![]() Public | t_demo | table | hs | permanent | 423 MB |Ĥ23 MB vs. Public | mat_view | materialized view | hs | permanent | 16 kB | Schema | Name | Type | Owner | Persistence | Size | Description What is really important to note here is the size of the materialized view compared to the underlying table: Here is an example of a materialized viewĭemo=# CREATE MATERIALIZED VIEW mat_view AS ) ]īasically, a materialized view has a name, some parameters, and is based on a query. To create a materialized view in PostgreSQL, we can make use of the following syntax specification:ĭescription: define a new materialized viewĬREATE MATERIALIZED VIEW table_name We have created 10 million rows organized in 2 groups. ![]() Creating a materialized viewīefore we can actually take a look at materialized views and figure out how they work, we have to import some sample data which we can use as the basis for our calculations:ĭemo=# CREATE TABLE t_demo (grp int, data numeric) ĭemo=# INSERT INTO t_demo SELECT 1, random()ĭemo=# INSERT INTO t_demo SELECT 2, random() The main questions are now: What are the pitfalls, and how can you make use of materialized views in the first place? Let’s dive in and find out. ![]() Naturally, PostgreSQL also provides support for materialized views, and offers the end-user a powerful tool to handle more time-consuming requests. Materialized views are an integral feature of pretty much all advanced database systems. ALTER MATERIALIZED VIEW Materialized view REFRESH MATERIALIZED VIEW REFRESH MATERIALIZED VIEW CONCURRENTLYĪ “materialized view” is a database object which stores the result of a precalculated database query and makes it easy to refresh this result as needed.
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