Postgres Materialized Views: A Practical 101 – Part 2


Welcome back to the second installment of our deep dive into the Materialized views! In our previous post, we looked at the basics of materialized views, showcasing how they can help query performance and simplify complex data aggregations with lightning speed. If you haven’t had the chance to explore these insights, we highly recommend taking a moment to catch up on the first part of this blog series. It sets the stage for the advanced strategies we’re about to uncover, ensuring you’re fully equipped to leverage the full power of materialized views in your PostgreSQL environment.

Beyond the Basics: Indexing Strategies

Another internal aspect to consider is the impact of indexing on materialized views. Just like tables, materialized views can have indexes created on them, which are crucial for query performance. PostgreSQL manages these indexes in the same way as table indexes, using the B-tree indexing method by default but also supporting GIN, GiST, and other indexing strategies. These indexes are particularly valuable when materialized views are used for complex queries involving joins, full-text searches, or aggregations, as they can dramatically reduce query execution times.

Crafting the perfect indexing strategy for your PostgreSQL materialized views can optimize your query performance to new heights. Since materialized views are more than just virtual tables—they’re tangible elements residing within your database—they can be optimized with indexes just like tables. Following approaches would help:

Don’t Forget the Primary Key Index: Just like with your regular tables, indexing the primary key of a materialized view keeps your data aligned with the original tables. Remember, unique data in this column is a must, especially if you’re mixing in concurrent refreshes into your strategy.

Pinpoint the Essential Indexes: Dive into the queries hitting your materialized views and identify which columns are the usual suspects in your query conditions. These are your prime candidates for indexing. However, tread lightly—over-indexing can lead to increased storage use and can bog down the refresh rates of your materialized views.

Select the Optimal Index Type: PostgreSQL isn’t a one-trick pony when it comes to indexes. It boasts a variety, from the all-rounder B-tree to the precise hash, and the more specialized GiST and GIN indexes. B-tree indexes are your go-to for those broad range queries, while hash indexes shine in the face of equality checks. Matching the index type to your query patterns is crucial.

Embrace Composite Indexing: When your queries consistently involve multiple columns, a composite index might be your silver bullet. By bundling columns together in a single index, PostgreSQL can streamline operations like joins, filters, and sorting, reducing the need for separate index lookups.

Iterate with Testing and Tuning: The path to indexing nirvana is iterative. Arm yourself with PostgreSQL’s EXPLAIN and ANALYZE commands to peek into your query execution plans. This insight lets you fine-tune your indexes, ensuring they’re always in top form to meet your performance goals.

By incorporating these nuanced strategies into your PostgreSQL environment, you’ll not only boost the performance of your materialized views but also maintain a balanced system where data integrity, query speed, and storage efficiency coexist harmoniously.

Time-series Data and Matview Alternate

One thing we knwo about the dealing with time-series data — it just keeps coming, and before you know it, your database is brimming. This rapid growth can turn data aggregation into a sluggish chore, making timely insights hard to come by. While working on this problem for a customer, we found Continuous Aggregates from Timescale, which turbocharge the process, making data aggregation as swift as a lightning bolt.

Imagine you’re tracking temperature readings every second of the day. You’re drowning in data and just want a simple average temperature for each hour. Traditionally, every query for this average would mean a laborious trudge through the entire dataset, recalculating each time. It’s as time-consuming as it sounds.

Continuous aggregates, however, change the game. They’re a special breed of hypertable designed to be refreshed on the fly, seamlessly integrating new data or updating with changed data in the background. This is like having a diligent assistant who constantly updates your summaries, tracking any changes in your data and refreshing the behind-the-scenes hypertable without any prompt.

These aggregates update themselves—continuously and incrementally, saving you from the heavy lifting. Compared to the traditional PostgreSQL materialized views, which require a full rebuild with each refresh, continuous aggregates are a breath of fresh air. They significantly reduce the maintenance load, allowing you to focus more on leveraging your data and less on database upkeep.

In essence, continuous aggregates offer a streamlined, efficient path through the ever-growing jungle of time-series data, ensuring your data aggregation is not just fast, but lightning fast.

Learn more at:


Materialized views in PostgreSQL offer a robust solution for improving data retrieval times through precomputed query results. By leveraging advanced features like concurrent refreshes and strategic indexing, developers and data analysts can significantly enhance the performance and responsiveness of their applications. Whether it’s simplifying complex data aggregations or ensuring up-to-date data for reports, materialized views provide a flexible and efficient tool in the PostgreSQL arsenal. For Time-series and huge datasets, looking at alternative solutions like Timescale and Materialize is recommended.

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