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Aditya Mathur
Aditya Mathur

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Demystifying Materialized Views in PostgreSQL

Welcome back, dear readers! It's been a while since I last shared my thoughts and insights through my writing. Life took me on a bit of a detour, but I'm thrilled to be back, more passionate than ever, to share the knowledge I've gained during my absence. Writing has always been my way of distilling and sharing what I learn, and I'm excited to continue that journey with you.

In this blog, I'm diving into a fascinating topic: materialized views in PostgreSQL. It's a powerful tool for optimizing database performance and streamlining data analysis. So, let's pick up where we left off and explore this topic together.


In the realm of database management, performance optimization is a perpetual quest. Every byte of data and every query's response time matter. And in this pursuit of efficiency, materialized views emerge as a potent solution.

Materialized views are more than just a database concept; they are a performance-enhancing wizardry. They provide a way to store the results of a complex query as a physical table, effectively precomputing and caching data. The relevance of materialized views lies in their ability to improve query performance drastically, streamline data access, and unlock a world of possibilities for data analysis.

In this blog, we'll delve deep into the fascinating realm of materialized views in PostgreSQL. We'll uncover their inner workings, learn how to create them and explore their benefits and use cases. Whether you're a seasoned database administrator or just venturing into the world of databases, this journey promises to be enlightening.

What is a Materialized View?

A materialized view is a database object that serves as a powerful tool for improving query performance and data analysis. It's essentially a snapshot of the result set of a query that is stored as a physical table. This table, often referred to as the "materialized" table, contains the actual data computed by the query, and it can be indexed, searched, and queried just like any other table in your database.

Contrasting Materialized Views with Regular (Non-Materialized) Views:

To understand the power of materialized views, let's contrast them with regular views. A regular view, also known as a virtual view, is a saved SQL query that acts as a logical window into your data. It doesn't store data itself but retrieves it on the fly every time you query the view. This can be quite efficient for simple queries or when you need to maintain a consistent and up-to-date view of your data. However, for complex and time-consuming queries, the performance can suffer.

Consider a scenario where you have a database containing tables for customers, orders, and products. Now, you want to generate a report that joins these tables to calculate the total sales for each customer, including their name and the products they've ordered. The SQL query might look something like this:

SQL Query
While this query provides the desired information, it involves multiple table joins, aggregations, and calculations, which can be time-consuming, especially as the data grows.

This is where materialized views come into play. You can create a materialized view that stores the result of this complex query as a table, updating it periodically to reflect changes in the source data. Now, when you need the total sales for each customer, you can simply query the materialized view, which offers a substantial performance boost, as the data is precomputed and readily available for analysis.

In essence, materialized views give you the best of both worlds—complex query results stored as physical tables for quick and efficient access, making them a valuable asset in your database management toolkit.

Creating a Materialized View in PostgreSQL

Creating a materialized view in PostgreSQL is a straightforward process. You define the view using a SQL query, and PostgreSQL handles the rest. Here's a step-by-step guide with code examples:

  1. Create the 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:

Replace my_materialized_view 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.

  1. Initial Data Population: Once you create the materialized view, it's empty. You need to populate it with data by running a REFRESH command, like this:
REFRESH MATERIALIZED VIEW my_materialized_view;
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This command executes the query defined in the materialized view to populate it with the initial data.

  1. Query the Materialized View: You can now query the materialized view just like any other table:
SELECT * FROM my_materialized_view;
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Refreshing the Materialized View:
Materialized views store data at a specific point in time. To keep them up to date, you need to periodically refresh them, especially when the underlying data changes. PostgreSQL provides several options for refreshing materialized views:

  • Manually: You can manually refresh a materialized view using the REFRESH MATERIALIZED VIEW command, as shown earlier.

  • Automatically: PostgreSQL allows you to schedule automatic refreshes using the REFRESH MATERIALIZED VIEW command in combination with a scheduler such as cron for Linux-based systems or Task Scheduler for Windows.

  • On Data Changes: You can configure your materialized view to automatically refresh when specific tables it depends on are modified. This can be achieved using triggers or rules, ensuring that the materialized view stays up to date with the source data.

  • Refresh Methods: PostgreSQL offers options for choosing how the materialized view is refreshed. You can use CONCURRENTLY to allow queries on the view to continue during the refresh process or use REFRESH without CONCURRENTLY for a lock-based refresh.

Here's an example of scheduling an automatic refresh using a cron job:

0 3 * * * psql -d your_database_name -c "REFRESH MATERIALIZED VIEW my_materialized_view"
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This cron job refreshes the materialized view my_materialized_view every day at 3:00 AM.

With these steps and options, you can create, populate, and maintain materialized views in PostgreSQL, ensuring that they provide up-to-date information for your queries and analysis.

Benefits of Materialized Views in PostgreSQL

Materialized views are a powerful feature in PostgreSQL that offers several advantages for database administrators and analysts. Let's explore these benefits in detail:

  • Performance Improvement:

Materialized views significantly enhance query performance, especially for complex and time-consuming queries. By precomputing and storing the results of a query, subsequent queries can access the data quickly without the need to recompute the same results repeatedly.

This performance improvement is especially noticeable in scenarios involving large datasets and intricate aggregations or joins.

  • Reduced Overhead:

Materialized views reduce computational overhead on the database server. Since the results are precomputed and stored in a table, the database doesn't need to re-evaluate the query logic each time it's executed.

This reduction in computational load can lead to more efficient resource utilization, freeing up database resources for other tasks.

  • Aggregation and Reporting:

Materialized views are particularly useful for data aggregation, reporting, and data warehousing. They enable you to generate complex reports or perform aggregations on large datasets quickly.

Data warehousing scenarios, where historical data is stored and queried for business intelligence and analysis, benefit greatly from materialized views as they facilitate high-performance access to large volumes of data.

Use Cases

  • Business Intelligence (BI) and Reporting:

Use Case: A company needs to generate daily, weekly, and monthly sales reports from a large transactional database. These reports involve complex aggregations, such as summing sales by region, product, and time period.

Benefit: Materialized views can precompute and store aggregated data, making it much faster to generate reports. Users can access the latest figures without waiting for long-running queries.

  • E-commerce Product Recommendations:

Use Case: An e-commerce website needs to provide personalized product recommendations to its users based on their purchase history and browsing behavior. Recommender systems involve complex data analysis.

Benefit: Materialized views can store and update user-product interaction data, allowing the system to quickly generate personalized recommendations without querying the entire user history each time.

  • Data Warehousing:

Use Case: A large enterprise maintains a data warehouse for historical data analysis. This data warehouse accumulates data from various sources, and analysts need to run complex queries to gain insights.

Benefit: Materialized views can store pre-computed aggregations and subsets of data, reducing query response times and facilitating historical analysis.

  • Geospatial Data Analysis:

Use Case: A mapping application needs to provide users with near real-time traffic updates and route recommendations based on live geospatial data.

Benefit: Materialized views can store geospatial data indexed for quick retrieval. This ensures that traffic updates and route recommendations are generated quickly, even when dealing with vast amounts of dynamic location data.

  • Financial Analysis and Risk Management:

Use Case: Financial institutions require efficient ways to analyze trading data, assess risk, and generate financial reports.

Benefit: Materialized views can store and aggregate trading data, enabling rapid risk assessment and financial analysis, which is crucial in fast-paced financial markets.

  • Content Recommendation in Media Streaming:

Use Case: A streaming platform wants to provide personalized content recommendations to its users based on their viewing history, preferences, and trending content.

Benefit: Materialized views can store user interaction data and content metadata, speeding up the content recommendation engine, and enhancing the user experience.

  • Social Media Analytics:

Use Case: A social media analytics platform needs to process and analyze vast amounts of social media data to track trends, sentiment, and engagement metrics.

Benefit: Materialized views can precompute and store metrics, allowing analysts to perform real-time and historical social media analysis efficiently.

  • Inventory Management and Supply Chain Optimization:

Use Case: A company managing a large inventory needs to optimize stock levels, demand forecasting, and supply chain operations.

Benefit: Materialized views can store inventory data and demand forecasting results, enabling quick decision-making in inventory management and supply chain optimization.


In this blog post, we embarked on a journey to explore the fascinating world of materialized views in PostgreSQL. We began 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, covering the steps to define and populate them with data. Additionally, we discussed the various methods to refresh or update materialized views as the underlying data changes, ensuring they remain up to date.

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Top comments (2)

mortylen profile image

Materialized view is an excellent helper in terms of performance, but we need to consider updating the data to get the data we need. I played with it in MS SQL, but I am a big fan of PostgreSQL, so I will try it here. Thanks for the article.

adii9 profile image
Aditya Mathur • Edited

So when it comes to updating the materialized view, it depends on your workflow. For instance, if you want updated data for making a particular report for analytics you need to understand at what point your base tables will have the most recent data depending upon that you can refresh your view.