How to Use Materialized Views in PostgreSQL

Introduction: About Views and Materialized Views

For a complex SQL query, it is impractical to rewrite the entire query every time its results are needed. Views solve this problem. A view is a named (pre-defined) query and a pseudo-table with the output of that query. The code to make a view based on a query looks like this:

-- pseudocode


    SELECT ...

        FROM ... JOIN ... ON ...

        WHERE ... AND ... 

        ORDER BY ...

        LIMIT ...

Essentially, the query is prepended with CREATE VIEW my_view AS. This creates a new view, my_view; query it as if it were a regular table:

-- pseudocode

SELECT ...  

    FROM my_view 

    WHERE ...

Thus, you can access the results of the query using just the view without having to write out the entire query. A view is partially analogous to a “function” in a programming language – a short name for a complex query. It helps with the user experience and readability of the code.

But under the surface, every time the view is accessed, the database converts the view to the full query and (re-)evaluates it before presenting the output. Recomputing a complex query every time is inefficient and leads to no performance gains for the database. On the contrary, repeatedly executing complex queries on large tables degrades the performance.

Like views, materialized views offer a consistent interface to the database. Materialized views abstract away the database design and implementation details to present a consistent querying interface to the API layer. Materialized views cache the results of the query in a persistent structure so it can be accessed without having to be recomputed. This saves time on repeatedly accessed complex queries.

Materialized views solve this problem by storing (caching) the output of a named (pre-defined) query in a persistent data structure similar to a table. You run SELECT queries and create indices on them as if they were a regular table. It is also possible to construct materialized views based on queries on other materialized views.

Materialized views are available in PostgreSQL, Oracle Database, SQL Server, and a few other database engines. This feature is not available on MySQL.


Basic familiarity with the PostgreSQL database is necessary to benefit from this guide. It is assumed you have some experience with PostgreSQL basics – installing the software, creating a new database, creating tables, standard queries, and so on. For the SQL examples, it is assumed that you already have a running PostgreSQL instance set up on either UbuntuFreeBSDCentOS or its successors, or that you are using a managed database service.

The SQL examples in this guide are tested on PostgreSQL 14.5 running on FreeBSD 13.1-RELEASE. They should be compatible with all recent versions of PostgreSQL running on all recent operating systems.

Set up Test Tables

Before creating a materialized view, set up two test tables and populate them with data.

Create a table product:


    product_id INTEGER PRIMARY KEY, 

    name VARCHAR(20) NOT NULL, 



Create a table orders:


    order_id INTEGER, 

    product_id INTEGER REFERENCES product (product_id),

    PRIMARY KEY (order_id, product_id)


Check the descriptions of the created tables:

\d orders

Insert some rows with dummy data into the product table:

 INSERT INTO product (product_id, name, price) VALUES (1, 'Floppy Disk Drive', 40);

 INSERT INTO product (product_id, name, price) VALUES (2, 'Oculus Quest', 400);

Insert dummy data into the orders table:

INSERT INTO orders (order_id, product_id) VALUES (1, 1);

INSERT INTO orders (order_id, product_id) VALUES (1, 2);

INSERT INTO orders (order_id, product_id) VALUES (2, 1);

Check the data in the tables:

SELECT * FROM orders;

SELECT * FROM product;

Create a Materialized View

Create a materialized view on a join query:








    product p 


    orders o 


    p.product_id = o.product_id;

This creates a materialized view, mv_products_orders, and populates it based on the data in the underlying tables at the time of the creation of the materialized view. By default, the column names of the materialized view derive from the column names of the underlying tables.

Check the definition of the newly created materialized view:

\d mv_products_orders

Check the data in mv_products_orders:

SELECT * FROM mv_products_orders;

To rename a materialized view, use the ALTER command:

ALTER MATERIALIZED VIEW mv_products_orders RENAME TO my_mv;

Create an Index on the Materialized View

Indices on materialized views have the same benefits as they do on regular tables – they help in fast lookups. In particular, a common way of refreshing (discussed in a later section) materialized views requires the use of indices. You can define an index on any column(s) of a materialized view.

CREATE UNIQUE INDEX product_order ON mv_products_orders (order_id, product_id);

Check that the description of the materialized view now includes the index:

\d mv_products_orders

(Materialized) Views on (Materialized) Views

It is possible to construct materialized views based on other materialized views.




    * from mv_products_orders

    limit 2;

Similarly, it is also possible to create views based on materialized views and materialized views based on views.

Refreshing (Updating) Materialized Views

PostgreSQL does not automatically refresh materialized views. This means, by default, the data in the materialized view becomes outdated when the underlying tables are updated. You need to update the materialized view manually or configure a system to do it automatically.

Add a new row to the orders table:

INSERT INTO orders (order_id, product_id) VALUES (2, 2);

Re-check the materialized view:

SELECT * FROM mv_products_orders;

The output is still the same as before.

Manual Refresh

The REFRESH command is used to refresh the contents of a materialized view:


Check that the materialized view now includes the newly added data to the orders table:

SELECT * FROM mv_products_orders;

Doing a refresh discards the old contents and recreates the materialized view. Note that it is not possible to query the materialized view while it is being refreshed in this way. The refresh operation places a lock on the materialized view and blocks even SELECT queries on it. This lock is held until the end of the (refresh) transaction.


Refreshing with the CONCURRENTLY option solves this problem.


With the CONCURRENTLY option, the database does not block SELECT queries on the materialized view while it is being refreshed. When this option is specified, internally it creates a temporary data structure with the new results of the materialized view’s query. The old and new results are compared, and the changes are applied to the original materialized view using UPDATE and INSERT operations.

Note that in order to refresh concurrently, the materialized view must contain at least one column-based unique index. When you try to use this on a materialized view that does not have a unique index, it throws an error:

ERROR:  cannot refresh materialized view "public.mv_products_orders" concurrently

HINT:  Create a unique index with no WHERE clause on one or more columns of the materialized view. 

Note also that it is possible to run only one refresh operation on a materialized view at a time (even with the CONCURRENTLY option).


If an update (refresh) involves a lot of new data, the speed of refresh is faster when the CONCURRENTLY option is not used. This is because of all the comparisons and update operations involved in a concurrent refresh.

Practical Tip: In general, it is helpful to periodically vacuum a database to clean up unused data structures and free up space. This is especially relevant with a concurrent refresh because this operation involves the creation of temporary data structures. It is advisable to do the vacuuming after the refresh. Vacuuming is an extensive topic in itself and out of the scope of this guide.

Automatic Refresh

As of November 2022, PostgreSQL has no features for automatically refreshing materialized views. It is, however, possible to set up automatic refreshes using other tools.

Cron Jobs

A common approach to automatically refresh materialized views is by using cron jobs:

15 * * * * psql -d name_of_your_database -c "REFRESH MATERIALIZED VIEW CONCURRENTLY mv_products_orders"

Adding this line to the crontab of user postgres will call the psql command every 15 minutes and pass to it as parameters the name of the database and the SQL command to refresh the materialized view.

For the psql command above, note that if you did not explicitly create or connect to a specific database, by default, queries are executed in the postgres database.

Practical Tip: Since only one refresh operation can run at a time, it is important to have some idea about how long a refresh operation takes before scheduling cron jobs for it.


It is also possible to use triggers to update materialized views. To do this, create a function that refreshes the materialized view. On those tables whose data goes into the materialized view, set a trigger to call this function after INSERTUPDATE, and DELETE operations.

Create a PL/pgSQL – the SQL Procedural Language function that refreshes the materialized view:


RETURNS trigger LANGUAGE plpgsql AS $$






Create a trigger that calls this function when certain operations (INSERTUPDATE, and DELETE) are run on the orders table:

CREATE TRIGGER mv_trigger 


ON orders


Check that the definition of the orders table includes the trigger:

\d orders

Similarly, add a trigger to call the mv_refresh() function when the data in the products table changes.

Delete a row from the orders table:

DELETE FROM orders WHERE order_id = 1 AND product_id = 2;

Check that the materialized view no longer includes the deleted row:

SELECT * FROM mv_products_orders;

Dropping Materialized Views

Dropping a materialized view is similar to dropping a regular view.

DROP MATERIALIZED VIEW mv_products_orders;

To drop the materialized view along with all other objects that depend on it, use the CASCADE option:


The above command drops both materialized views: mv_products_orders, as well as my_mv, which was created based on it.


Like all optimization tools, the use of materialized views involves trade-offs. It is important to understand the specific needs of each use case before deciding whether a tool is a right fit.


Materialized views consume additional storage, but in practice, the cost of extra storage is not a deciding factor when storage is cheap. Also, consider data recency; if the underlying tables are frequently updated, it is likely that the data cached by the materialized view will have been partially outdated by the time it is used. This is a problem for queries that need to return real-time data. Automated refreshes help with data recency, but their use comes with trade-offs, especially for large tables and write-heavy databases.


Materialized views help improve performance, often significantly, in situations where the system needs to handle high volumes of the same (known in advance) complex queries. For example:

  • Reporting and analytics applications that involve complex queries on large tables
  • Database designs involving unstructured or semi-structured data, where querying is inefficient
  • Dashboards presenting collated or consolidated (daily, monthly, and so on) information
  • Queries involving external tables and data stores – where it can be slow or expensive to query the data source repeatedly
  • Providing API services to third parties where contractual requirements often necessitate a consistent API structure and where heavy loads are expected

The use of materialized views is not necessary when the underlying query is simple or fast.

Materialized views are not suitable for queries that power real-time applications like live trading, online bidding, sports scores, messaging, live news feeds, and the like.

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