RENAME column_name TO new_column_nameĪLTER MATERIALIZED VIEW ALL IN TABLESPACE nameĪLTER column_name SET STATISTICS integerĪLTER column_name RESET ( attribute_option ) The syntax is pretty similar to what ALTER TABLE can do for you:ĭescription: change the definition of a materialized viewĪLTER MATERIALIZED VIEW name Modifying a materialized view is done using the ALTER MATERIALIZED VIEW command which is a powerful tool to facilitate all kinds of changes. Let’s take a look at the definition of the view:Ĭolumn | Type | Collation | Nullable | Default A classical example would be to rename a column of the view without recreating it. Sometimes a materialized view has to be modified later on. Modifying a materialized view in PostgreSQL It’s still worth mentioning that this option does exist. We have not seen the NO DATA option being used too frequently in database support. One has to be aware of the fact that in this case, the view cannot be queried until it has been refreshed to contain data. However, if the object is created using the NO DATA option, it is empty. CREATE MATERIALIZED VIEW … DATA vs NO DATA …īy default, a materialized view is created in a way that it contains the result. If you want to find out more about tablespaces, consider checking out our blog post about tablespaces in PostgreSQL. SHOW is an easy way to figure out where that is. In the default setup, the materialized view will be stored in the default tablespace which is $PGDATA: The same is true for the TABLESPACE keyword. In general, a materialized view is stored just like a table, so simply follow the same recommendations as you would follow when creating a standard table. Here’s an example:ĭemo=# CREATE MATERIALIZED VIEW mat_view_2 It allows you to define storage parameters such as autovacuum behavior, FILLFACTOR and so on. The WITH-clause is the same as in CREATE TABLE. At the moment, heap is the default format, and that’s perfectly fine. The idea of USING is to have the ability to select the best storage format for the given requirement. However, in the future, we hope to see more storage formats such as zheap or zedstore. What is the purpose of this one? At the moment, PostgreSQL supports only one storage format (heap). One of those features is the USING clause. PostgreSQL offers some additional important features which are relevant in this field. Autovacuum is also able to recreate optimizer statistics. If you want to know more about ANALYZE in general, we recommend our posts about PostgreSQL optimizer statistics and autovacuum. Seq Scan on mat_view (cost=0.00.1.02 rows=2 width=26)ĪNALYZE has recreated the new optimizer statistics and fixed the estimate nicely. The process is the same as for any other table: We can simply query the data.Īt this point, the only suboptimal thing is the query optimizer’s estimate (= 1130 rows). Looking at the execution plan ( explain) shows that there is no difference between a normal table and a materialized view. If you want to query the materialized view, you can do it just like you would query a normal PostgreSQL table: 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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