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Mention that EXPLAIN results on small tables shouldn't be extrapolated
to large tables. Recommend ANALYZE or VACUUM ANALYZE after populating a table.
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<!--
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$Header: /cvsroot/pgsql/doc/src/sgml/perform.sgml,v 1.6 2001/06/11 00:52:09 tgl Exp $
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$Header: /cvsroot/pgsql/doc/src/sgml/perform.sgml,v 1.7 2001/06/22 18:53:36 tgl Exp $
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-->
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<chapter id="performance-tips">
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@ -110,7 +110,7 @@ select * from pg_class where relname = 'tenk1';
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</programlisting>
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you'll find out that tenk1 has 233 disk
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pages and 10000 tuples. So the cost is estimated at 233 block
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pages and 10000 tuples. So the cost is estimated at 233 page
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reads, defined as 1.0 apiece, plus 10000 * cpu_tuple_cost which is
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currently 0.01 (try <command>show cpu_tuple_cost</command>).
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</para>
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@ -248,6 +248,19 @@ Hash Join (cost=173.44..557.03 rows=47 width=296)
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10000 times. Note, however, that we are NOT charging 10000 times 173.32;
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the hash table setup is only done once in this plan type.
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</para>
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<para>
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It is worth noting that EXPLAIN results should not be extrapolated
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to situations other than the one you are actually testing; for example,
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results on a toy-sized table can't be assumed to apply to large tables.
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The planner's cost estimates are not linear and so it may well choose
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a different plan for a larger or smaller table. An extreme example
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is that on a table that only occupies one disk page, you'll nearly
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always get a sequential scan plan whether indexes are available or not.
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The planner realizes that it's going to take one disk page read to
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process the table in any case, so there's no value in expending additional
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page reads to look at an index.
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</para>
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</sect1>
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<sect1 id="explicit-joins">
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@ -375,10 +388,13 @@ SELECT * FROM d LEFT JOIN
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<para>
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Turn off auto-commit and just do one commit at
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the end. Otherwise <productname>Postgres</productname> is doing a
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lot of work for each record
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added. In general when you are doing bulk inserts, you want
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to turn off some of the database features to gain speed.
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the end. (In plain SQL, this means issuing <command>BEGIN</command>
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at the start and <command>COMMIT</command> at the end. Some client
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libraries may do this behind your back, in which case you need to
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make sure the library does it when you want it done.)
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If you allow each insertion to be committed separately,
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<productname>Postgres</productname> is doing a lot of work for each
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record added.
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</para>
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</sect2>
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@ -387,10 +403,11 @@ SELECT * FROM d LEFT JOIN
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<para>
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Use <command>COPY FROM STDIN</command> to load all the records in one
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command, instead
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of a series of INSERT commands. This reduces parsing, planning, etc
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command, instead of using
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a series of <command>INSERT</command> commands. This reduces parsing,
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planning, etc
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overhead a great deal. If you do this then it's not necessary to fool
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around with autocommit, since it's only one command anyway.
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around with auto-commit, since it's only one command anyway.
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</para>
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</sect2>
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@ -399,16 +416,32 @@ SELECT * FROM d LEFT JOIN
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<para>
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If you are loading a freshly created table, the fastest way is to
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create the table, bulk-load with COPY, then create any indexes needed
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create the table, bulk-load with <command>COPY</command>, then create any
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indexes needed
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for the table. Creating an index on pre-existing data is quicker than
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updating it incrementally as each record is loaded.
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</para>
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<para>
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If you are augmenting an existing table, you can <command>DROP
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INDEX</command>, load the table, then recreate the index. Of
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INDEX</command>, load the table, then recreate the index. Of
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course, the database performance for other users may be adversely
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affected during the time that the index is missing.
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affected during the time that the index is missing. One should also
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think twice before dropping UNIQUE indexes, since the error checking
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afforded by the UNIQUE constraint will be lost while the index is missing.
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</para>
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</sect2>
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<sect2 id="populate-analyze">
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<title>ANALYZE Afterwards</title>
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<para>
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It's a good idea to run <command>ANALYZE</command> or <command>VACUUM
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ANALYZE</command> anytime you've added or updated a lot of data,
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including just after initially populating a table. This ensures that
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the planner has up-to-date statistics about the table. With no statistics
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or obsolete statistics, the planner may make poor choices of query plans,
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leading to bad performance on queries that use your table.
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</para>
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</sect2>
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</sect1>
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