Materialized Views

Hydra supports traditional materialized views using both row and columnar tables, as well as incremental materialized views powered by pg_ivm.

Unlike regular views, which are virtual tables that run a query each time they are accessed, materialized views are precomputed database tables that store the results of a query and only update them when they are directly refreshed. Compared to performing equivalent queries on the underlying tables, materialized views can significantly enhance the speed of your queries, particularly for more complex queries.

Materialized views are typically used in scenarios where query performance is critical, queries are executed frequently against large and complex data sets, and the data being queried doesn't change as frequently. In cases where your queried data requires multiple aggregations, joins, and operations that are computed frequently for reporting or analytics, you can efficiently improve performance and reduce execution time using precomputed materialized views.

You can create a materialized view using the following command:

CREATE MATERIALIZED VIEW [ IF NOT EXISTS ] view_name
    [ (column_name [, ...] ) ]
    [ USING method ]
    AS query
    [ WITH [ NO ] DATA ]

You can set USING columnar to store large materialized views in columnar for fast analytics
You can set USING heap for traditional row-based materialized views

For example, here's a materialized view that computes a monthly sales summary containing the total products sold and sales gained:

CREATE MATERIALIZED VIEW sales_summary
  USING columnar
AS
SELECT
    date_trunc('month', order_date) AS month,
    product_id,
    SUM(quantity) AS total_quantity,
    SUM(quantity * price) AS total_sales
FROM
    sales
GROUP BY
    date_trunc('month', order_date),
    product_id;

You can refresh your materialized view with the latest data using REFRESH MATERIALIZED VIEW [ CONCURRENTLY ] view_name. Using the CONCURRENTLY option will allow the view to be refreshed while allowing queries to continue on the current data, but requires a unique index on the view.

Best Practices for Materialized Views

There are a couple of things you can do to make sure you're making the most of materialized views:

Refresh your materialized view: The frequency and methods you employ to refresh your materialized view are crucial factors that influence the overall effectiveness of your view. As refreshing a materialized view can be a resource-intensive operation, it's better suited to data that updates infrequently.
Monitor disk space and performance: Materialized views can consume significant amounts of disk space, so it's essential to monitor their size and delete unused ones to free up space. You should also monitor their refresh performance to ensure that they don't negatively impact the overall performance.
Add indexes to optimize your queries: Materialized views offer a distinct advantage by being stored as regular tables in Postgres. This means that they can fully leverage the benefits of indexing techniques, leading to enhanced performance and efficient processing of large data sets.

Incremental Materialized Views with pg_ivm

Documentation in this section is courtesy pg_ivm.

pg_ivm provides a way to make materialized views up-to-date where only incremental changes are computed and applied on views rather than recomputing the contents from scratch as REFRESH MATERIALIZED VIEW does. This process is called "incremental view maintenance" (or IVM), and it can update materialized views more efficiently than recomputation when only small parts of the view are changed.

There are two approaches with regard to timing of view maintenance: immediate and deferred. In immediate maintenance, views are updated in the same transaction that its base table is modified. In deferred maintenance, views are updated after the transaction is committed, for example, when the view is accessed, as a response to user command like REFRESH MATERIALIZED VIEW, or periodically in background, and so on. pg_ivm provides a kind of immediate maintenance, in which materialized views are updated immediately in AFTER triggers when a base table is modified.

To begin using pg_ivm, you must first enable it on your database:

CREATE EXTENSION IF NOT EXISTS pg_ivm;

We call a materialized view supporting IVM an Incrementally Maintainable Materialized View (IMMV). To create an IMMV, you have to call create_immv function with a relation name and a view definition query. For example:

SELECT create_immv('sales_test', 'SELECT * FROM sales');

creates an IMMV with name 'sales_test' defined as SELECT * FROM sales. This corresponds to the following command to create a normal materialized view:

CREATE MATERIALIZED VIEW sales_test AS SELECT * FROM sales;

When an IMMV is created, some triggers are automatically created so that the view's contents are immediately updated when its base tables are modified.

postgres=# SELECT create_immv('m', 'SELECT * FROM t0');
NOTICE:  could not create an index on immv "m" automatically
DETAIL:  This target list does not have all the primary key columns, or this view does not contain DISTINCT clause.
HINT:  Create an index on the immv for efficient incremental maintenance.
 create_immv
-------------
           3
(1 row)

postgres=# SELECT * FROM m;
 i
---
 1
 2
 3
(3 rows)

postgres=# INSERT INTO t0 VALUES (4);
INSERT 0 1
postgres=# SELECT * FROM m; -- automatically updated
 i
---
 1
 2
 3
 4
(4 rows)

IMMV and Columnar

You can create an IMMV using columnar store by setting the default table access method. If the source table is columnar, you must also disable parallelism temporarily to create the IMMV:

SET default_table_access_method = 'columnar';
SET max_parallel_workers = 1;
SELECT create_immv('sales_test', 'SELECT * FROM sales');

After the IMMV is created, you can restore the settings to their defaults. (These settings do not affect other connections and will also be restored to their defaults upon your next session.)

SET max_parallel_workers = DEFAULT;
SET default_table_access_method = DEFAULT;

Functions

create_immv

Use create_immv function to create IMMV.

create_immv(immv_name text, view_definition text) RETURNS bigint

create_immv defines a new IMMV of a query. A table of the name immv_name is created and a query specified by view_definition is executed and used to populate the IMMV. The query is stored in pg_ivm_immv, so that it can be refreshed later upon incremental view maintenance. create_immv returns the number of rows in the created IMMV.

When an IMMV is created, triggers are automatically created so that the view's contents are immediately updated when its base tables are modified. In addition, a unique index is created on the IMMV automatically if possible. If the view definition query has a GROUP BY clause, a unique index is created on the columns of GROUP BY expressions. Also, if the view has DISTINCT clause, a unique index is created on all columns in the target list. Otherwise, if the IMMV contains all primary key attributes of its base tables in the target list, a unique index is created on these attributes. In other cases, no index is created.

refresh_immv

Use refresh_immv function to refresh IMMV.

refresh_immv(immv_name text, with_data bool) RETURNS bigint

refresh_immv completely replaces the contents of an IMMV as REFRESH MATERIALIZED VIEW command does for a materialized view. To execute this function you must be the owner of the IMMV. The old contents are discarded.

The with_data flag is corresponding to WITH [NO] DATA option of REFRESH MATERIALIZED VIEW command. If with_data is true, the backing query is executed to provide the new data, and if the IMMV is unpopulated, triggers for maintaining the view are created. Also, a unique index is created for IMMV if it is possible and the view doesn't have that yet. If with_data is false, no new data is generated and the IMMV become unpopulated, and the triggers are dropped from the IMMV. Note that unpopulated IMMV is still scannable although the result is empty. This behaviour may be changed in future to raise an error when an unpopulated IMMV is scanned.

get_immv_def

get_immv_def reconstructs the underlying SELECT command for an IMMV. (This is a decompiled reconstruction, not the original text of the command.)

get_immv_def(immv regclass) RETURNS text

IMMV metadata catalog

The catalog pg_ivm_immv stores IMMV information.

Name

Type

Description

immvrelid

regclass

The OID of the IMMV

viewdef

text

Query tree (in the form of a nodeToString() representation) for the view definition

ispopulated

bool

True if IMMV is currently populated

Example

In general, IMMVs allow faster updates than REFRESH MATERIALIZED VIEW at the price of slower updates to their base tables. Update of base tables is slower because triggers will be invoked and the IMMV is updated in triggers per modification statement.

For example, suppose a normal materialized view defined as below:

test=# CREATE MATERIALIZED VIEW mv_normal AS
        SELECT a.aid, b.bid, a.abalance, b.bbalance
        FROM pgbench_accounts a JOIN pgbench_branches b USING(bid);
SELECT 10000000

Updating a tuple in a base table of this materialized view is rapid but the REFRESH MATERIALIZED VIEW command on this view takes a long time:

test=# UPDATE pgbench_accounts SET abalance = 1000 WHERE aid = 1;
UPDATE 1
Time: 9.052 ms

test=# REFRESH MATERIALIZED VIEW mv_normal ;
REFRESH MATERIALIZED VIEW
Time: 20575.721 ms (00:20.576)

On the other hand, after creating IMMV with the same view definition as below:

test=# SELECT create_immv('immv',
        'SELECT a.aid, b.bid, a.abalance, b.bbalance
         FROM pgbench_accounts a JOIN pgbench_branches b USING(bid)');
NOTICE:  created index "immv_index" on immv "immv"
 create_immv
-------------
    10000000
(1 row)

updating a tuple in a base table takes more than the normal view, but its content is updated automatically and this is faster than the REFRESH MATERIALIZED VIEW command.

test=# UPDATE pgbench_accounts SET abalance = 1234 WHERE aid = 1;
UPDATE 1
Time: 15.448 ms

test=# SELECT * FROM immv WHERE aid = 1;
 aid | bid | abalance | bbalance
-----+-----+----------+----------
   1 |   1 |     1234 |        0
(1 row)

An appropriate index on IMMV is necessary for efficient IVM because we need to looks for tuples to be updated in IMMV. If there are no indexes, it will take a long time.

Therefore, when an IMMV is created by the create_immv function, a unique index is created on it automatically if possible. If the view definition query has a GROUP BY clause, a unique index is created on the columns of GROUP BY expressions. Also, if the view has DISTINCT clause, a unique index is created on all columns in the target list. Otherwise, if the IMMV contains all primary key attributes of its base tables in the target list, a unique index is created on these attributes. In other cases, no index is created.

In the previous example, a unique index "immv_index" is created on aid and bid columns of "immv", and this enables the rapid update of the view. Dropping this index make updating the view take a loger time.

test=# DROP INDEX immv_index;
DROP INDEX

test=# UPDATE pgbench_accounts SET abalance = 9876 WHERE aid = 1;
UPDATE 1
Time: 3224.741 ms (00:03.225)

Supported View Definitions and Restriction

Currently, IMMV's view definition can contain inner joins, DISTINCT clause, some built-in aggregate functions, simple sub-queries in FROM clause, and simple CTE (WITH query). Inner joins including self-join are supported, but outer joins are not supported. Supported aggregate functions are count, sum, avg, min and max. Other aggregates, sub-queries which contain an aggregate or DISTINCT clause, sub-queries in other than FROM clause, window functions, HAVING, ORDER BY, LIMIT/OFFSET, UNION/INTERSECT/EXCEPT, DISTINCT ON, TABLESAMPLE, VALUES, and FOR UPDATE/SHARE can not be used in view definition.

The base tables must be simple tables. Views, materialized views, inheritance parent tables, partitioned tables, partitions, and foreign tables can not be used.

The targetlist cannot contain system columns, columns whose name starts with __ivm_.

Logical replication is not supported, that is, even when a base table at a publisher node is modified, IMMVs at subscriber nodes defined on these base tables are not updated.

Limitiations and Restrictions

Aggregates

Supported aggregate functions are count, sum, avg, min, and max. Currently, only built-in aggregate functions are supported and user defined aggregates cannot be used.

When an IMMV including aggregate is created, some extra columns whose name start with __ivm are automatically added to the target list. __ivm_count__ contains the number of tuples aggregated in each group. In addition, more than one extra columns for each column of aggregated value are added in order to maintain the value. For example, columns named like __ivm_count_avg__ and __ivm_sum_avg__ are added for maintaining an average value. When a base table is modified, the new aggregated values are incrementally calculated using the old aggregated values and values of related extra columns stored in the IMMV.

Note that for min or max, the new values could be re-calculated from base tables with regard to the affected groups when a tuple containing the current minimal or maximal values are deleted from a base table. Therefore, it can takes a long time to update an IMMV containing these functions.

Also, note that using sum or avg on real (float4) type or double precision (float8) type in IMMV is unsafe, because aggregated values in IMMV can become different from results calculated from base tables due to the limited precision of these types. To avoid this problem, use the numeric type instead.

Restrictions on Aggregate

If we have a GROUP BY clause, expressions specified in GROUP BY must appear in the target list. This is how tuples to be updated in the IMMV are identified. These attributes are used as scan keys for searching tuples in the IMMV, so indexes on them are required for efficient IVM.

Targetlist cannot contain expressions which contain an aggregate in it.

Subqueries

Simple subqueries in FROM clause are supported.

Restrictions on Subqueries

Subqueries can be used only in FROM clause. Subqueries in target list or WHERE clause are not supported.

Subqueries containing an aggregate function or DISTINCT are not supported.

CTE

Simple CTEs (WITH queries) are supported.

Restrictions on CTEs

WITH queries containing an aggregate function or DISTINCT are not supported.

Recursive queries (WITH RECURSIVE) are not allowed. Unreferenced CTEs are not allowed either, that is, a CTE must be referenced at least once in the view definition query.

DISTINCT

DISTINCT is allowed in IMMV's definition queries. Suppose an IMMV defined with DISTINCT on a base table containing duplicate tuples. When tuples are deleted from the base table, a tuple in the view is deleted if and only if the multiplicity of the tuple becomes zero. Moreover, when tuples are inserted into the base table, a tuple is inserted into the view only if the same tuple doesn't already exist in it.

Physically, an IMMV defined with DISTINCT contains tuples after eliminating duplicates, and the multiplicity of each tuple is stored in a extra column named __ivm_count__ that is added when such IMMV is created.

TRUNCATE

When a base table is truncated, the IMMV is also truncated and the contents become empty if the view definition query does not contain an aggregate without a GROUP BY clause. Aggregate views without a GROUP BY clause always have one row. Therefore, in such cases, if a base table is truncated, the IMMV is simply refreshed instead of being truncated.

Concurrent Transactions

Suppose an IMMV is defined on two base tables and each table was modified in different a concurrent transaction simultaneously. In the transaction which was committed first, the IMMV can be updated considering only the change which happened in this transaction. On the other hand, in order to update the IMMV correctly in the transaction which was committed later, we need to know the changes occurred in both transactions. For this reason, ExclusiveLock is held on an IMMV immediately after a base table is modified in READ COMMITTED mode to make sure that the IMMV is updated in the latter transaction after the former transaction is committed. In REPEATABLE READ or SERIALIZABLE mode, an error is raised immediately if lock acquisition fails because any changes which occurred in other transactions are not be visible in these modes and IMMV cannot be updated correctly in such situations. However, as an exception if the IMMV has only one base table and doesn't use DISTINCT or GROUP BY, and the table is modified by INSERT, then the lock held on the IMMV is RowExclusiveLock.

Row Level Security

If some base tables have row level security policy, rows that are not visible to the materialized view's owner are excluded from the result. In addition, such rows are excluded as well when views are incrementally maintained. However, if a new policy is defined or policies are changed after the materialized view was created, the new policy will not be applied to the view contents. To apply the new policy, you need to recreate IMMV.

How to Disable or Enable Immediate Maintenance

IVM is effective when we want to keep an IMMV up-to-date and small fraction of a base table is modified infrequently. Due to the overhead of immediate maintenance, IVM is not effective when a base table is modified frequently. Also, when a large part of a base table is modified or large data is inserted into a base table, IVM is not effective and the cost of maintenance can be larger than refresh from scratch.

In such situation, we can use refesh_immv function with with_data = false to disable immediate maintenance before modifying a base table. After a base table modification, call refresh_immvwith with_data = true to refresh the view data and enable immediate maintenance.

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