Most SQL developers eventually reach a point where queries become too heavy. You start joining multiple tables, aggregating large datasets, filtering by time windows, or building dashboards that load slowly because the same complex query runs over and over again. At some point you wish you could store the result of a query and reuse it without recomputing everything.
Materialized views exist for exactly this reason.
A materialized view is a database object that stores the result of a query physically on disk. It is not just a saved query definition. It is an actual snapshot of the data produced by the query at a certain moment. When you query a materialized view, you read precomputed data instead of doing all the work again.
This simple idea has big effects on performance, cost, and system design.
Why Materialized Views Exist
1. Heavy computations become fast reads
If your team has dashboards or APIs that run a big aggregation each time the endpoint is hit, you are wasting CPU cycles and delaying response times. A materialized view turns that heavy work into something stored and ready to read.
2. Predictable performance
Once a materialized view is built, the time it takes to query it is stable. It is the same as querying any other table. This makes performance planning far easier.
3. Offloading repeated work
If multiple services, teams, or jobs need the same derived dataset, a materialized view becomes a single source of truth for that preprocessed data.
How Materialized Views Actually Work
A materialized view is created using a query. The database runs the query once and stores the result. After that, it can refresh the materialized view when needed.
A simple example:
CREATE MATERIALIZED VIEW monthly_sales AS
SELECT
date_trunc('month', sold_at) AS month,
SUM(amount) AS total_sales
FROM sales
GROUP BY 1;
Behind the scenes, the database creates a table-like structure with the aggregated data.
When you query this materialized view:
SELECT * FROM monthly_sales;
you are not recalculating anything. You are just reading stored rows.
But What About Updates
This is where materialized views differ across database systems, so it helps to understand the basic idea.
Materialized views do not automatically stay up to date unless the database supports that feature and you enable it. In many systems, including PostgreSQL, they stay still until you refresh them.
A refresh re-runs the original query and updates the stored results.
REFRESH MATERIALIZED VIEW monthly_sales;
Some databases support incremental or fast refreshes. This means only updated parts of the underlying tables are processed instead of rebuilding everything. Other databases require full refreshes, which can be expensive for large datasets.
Knowing this helps you decide how often to refresh and what performance costs to expect.
What Materialized Views Are Not
New developers sometimes assume materialized views behave like regular views. They do not.
They are not automatically updated
A normal view is like a saved SELECT statement. Each time you query it, the database executes the underlying query and returns fresh results. A materialized view stores results until you refresh it.
They are not free
Materialized views take up storage. They also take time to refresh.
They are not a cure for every slow query
If your underlying query depends on very dynamic data that must always be fresh, materialized views may not help.
When You Should Use Materialized Views
Good uses
- Analytics dashboards that read the same computed results every few minutes
- Pre aggregating time series data
- Providing read optimized datasets to downstream teams
- Reducing expensive join operations in frequent queries
- Caching results for APIs or background jobs
Poor uses
- Highly transactional systems that require immediate consistency
- Datasets that change so often the refresh cost is worse than running the original query
- Scenarios where partial staleness is unacceptable
Materialized Views in the Real World
Materialized views often become part of a data pipeline. For example:
- Raw events enter the database
- Scheduled jobs clean and organize the data
- Materialized views aggregate the data for analytics or reporting
- Dashboards or APIs read only from those materialized views
This approach reduces the load on primary tables, keeps response times predictable, and maintains a clear separation between raw and derived data.
Some engineering teams layer multiple materialized views together. For example, daily summaries feed into monthly summaries, which feed into yearly summaries. This can scale surprisingly well.
What to Watch Out For
Refresh timing
Refreshing too often wastes resources. Refreshing too rarely produces stale results. Pick a schedule that matches your use case.
Locking behavior
Some databases lock a materialized view while refreshing it, preventing reads. Others allow concurrent refreshes. Know what your database supports to avoid blocking production queries.
Storage growth
Materialized views can become large. Monitor their size like any other table.
The Payoff
Once you start using materialized views, you begin to see patterns where they fit naturally. Any repeated and expensive calculation becomes a candidate for precomputation. Over time, this improves performance, reduces cost, and simplifies the mental model of your system.
Materialized views give SQL developers a powerful middle ground between raw tables and fully custom ETL jobs. They are easy to create, easy to query, and can dramatically improve the responsiveness of data heavy applications.
They may not be the first tool SQL developers reach for, but once you understand them, they quickly become one of the most useful.
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