced financial markets.

Inventory Management and Supply Chain Optimization:

Companies managing large inventories can use materialized views to store inventory data and demand forecasting results. This enables quick decision-making in inventory management and supply chain optimization.

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In this blog post, we introduced materialized views in PostgreSQL. We started by understanding what materialized views are and how they differ from regular views, emphasizing that materialized views are physical copies of query results that can significantly improve query performance. We then delved into the process of creating materialized views and the various methods to refresh or update them as the underlying data changes. Lastly, we introduced some classic use cases for materialized views. > >