Materialized views in PostgreSQL use the rule system like views do, but persist the results in a table-like form. The main differences between:
CREATE MATERIALIZED VIEW mymatview AS SELECT * FROM mytab;
CREATE TABLE mymatview AS SELECT * FROM mytab;
are that the materialized view cannot subsequently be directly updated and that the query used to create the materialized view is stored in exactly the same way that a view's query is stored, so that fresh data can be generated for the materialized view with:
REFRESH MATERIALIZED VIEW mymatview;
The information about a materialized view in the PostgreSQL system catalogs is exactly the same as it is for a table or view. So for the parser, a materialized view is a relation, just like a table or a view. When a materialized view is referenced in a query, the data is returned directly from the materialized view, like from a table; the rule is only used for populating the materialized view.
While access to the data stored in a materialized view is often much faster than accessing the underlying tables directly or through a view, the data is not always current; yet sometimes current data is not needed. Consider a table which records sales:
CREATE TABLE invoice ( invoice_no integer PRIMARY KEY, seller_no integer, -- ID of salesperson invoice_date date, -- date of sale invoice_amt numeric(13,2) -- amount of sale );
If people want to be able to quickly graph historical sales data, they might want to summarize, and they may not care about the incomplete data for the current date:
CREATE MATERIALIZED VIEW sales_summary AS SELECT seller_no, invoice_date, sum(invoice_amt)::numeric(13,2) as sales_amt FROM invoice WHERE invoice_date < CURRENT_DATE GROUP BY seller_no, invoice_date; CREATE UNIQUE INDEX sales_summary_seller ON sales_summary (seller_no, invoice_date);
This materialized view might be useful for displaying a graph in the dashboard created for salespeople. A job could be scheduled to update the statistics each night using this SQL statement:
REFRESH MATERIALIZED VIEW sales_summary;
Another use for a materialized view is to allow faster access to data brought across from a remote system through a foreign data wrapper. A simple example using
file_fdw is below, with timings, but since this is using cache on the local system the performance difference compared to access to a remote system would usually be greater than shown here. Notice we are also exploiting the ability to put an index on the materialized view, whereas
file_fdw does not support indexes; this advantage might not apply for other sorts of foreign data access.
CREATE EXTENSION file_fdw; CREATE SERVER local_file FOREIGN DATA WRAPPER file_fdw; CREATE FOREIGN TABLE words (word text NOT NULL) SERVER local_file OPTIONS (filename '/usr/share/dict/words'); CREATE MATERIALIZED VIEW wrd AS SELECT * FROM words; CREATE UNIQUE INDEX wrd_word ON wrd (word); CREATE EXTENSION pg_trgm; CREATE INDEX wrd_trgm ON wrd USING gist (word gist_trgm_ops); VACUUM ANALYZE wrd;
Now let's spell-check a word. Using
SELECT count(*) FROM words WHERE word = 'caterpiler'; count ------- 0 (1 row)
EXPLAIN ANALYZE, we see:
Aggregate (cost=21763.99..21764.00 rows=1 width=0) (actual time=188.180..188.181 rows=1 loops=1) -> Foreign Scan on words (cost=0.00..21761.41 rows=1032 width=0) (actual time=188.177..188.177 rows=0 loops=1) Filter: (word = 'caterpiler'::text) Rows Removed by Filter: 479829 Foreign File: /usr/share/dict/words Foreign File Size: 4953699 Planning time: 0.118 ms Execution time: 188.273 ms
If the materialized view is used instead, the query is much faster:
Aggregate (cost=4.44..4.45 rows=1 width=0) (actual time=0.042..0.042 rows=1 loops=1) -> Index Only Scan using wrd_word on wrd (cost=0.42..4.44 rows=1 width=0) (actual time=0.039..0.039 rows=0 loops=1) Index Cond: (word = 'caterpiler'::text) Heap Fetches: 0 Planning time: 0.164 ms Execution time: 0.117 ms
Either way, the word is spelled wrong, so let's look for what we might have wanted. Again using
SELECT word FROM words ORDER BY word <-> 'caterpiler' LIMIT 10; word --------------- cater caterpillar Caterpillar caterpillars caterpillar's Caterpillar's caterer caterer's caters catered (10 rows)
Limit (cost=11583.61..11583.64 rows=10 width=32) (actual time=1431.591..1431.594 rows=10 loops=1) -> Sort (cost=11583.61..11804.76 rows=88459 width=32) (actual time=1431.589..1431.591 rows=10 loops=1) Sort Key: ((word <-> 'caterpiler'::text)) Sort Method: top-N heapsort Memory: 25kB -> Foreign Scan on words (cost=0.00..9672.05 rows=88459 width=32) (actual time=0.057..1286.455 rows=479829 loops=1) Foreign File: /usr/share/dict/words Foreign File Size: 4953699 Planning time: 0.128 ms Execution time: 1431.679 ms
Using the materialized view:
Limit (cost=0.29..1.06 rows=10 width=10) (actual time=187.222..188.257 rows=10 loops=1) -> Index Scan using wrd_trgm on wrd (cost=0.29..37020.87 rows=479829 width=10) (actual time=187.219..188.252 rows=10 loops=1) Order By: (word <-> 'caterpiler'::text) Planning time: 0.196 ms Execution time: 198.640 ms
If you can tolerate periodic update of the remote data to the local database, the performance benefit can be substantial.
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