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I've made a significant effort at filling in the "Using EXPLAIN" section to be reasonably complete about mentioning everything that EXPLAIN can output, including the "Rows Removed" outputs that were added by Marko Tiikkaja's recent documentation-free patch. I also updated the examples to be consistent with current behavior; several of them were not close to what the current code will do. No doubt there's more that can be done here, but I'm out of patience for today.
1554 lines
67 KiB
Plaintext
1554 lines
67 KiB
Plaintext
<!-- doc/src/sgml/perform.sgml -->
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<chapter id="performance-tips">
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<title>Performance Tips</title>
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<indexterm zone="performance-tips">
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<primary>performance</primary>
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</indexterm>
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<para>
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Query performance can be affected by many things. Some of these can
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be controlled by the user, while others are fundamental to the underlying
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design of the system. This chapter provides some hints about understanding
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and tuning <productname>PostgreSQL</productname> performance.
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</para>
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<sect1 id="using-explain">
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<title>Using <command>EXPLAIN</command></title>
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<indexterm zone="using-explain">
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<primary>EXPLAIN</primary>
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</indexterm>
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<indexterm zone="using-explain">
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<primary>query plan</primary>
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</indexterm>
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<para>
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<productname>PostgreSQL</productname> devises a <firstterm>query
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plan</firstterm> for each query it receives. Choosing the right
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plan to match the query structure and the properties of the data
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is absolutely critical for good performance, so the system includes
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a complex <firstterm>planner</> that tries to choose good plans.
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You can use the <xref linkend="sql-explain"> command
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to see what query plan the planner creates for any query.
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Plan-reading is an art that requires some experience to master,
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but this section attempts to cover the basics.
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</para>
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<para>
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Examples in this section are drawn from the regression test database
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after doing a <command>VACUUM ANALYZE</>, using 9.2 development sources.
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You should be able to get similar results if you try the examples
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yourself, but your estimated costs and row counts might vary slightly
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because <command>ANALYZE</>'s statistics are random samples rather
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than exact, and because costs are inherently somewhat platform-dependent.
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</para>
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<para>
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The examples use <command>EXPLAIN</>'s default <quote>text</> output
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format, which is compact and convenient for humans to read.
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If you want to feed <command>EXPLAIN</>'s output to a program for further
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analysis, you should use one of its machine-readable output formats
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(XML, JSON, or YAML) instead.
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</para>
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<sect2 id="using-explain-basics">
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<title><command>EXPLAIN</command> Basics</title>
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<para>
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The structure of a query plan is a tree of <firstterm>plan nodes</>.
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Nodes at the bottom level of the tree are scan nodes: they return raw rows
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from a table. There are different types of scan nodes for different
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table access methods: sequential scans, index scans, and bitmap index
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scans. There are also non-table row sources, such as <literal>VALUES</>
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clauses and set-returning functions in <literal>FROM</>, which have their
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own scan node types.
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If the query requires joining, aggregation, sorting, or other
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operations on the raw rows, then there will be additional nodes
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above the scan nodes to perform these operations. Again,
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there is usually more than one possible way to do these operations,
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so different node types can appear here too. The output
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of <command>EXPLAIN</command> has one line for each node in the plan
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tree, showing the basic node type plus the cost estimates that the planner
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made for the execution of that plan node. Additional lines might appear,
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indented from the node's summary line,
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to show additional properties of the node.
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The very first line (the summary line for the topmost
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node) has the estimated total execution cost for the plan; it is this
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number that the planner seeks to minimize.
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</para>
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<para>
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Here is a trivial example, just to show what the output looks like:
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<screen>
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EXPLAIN SELECT * FROM tenk1;
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QUERY PLAN
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-------------------------------------------------------------
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Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244)
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</screen>
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</para>
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<para>
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Since this query has no <literal>WHERE</> clause, it must scan all the
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rows of the table, so the planner has chosen to use a simple sequential
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scan plan. The numbers that are quoted in parentheses are (left
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to right):
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<itemizedlist>
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<listitem>
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<para>
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Estimated start-up cost. This is the time expended before the output
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phase can begin, e.g., time to do the sorting in a sort node.
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</para>
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</listitem>
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<listitem>
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<para>
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Estimated total cost. This is stated on the assumption that the plan
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node is run to completion, i.e., all available rows are retrieved.
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In practice a node's parent node might stop short of reading all
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available rows (see the <literal>LIMIT</> example below).
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</para>
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</listitem>
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<listitem>
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<para>
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Estimated number of rows output by this plan node. Again, the node
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is assumed to be run to completion.
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</para>
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</listitem>
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<listitem>
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<para>
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Estimated average width of rows output by this plan node (in bytes).
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</para>
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</listitem>
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</itemizedlist>
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</para>
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<para>
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The costs are measured in arbitrary units determined by the planner's
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cost parameters (see <xref linkend="runtime-config-query-constants">).
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Traditional practice is to measure the costs in units of disk page
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fetches; that is, <xref linkend="guc-seq-page-cost"> is conventionally
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set to <literal>1.0</> and the other cost parameters are set relative
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to that. The examples in this section are run with the default cost
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parameters.
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</para>
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<para>
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It's important to understand that the cost of an upper-level node includes
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the cost of all its child nodes. It's also important to realize that
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the cost only reflects things that the planner cares about.
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In particular, the cost does not consider the time spent transmitting
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result rows to the client, which could be an important
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factor in the real elapsed time; but the planner ignores it because
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it cannot change it by altering the plan. (Every correct plan will
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output the same row set, we trust.)
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</para>
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<para>
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The <literal>rows</> value is a little tricky because it is
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not the number of rows processed or scanned by the
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plan node, but rather the number emitted by the node. This is often
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less than the number scanned, as a result of filtering by any
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<literal>WHERE</>-clause conditions that are being applied at the node.
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Ideally the top-level rows estimate will approximate the number of rows
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actually returned, updated, or deleted by the query.
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</para>
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<para>
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Returning to our example:
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<screen>
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EXPLAIN SELECT * FROM tenk1;
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QUERY PLAN
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-------------------------------------------------------------
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Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244)
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</screen>
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</para>
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<para>
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These numbers are derived very straightforwardly. If you do:
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<programlisting>
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SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';
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</programlisting>
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you will find that <classname>tenk1</classname> has 358 disk
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pages and 10000 rows. The estimated cost is computed as (disk pages read *
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<xref linkend="guc-seq-page-cost">) + (rows scanned *
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<xref linkend="guc-cpu-tuple-cost">). By default,
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<varname>seq_page_cost</> is 1.0 and <varname>cpu_tuple_cost</> is 0.01,
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so the estimated cost is (358 * 1.0) + (10000 * 0.01) = 458.
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</para>
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<para>
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Now let's modify the query to add a <literal>WHERE</> condition:
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<screen>
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EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000;
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QUERY PLAN
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------------------------------------------------------------
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Seq Scan on tenk1 (cost=0.00..483.00 rows=7001 width=244)
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Filter: (unique1 < 7000)
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</screen>
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Notice that the <command>EXPLAIN</> output shows the <literal>WHERE</>
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clause being applied as a <quote>filter</> condition attached to the Seq
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Scan plan node. This means that
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the plan node checks the condition for each row it scans, and outputs
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only the ones that pass the condition.
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The estimate of output rows has been reduced because of the
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<literal>WHERE</> clause.
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However, the scan will still have to visit all 10000 rows, so the cost
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hasn't decreased; in fact it has gone up a bit (by 10000 * <xref
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linkend="guc-cpu-operator-cost">, to be exact) to reflect the extra CPU
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time spent checking the <literal>WHERE</> condition.
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</para>
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<para>
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The actual number of rows this query would select is 7000, but the <literal>rows</>
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estimate is only approximate. If you try to duplicate this experiment,
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you will probably get a slightly different estimate; moreover, it can
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change after each <command>ANALYZE</command> command, because the
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statistics produced by <command>ANALYZE</command> are taken from a
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randomized sample of the table.
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</para>
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<para>
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Now, let's make the condition more restrictive:
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<screen>
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EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100;
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QUERY PLAN
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------------------------------------------------------------------------------
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Bitmap Heap Scan on tenk1 (cost=5.03..229.17 rows=101 width=244)
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Recheck Cond: (unique1 < 100)
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-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
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Index Cond: (unique1 < 100)
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</screen>
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Here the planner has decided to use a two-step plan: the child plan
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node visits an index to find the locations of rows matching the index
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condition, and then the upper plan node actually fetches those rows
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from the table itself. Fetching rows separately is much more
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expensive than reading them sequentially, but because not all the pages
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of the table have to be visited, this is still cheaper than a sequential
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scan. (The reason for using two plan levels is that the upper plan
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node sorts the row locations identified by the index into physical order
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before reading them, to minimize the cost of separate fetches.
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The <quote>bitmap</> mentioned in the node names is the mechanism that
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does the sorting.)
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</para>
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<para>
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Now let's add another condition to the <literal>WHERE</> clause:
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<screen>
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EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND stringu1 = 'xxx';
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QUERY PLAN
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------------------------------------------------------------------------------
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Bitmap Heap Scan on tenk1 (cost=5.01..229.40 rows=1 width=244)
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Recheck Cond: (unique1 < 100)
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Filter: (stringu1 = 'xxx'::name)
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-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
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Index Cond: (unique1 < 100)
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</screen>
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The added condition <literal>stringu1 = 'xxx'</literal> reduces the
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output-rowcount estimate, but not the cost because we still have to visit
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the same set of rows. Notice that the <literal>stringu1</> clause
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cannot be applied as an index condition, since this index is only on
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the <literal>unique1</> column. Instead it is applied as a filter on
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the rows retrieved by the index. Thus the cost has actually gone up
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slightly to reflect this extra checking.
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</para>
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<para>
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In some cases the planner will prefer a <quote>simple</> index scan plan:
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<screen>
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EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42;
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QUERY PLAN
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-----------------------------------------------------------------------------
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Index Scan using tenk1_unique1 on tenk1 (cost=0.00..8.27 rows=1 width=244)
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Index Cond: (unique1 = 42)
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</screen>
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In this type of plan the table rows are fetched in index order, which
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makes them even more expensive to read, but there are so few that the
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extra cost of sorting the row locations is not worth it. You'll most
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often see this plan type for queries that fetch just a single row. It's
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also often used for queries that have an <literal>ORDER BY</> condition
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that matches the index order, because then no extra sort step is needed to
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satisfy the <literal>ORDER BY</>.
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</para>
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<para>
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If there are indexes on several columns referenced in <literal>WHERE</>,
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the planner might choose to use an AND or OR combination of the indexes:
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<screen>
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EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000;
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QUERY PLAN
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-------------------------------------------------------------------------------------
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Bitmap Heap Scan on tenk1 (cost=25.01..60.14 rows=10 width=244)
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Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))
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-> BitmapAnd (cost=25.01..25.01 rows=10 width=0)
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-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
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Index Cond: (unique1 < 100)
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-> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.74 rows=999 width=0)
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Index Cond: (unique2 > 9000)
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</screen>
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But this requires visiting both indexes, so it's not necessarily a win
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compared to using just one index and treating the other condition as
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a filter. If you vary the ranges involved you'll see the plan change
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accordingly.
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</para>
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<para>
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Here is an example showing the effects of <literal>LIMIT</>:
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<screen>
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EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2;
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QUERY PLAN
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-------------------------------------------------------------------------------------
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Limit (cost=0.00..14.25 rows=2 width=244)
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-> Index Scan using tenk1_unique2 on tenk1 (cost=0.00..71.23 rows=10 width=244)
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Index Cond: (unique2 > 9000)
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Filter: (unique1 < 100)
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</screen>
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</para>
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<para>
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This is the same query as above, but we added a <literal>LIMIT</> so that
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not all the rows need be retrieved, and the planner changed its mind about
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what to do. Notice that the total cost and row count of the Index Scan
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node are shown as if it were run to completion. However, the Limit node
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is expected to stop after retrieving only a fifth of those rows, so its
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total cost is only a fifth as much, and that's the actual estimated cost
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of the query. This plan is preferred over adding a Limit node to the
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previous plan because the Limit could not avoid paying the startup cost
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of the bitmap scan, so the total cost would be something over 25 units
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with that approach.
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</para>
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<para>
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Let's try joining two tables, using the columns we have been discussing:
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<screen>
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EXPLAIN SELECT *
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FROM tenk1 t1, tenk2 t2
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WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2;
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QUERY PLAN
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--------------------------------------------------------------------------------------
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Nested Loop (cost=4.33..118.25 rows=10 width=488)
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-> Bitmap Heap Scan on tenk1 t1 (cost=4.33..39.44 rows=10 width=244)
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Recheck Cond: (unique1 < 10)
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-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.33 rows=10 width=0)
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Index Cond: (unique1 < 10)
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-> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..7.87 rows=1 width=244)
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Index Cond: (unique2 = t1.unique2)
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</screen>
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</para>
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<para>
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In this plan, we have a nested-loop join node with two table scans as
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inputs, or children. The indentation of the node summary lines reflects
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the plan tree structure. The join's first, or <quote>outer</>, child
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is a bitmap scan similar to those we saw before. Its cost and row count
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are the same as we'd get from <literal>SELECT ... WHERE unique1 < 10</>
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because we are
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applying the <literal>WHERE</> clause <literal>unique1 < 10</literal>
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at that node.
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The <literal>t1.unique2 = t2.unique2</literal> clause is not relevant yet,
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so it doesn't affect the row count of the outer scan. The nested-loop
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join node will run its second,
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or <quote>inner</> child once for each row obtained from the outer child.
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Column values from the current outer row can be plugged into the inner
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scan; here, the <literal>t1.unique2</> value from the outer row is available,
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so we get a plan and costs similar to what we saw above for a simple
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<literal>SELECT ... WHERE t2.unique2 = <replaceable>constant</></> case.
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(The estimated cost is actually a bit lower than what was seen above,
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as a result of caching that's expected to occur during the repeated
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indexscans on <literal>t2</>.) The
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costs of the loop node are then set on the basis of the cost of the outer
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scan, plus one repetition of the inner scan for each outer row (10 * 7.87,
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here), plus a little CPU time for join processing.
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</para>
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<para>
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In this example the join's output row count is the same as the product
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of the two scans' row counts, but that's not true in all cases because
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there can be additional <literal>WHERE</> clauses that mention both tables
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and so can only be applied at the join point, not to either input scan.
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For example, if we add one more condition:
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<screen>
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EXPLAIN SELECT *
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FROM tenk1 t1, tenk2 t2
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WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2 AND t1.hundred < t2.hundred;
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QUERY PLAN
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--------------------------------------------------------------------------------------
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Nested Loop (cost=4.33..118.28 rows=3 width=488)
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Join Filter: (t1.hundred < t2.hundred)
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-> Bitmap Heap Scan on tenk1 t1 (cost=4.33..39.44 rows=10 width=244)
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Recheck Cond: (unique1 < 10)
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-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.33 rows=10 width=0)
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Index Cond: (unique1 < 10)
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-> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..7.87 rows=1 width=244)
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Index Cond: (unique2 = t1.unique2)
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</screen>
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The extra condition <literal>t1.hundred < t2.hundred</literal> can't be
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tested in the <literal>tenk2_unique2</> index, so it's applied at the
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join node. This reduces the estimated output row count of the join node,
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but does not change either input scan.
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</para>
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<para>
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When dealing with outer joins, you might see join plan nodes with both
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<quote>Join Filter</> and plain <quote>Filter</> conditions attached.
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Join Filter conditions come from the outer join's <literal>ON</> clause,
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so a row that fails the Join Filter condition could still get emitted as
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a null-extended row. But a plain Filter condition is applied after the
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outer-join rules and so acts to remove rows unconditionally. In an inner
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join there is no semantic difference between these types of filters.
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</para>
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<para>
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If we change the query's selectivity a bit, we might get a very different
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join plan:
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<screen>
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EXPLAIN SELECT *
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FROM tenk1 t1, tenk2 t2
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WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;
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QUERY PLAN
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------------------------------------------------------------------------------------------
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Hash Join (cost=230.43..713.94 rows=101 width=488)
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Hash Cond: (t2.unique2 = t1.unique2)
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-> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244)
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-> Hash (cost=229.17..229.17 rows=101 width=244)
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-> Bitmap Heap Scan on tenk1 t1 (cost=5.03..229.17 rows=101 width=244)
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Recheck Cond: (unique1 < 100)
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-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
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Index Cond: (unique1 < 100)
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</screen>
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</para>
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<para>
|
|
Here, the planner has chosen to use a hash join, in which rows of one
|
|
table are entered into an in-memory hash table, after which the other
|
|
table is scanned and the hash table is probed for matches to each row.
|
|
Again note how the indentation reflects the plan structure: the bitmap
|
|
scan on <literal>tenk1</> is the input to the Hash node, which constructs
|
|
the hash table. That's then returned to the Hash Join node, which reads
|
|
rows from its outer child plan and searches the hash table for each one.
|
|
</para>
|
|
|
|
<para>
|
|
Another possible type of join is a merge join, illustrated here:
|
|
|
|
<screen>
|
|
EXPLAIN SELECT *
|
|
FROM tenk1 t1, onek t2
|
|
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;
|
|
|
|
QUERY PLAN
|
|
------------------------------------------------------------------------------------------
|
|
Merge Join (cost=197.83..267.93 rows=10 width=488)
|
|
Merge Cond: (t1.unique2 = t2.unique2)
|
|
-> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.00..656.25 rows=101 width=244)
|
|
Filter: (unique1 < 100)
|
|
-> Sort (cost=197.83..200.33 rows=1000 width=244)
|
|
Sort Key: t2.unique2
|
|
-> Seq Scan on onek t2 (cost=0.00..148.00 rows=1000 width=244)
|
|
</screen>
|
|
</para>
|
|
|
|
<para>
|
|
Merge join requires its input data to be sorted on the join keys. In this
|
|
plan the <literal>tenk1</> data is sorted by using an index scan to visit
|
|
the rows in the correct order, but a sequential scan and sort is preferred
|
|
for <literal>onek</>, because there are many more rows to be visited in
|
|
that table.
|
|
(Seqscan-and-sort frequently beats an indexscan for sorting many rows,
|
|
because of the nonsequential disk access required by the indexscan.)
|
|
</para>
|
|
|
|
<para>
|
|
One way to look at variant plans is to force the planner to disregard
|
|
whatever strategy it thought was the cheapest, using the enable/disable
|
|
flags described in <xref linkend="runtime-config-query-enable">.
|
|
(This is a crude tool, but useful. See
|
|
also <xref linkend="explicit-joins">.)
|
|
For example, if we're unconvinced that seqscan-and-sort is the best way to
|
|
deal with table <literal>onek</> in the previous example, we could try
|
|
|
|
<screen>
|
|
SET enable_sort = off;
|
|
|
|
EXPLAIN SELECT *
|
|
FROM tenk1 t1, onek t2
|
|
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;
|
|
|
|
QUERY PLAN
|
|
------------------------------------------------------------------------------------------
|
|
Merge Join (cost=0.00..292.36 rows=10 width=488)
|
|
Merge Cond: (t1.unique2 = t2.unique2)
|
|
-> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.00..656.25 rows=101 width=244)
|
|
Filter: (unique1 < 100)
|
|
-> Index Scan using onek_unique2 on onek t2 (cost=0.00..224.76 rows=1000 width=244)
|
|
</screen>
|
|
|
|
which shows that the planner thinks that sorting <literal>onek</> by
|
|
indexscanning is about 12% more expensive than seqscan-and-sort.
|
|
Of course, the next question is whether it's right about that.
|
|
We can investigate that using <command>EXPLAIN ANALYZE</>, as discussed
|
|
below.
|
|
</para>
|
|
|
|
</sect2>
|
|
|
|
<sect2 id="using-explain-analyze">
|
|
<title><command>EXPLAIN ANALYZE</command></title>
|
|
|
|
<para>
|
|
It is possible to check the accuracy of the planner's estimates
|
|
by using <command>EXPLAIN</>'s <literal>ANALYZE</> option. With this
|
|
option, <command>EXPLAIN</> actually executes the query, and then displays
|
|
the true row counts and true run time accumulated within each plan node,
|
|
along with the same estimates that a plain <command>EXPLAIN</command>
|
|
shows. For example, we might get a result like this:
|
|
|
|
<screen>
|
|
EXPLAIN ANALYZE SELECT *
|
|
FROM tenk1 t1, tenk2 t2
|
|
WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2;
|
|
|
|
QUERY PLAN
|
|
---------------------------------------------------------------------------------------------------------------------------------
|
|
Nested Loop (cost=4.33..118.25 rows=10 width=488) (actual time=0.370..1.126 rows=10 loops=1)
|
|
-> Bitmap Heap Scan on tenk1 t1 (cost=4.33..39.44 rows=10 width=244) (actual time=0.254..0.380 rows=10 loops=1)
|
|
Recheck Cond: (unique1 < 10)
|
|
-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.33 rows=10 width=0) (actual time=0.164..0.164 rows=10 loops=1)
|
|
Index Cond: (unique1 < 10)
|
|
-> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..7.87 rows=1 width=244) (actual time=0.041..0.048 rows=1 loops=10)
|
|
Index Cond: (unique2 = t1.unique2)
|
|
Total runtime: 2.414 ms
|
|
</screen>
|
|
|
|
Note that the <quote>actual time</quote> values are in milliseconds of
|
|
real time, whereas the <literal>cost</> estimates are expressed in
|
|
arbitrary units; so they are unlikely to match up.
|
|
The thing that's usually most important to look for is whether the
|
|
estimated row counts are reasonably close to reality. In this example
|
|
the estimates were all dead-on, but that's quite unusual in practice.
|
|
</para>
|
|
|
|
<para>
|
|
In some query plans, it is possible for a subplan node to be executed more
|
|
than once. For example, the inner index scan will be executed once per
|
|
outer row in the above nested-loop plan. In such cases, the
|
|
<literal>loops</> value reports the
|
|
total number of executions of the node, and the actual time and rows
|
|
values shown are averages per-execution. This is done to make the numbers
|
|
comparable with the way that the cost estimates are shown. Multiply by
|
|
the <literal>loops</> value to get the total time actually spent in
|
|
the node. In the above example, we spent a total of 0.480 milliseconds
|
|
executing the indexscans on <literal>tenk2</>.
|
|
</para>
|
|
|
|
<para>
|
|
In some cases <command>EXPLAIN ANALYZE</> shows additional execution
|
|
statistics beyond the plan node execution times and row counts.
|
|
For example, Sort and Hash nodes provide extra information:
|
|
|
|
<screen>
|
|
EXPLAIN ANALYZE SELECT *
|
|
FROM tenk1 t1, tenk2 t2
|
|
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2 ORDER BY t1.fivethous;
|
|
|
|
QUERY PLAN
|
|
--------------------------------------------------------------------------------------------------------------------------------------------
|
|
Sort (cost=717.30..717.56 rows=101 width=488) (actual time=104.950..105.327 rows=100 loops=1)
|
|
Sort Key: t1.fivethous
|
|
Sort Method: quicksort Memory: 68kB
|
|
-> Hash Join (cost=230.43..713.94 rows=101 width=488) (actual time=3.680..102.396 rows=100 loops=1)
|
|
Hash Cond: (t2.unique2 = t1.unique2)
|
|
-> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) (actual time=0.046..46.219 rows=10000 loops=1)
|
|
-> Hash (cost=229.17..229.17 rows=101 width=244) (actual time=3.184..3.184 rows=100 loops=1)
|
|
Buckets: 1024 Batches: 1 Memory Usage: 27kB
|
|
-> Bitmap Heap Scan on tenk1 t1 (cost=5.03..229.17 rows=101 width=244) (actual time=0.612..1.959 rows=100 loops=1)
|
|
Recheck Cond: (unique1 < 100)
|
|
-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0) (actual time=0.390..0.390 rows=100 loops=1)
|
|
Index Cond: (unique1 < 100)
|
|
Total runtime: 107.392 ms
|
|
</screen>
|
|
|
|
The Sort node shows the sort method used (in particular, whether the sort
|
|
was in-memory or on-disk) and the amount of memory or disk space needed.
|
|
The Hash node shows the number of hash buckets and batches as well as the
|
|
peak amount of memory used for the hash table. (If the number of batches
|
|
exceeds one, there will also be disk space usage involved, but that is not
|
|
shown.)
|
|
</para>
|
|
|
|
<para>
|
|
Another type of extra information is the number of rows removed by a
|
|
filter condition:
|
|
|
|
<screen>
|
|
EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE ten < 7;
|
|
|
|
QUERY PLAN
|
|
----------------------------------------------------------------------------------------------------------
|
|
Seq Scan on tenk1 (cost=0.00..483.00 rows=7000 width=244) (actual time=0.111..59.249 rows=7000 loops=1)
|
|
Filter: (ten < 7)
|
|
Rows Removed by Filter: 3000
|
|
Total runtime: 85.340 ms
|
|
</screen>
|
|
|
|
These counts can be particularly valuable for filter conditions applied at
|
|
join nodes. The <quote>Rows Removed</> line only appears when at least
|
|
one scanned row, or potential join pair in the case of a join node,
|
|
is rejected by the filter condition.
|
|
</para>
|
|
|
|
<para>
|
|
A case similar to filter conditions occurs with <quote>lossy</>
|
|
indexscans. For example, consider this search for polygons containing a
|
|
specific point:
|
|
|
|
<screen>
|
|
EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)';
|
|
|
|
QUERY PLAN
|
|
------------------------------------------------------------------------------------------------------
|
|
Seq Scan on polygon_tbl (cost=0.00..1.05 rows=1 width=32) (actual time=0.251..0.251 rows=0 loops=1)
|
|
Filter: (f1 @> '((0.5,2))'::polygon)
|
|
Rows Removed by Filter: 4
|
|
Total runtime: 0.517 ms
|
|
</screen>
|
|
|
|
The planner thinks (quite correctly) that this sample table is too small
|
|
to bother with an indexscan, so we have a plain sequential scan in which
|
|
all the rows got rejected by the filter condition. But if we force an
|
|
indexscan to be used, we see:
|
|
|
|
<screen>
|
|
SET enable_seqscan TO off;
|
|
|
|
EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)';
|
|
|
|
QUERY PLAN
|
|
--------------------------------------------------------------------------------------------------------------------------
|
|
Index Scan using gpolygonind on polygon_tbl (cost=0.00..8.27 rows=1 width=32) (actual time=0.293..0.293 rows=0 loops=1)
|
|
Index Cond: (f1 @> '((0.5,2))'::polygon)
|
|
Rows Removed by Index Recheck: 1
|
|
Total runtime: 1.054 ms
|
|
</screen>
|
|
|
|
Here we can see that the index returned one candidate row, which was
|
|
then rejected by a recheck of the index condition. This happens because a
|
|
GiST index is <quote>lossy</> for polygon containment tests: it actually
|
|
returns the rows with polygons that overlap the target, and then we have
|
|
to do the exact containment test on those rows.
|
|
</para>
|
|
|
|
<para>
|
|
<command>EXPLAIN</> has a <literal>BUFFERS</> option that can be used with
|
|
<literal>ANALYZE</> to get even more runtime statistics:
|
|
|
|
<screen>
|
|
EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000;
|
|
|
|
QUERY PLAN
|
|
-----------------------------------------------------------------------------------------------------------------------------------
|
|
Bitmap Heap Scan on tenk1 (cost=25.07..60.23 rows=10 width=244) (actual time=3.069..3.213 rows=10 loops=1)
|
|
Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))
|
|
Buffers: shared hit=16
|
|
-> BitmapAnd (cost=25.07..25.07 rows=10 width=0) (actual time=2.967..2.967 rows=0 loops=1)
|
|
Buffers: shared hit=7
|
|
-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.02 rows=102 width=0) (actual time=0.732..0.732 rows=200 loops=1)
|
|
Index Cond: (unique1 < 100)
|
|
Buffers: shared hit=2
|
|
-> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.80 rows=1007 width=0) (actual time=2.015..2.015 rows=1009 loops=1)
|
|
Index Cond: (unique2 > 9000)
|
|
Buffers: shared hit=5
|
|
Total runtime: 3.917 ms
|
|
</screen>
|
|
|
|
The numbers provided by <literal>BUFFERS</> help to identify which parts
|
|
of the query are the most I/O-intensive.
|
|
</para>
|
|
|
|
<para>
|
|
Keep in mind that because <command>EXPLAIN ANALYZE</command> actually
|
|
runs the query, any side-effects will happen as usual, even though
|
|
whatever results the query might output are discarded in favor of
|
|
printing the <command>EXPLAIN</> data. If you want to analyze a
|
|
data-modifying query without changing your tables, you can
|
|
roll the command back afterwards, for example:
|
|
|
|
<screen>
|
|
BEGIN;
|
|
|
|
EXPLAIN ANALYZE UPDATE tenk1 SET hundred = hundred + 1 WHERE unique1 < 100;
|
|
|
|
QUERY PLAN
|
|
--------------------------------------------------------------------------------------------------------------------------------
|
|
Update on tenk1 (cost=5.03..229.42 rows=101 width=250) (actual time=81.055..81.055 rows=0 loops=1)
|
|
-> Bitmap Heap Scan on tenk1 (cost=5.03..229.42 rows=101 width=250) (actual time=0.766..3.396 rows=100 loops=1)
|
|
Recheck Cond: (unique1 < 100)
|
|
-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0) (actual time=0.461..0.461 rows=100 loops=1)
|
|
Index Cond: (unique1 < 100)
|
|
Total runtime: 81.922 ms
|
|
|
|
ROLLBACK;
|
|
</screen>
|
|
</para>
|
|
|
|
<para>
|
|
As seen in this example, when the query is an <command>INSERT</>,
|
|
<command>UPDATE</>, or <command>DELETE</> command, the actual work of
|
|
applying the table changes is done by a top-level Insert, Update,
|
|
or Delete plan node. The plan nodes underneath this node perform
|
|
the work of locating the old rows and/or computing the new data.
|
|
So above, we see the same sort of bitmap table scan we've seen already,
|
|
and its output is fed to an Update node that stores the updated rows.
|
|
It's worth noting that although the data-modifying node can take a
|
|
considerable amount of runtime (here, it's consuming the lion's share
|
|
of the time), the planner does not currently add anything to the cost
|
|
estimates to account for that work. That's because the work to be done is
|
|
the same for every correct query plan, so it doesn't affect planning
|
|
decisions.
|
|
</para>
|
|
|
|
<para>
|
|
The <literal>Total runtime</literal> shown by <command>EXPLAIN
|
|
ANALYZE</command> includes executor start-up and shut-down time, as well
|
|
as the time to run any triggers that are fired, but it does not include
|
|
parsing, rewriting, or planning time.
|
|
Time spent executing <literal>BEFORE</> triggers, if any, is included in
|
|
the time for the related Insert, Update, or Delete node; but time
|
|
spent executing <literal>AFTER</> triggers is not counted there because
|
|
<literal>AFTER</> triggers are fired after completion of the whole plan.
|
|
The total time spent in each trigger
|
|
(either <literal>BEFORE</> or <literal>AFTER</>) is also shown separately.
|
|
Note that deferred constraint triggers will not be executed
|
|
until end of transaction and are thus not shown at all by
|
|
<command>EXPLAIN ANALYZE</command>.
|
|
</para>
|
|
|
|
</sect2>
|
|
|
|
<sect2 id="using-explain-caveats">
|
|
<title>Caveats</title>
|
|
|
|
<para>
|
|
There are two significant ways in which run times measured by
|
|
<command>EXPLAIN ANALYZE</command> can deviate from normal execution of
|
|
the same query. First, since no output rows are delivered to the client,
|
|
network transmission costs and I/O conversion costs are not included.
|
|
Second, the measurement overhead added by <command>EXPLAIN
|
|
ANALYZE</command> can be significant, especially on machines with slow
|
|
<function>gettimeofday()</> operating-system calls.
|
|
</para>
|
|
|
|
<para>
|
|
<command>EXPLAIN</> results should not be extrapolated to situations
|
|
much different from the one you are actually testing; for example,
|
|
results on a toy-sized table cannot be assumed to apply to large tables.
|
|
The planner's cost estimates are not linear and so it might choose
|
|
a different plan for a larger or smaller table. An extreme example
|
|
is that on a table that only occupies one disk page, you'll nearly
|
|
always get a sequential scan plan whether indexes are available or not.
|
|
The planner realizes that it's going to take one disk page read to
|
|
process the table in any case, so there's no value in expending additional
|
|
page reads to look at an index. (We saw this happening in the
|
|
<literal>polygon_tbl</> example above.)
|
|
</para>
|
|
|
|
<para>
|
|
There are cases in which the actual and estimated values won't match up
|
|
well, but nothing is really wrong. One such case occurs when
|
|
plan node execution is stopped short by a <literal>LIMIT</> or similar
|
|
effect. For example, in the <literal>LIMIT</> query we used before,
|
|
|
|
<screen>
|
|
EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2;
|
|
|
|
QUERY PLAN
|
|
-------------------------------------------------------------------------------------------------------------------------------
|
|
Limit (cost=0.00..14.25 rows=2 width=244) (actual time=1.652..2.293 rows=2 loops=1)
|
|
-> Index Scan using tenk1_unique2 on tenk1 (cost=0.00..71.23 rows=10 width=244) (actual time=1.631..2.259 rows=2 loops=1)
|
|
Index Cond: (unique2 > 9000)
|
|
Filter: (unique1 < 100)
|
|
Rows Removed by Filter: 287
|
|
Total runtime: 2.857 ms
|
|
</screen>
|
|
|
|
the estimated cost and rowcount for the Index Scan node are shown as
|
|
though it were run to completion. But in reality the Limit node stopped
|
|
requesting rows after it got two, so the actual rowcount is only 2 and
|
|
the runtime is less than the cost estimate would suggest. This is not
|
|
an estimation error, only a discrepancy in the way the estimates and true
|
|
values are displayed.
|
|
</para>
|
|
|
|
<para>
|
|
Merge joins also have measurement artifacts that can confuse the unwary.
|
|
A merge join will stop reading one input if it's exhausted the other input
|
|
and the next key value in the one input is greater than the last key value
|
|
of the other input; in such a case there can be no more matches and so no
|
|
need to scan the rest of the first input. This results in not reading all
|
|
of one child, with results like those mentioned for <literal>LIMIT</>.
|
|
Also, if the outer (first) child contains rows with duplicate key values,
|
|
the inner (second) child is backed up and rescanned for the portion of its
|
|
rows matching that key value. <command>EXPLAIN ANALYZE</> counts these
|
|
repeated emissions of the same inner rows as if they were real additional
|
|
rows. When there are many outer duplicates, the reported actual rowcount
|
|
for the inner child plan node can be significantly larger than the number
|
|
of rows that are actually in the inner relation.
|
|
</para>
|
|
|
|
<para>
|
|
BitmapAnd and BitmapOr nodes always report their actual rowcounts as zero,
|
|
due to implementation limitations.
|
|
</para>
|
|
</sect2>
|
|
|
|
</sect1>
|
|
|
|
<sect1 id="planner-stats">
|
|
<title>Statistics Used by the Planner</title>
|
|
|
|
<indexterm zone="planner-stats">
|
|
<primary>statistics</primary>
|
|
<secondary>of the planner</secondary>
|
|
</indexterm>
|
|
|
|
<para>
|
|
As we saw in the previous section, the query planner needs to estimate
|
|
the number of rows retrieved by a query in order to make good choices
|
|
of query plans. This section provides a quick look at the statistics
|
|
that the system uses for these estimates.
|
|
</para>
|
|
|
|
<para>
|
|
One component of the statistics is the total number of entries in
|
|
each table and index, as well as the number of disk blocks occupied
|
|
by each table and index. This information is kept in the table
|
|
<link linkend="catalog-pg-class"><structname>pg_class</structname></link>,
|
|
in the columns <structfield>reltuples</structfield> and
|
|
<structfield>relpages</structfield>. We can look at it with
|
|
queries similar to this one:
|
|
|
|
<screen>
|
|
SELECT relname, relkind, reltuples, relpages
|
|
FROM pg_class
|
|
WHERE relname LIKE 'tenk1%';
|
|
|
|
relname | relkind | reltuples | relpages
|
|
----------------------+---------+-----------+----------
|
|
tenk1 | r | 10000 | 358
|
|
tenk1_hundred | i | 10000 | 30
|
|
tenk1_thous_tenthous | i | 10000 | 30
|
|
tenk1_unique1 | i | 10000 | 30
|
|
tenk1_unique2 | i | 10000 | 30
|
|
(5 rows)
|
|
</screen>
|
|
|
|
Here we can see that <structname>tenk1</structname> contains 10000
|
|
rows, as do its indexes, but the indexes are (unsurprisingly) much
|
|
smaller than the table.
|
|
</para>
|
|
|
|
<para>
|
|
For efficiency reasons, <structfield>reltuples</structfield>
|
|
and <structfield>relpages</structfield> are not updated on-the-fly,
|
|
and so they usually contain somewhat out-of-date values.
|
|
They are updated by <command>VACUUM</>, <command>ANALYZE</>, and a
|
|
few DDL commands such as <command>CREATE INDEX</>. A <command>VACUUM</>
|
|
or <command>ANALYZE</> operation that does not scan the entire table
|
|
(which is commonly the case) will incrementally update the
|
|
<structfield>reltuples</structfield> count on the basis of the part
|
|
of the table it did scan, resulting in an approximate value.
|
|
In any case, the planner
|
|
will scale the values it finds in <structname>pg_class</structname>
|
|
to match the current physical table size, thus obtaining a closer
|
|
approximation.
|
|
</para>
|
|
|
|
<indexterm>
|
|
<primary>pg_statistic</primary>
|
|
</indexterm>
|
|
|
|
<para>
|
|
Most queries retrieve only a fraction of the rows in a table, due
|
|
to <literal>WHERE</> clauses that restrict the rows to be
|
|
examined. The planner thus needs to make an estimate of the
|
|
<firstterm>selectivity</> of <literal>WHERE</> clauses, that is,
|
|
the fraction of rows that match each condition in the
|
|
<literal>WHERE</> clause. The information used for this task is
|
|
stored in the
|
|
<link linkend="catalog-pg-statistic"><structname>pg_statistic</structname></link>
|
|
system catalog. Entries in <structname>pg_statistic</structname>
|
|
are updated by the <command>ANALYZE</> and <command>VACUUM
|
|
ANALYZE</> commands, and are always approximate even when freshly
|
|
updated.
|
|
</para>
|
|
|
|
<indexterm>
|
|
<primary>pg_stats</primary>
|
|
</indexterm>
|
|
|
|
<para>
|
|
Rather than look at <structname>pg_statistic</structname> directly,
|
|
it's better to look at its view
|
|
<link linkend="view-pg-stats"><structname>pg_stats</structname></link>
|
|
when examining the statistics manually. <structname>pg_stats</structname>
|
|
is designed to be more easily readable. Furthermore,
|
|
<structname>pg_stats</structname> is readable by all, whereas
|
|
<structname>pg_statistic</structname> is only readable by a superuser.
|
|
(This prevents unprivileged users from learning something about
|
|
the contents of other people's tables from the statistics. The
|
|
<structname>pg_stats</structname> view is restricted to show only
|
|
rows about tables that the current user can read.)
|
|
For example, we might do:
|
|
|
|
<screen>
|
|
SELECT attname, inherited, n_distinct,
|
|
array_to_string(most_common_vals, E'\n') as most_common_vals
|
|
FROM pg_stats
|
|
WHERE tablename = 'road';
|
|
|
|
attname | inherited | n_distinct | most_common_vals
|
|
---------+-----------+------------+------------------------------------
|
|
name | f | -0.363388 | I- 580 Ramp+
|
|
| | | I- 880 Ramp+
|
|
| | | Sp Railroad +
|
|
| | | I- 580 +
|
|
| | | I- 680 Ramp
|
|
name | t | -0.284859 | I- 880 Ramp+
|
|
| | | I- 580 Ramp+
|
|
| | | I- 680 Ramp+
|
|
| | | I- 580 +
|
|
| | | State Hwy 13 Ramp
|
|
(2 rows)
|
|
</screen>
|
|
|
|
Note that two rows are displayed for the same column, one corresponding
|
|
to the complete inheritance hierarchy starting at the
|
|
<literal>road</literal> table (<literal>inherited</>=<literal>t</>),
|
|
and another one including only the <literal>road</literal> table itself
|
|
(<literal>inherited</>=<literal>f</>).
|
|
</para>
|
|
|
|
<para>
|
|
The amount of information stored in <structname>pg_statistic</structname>
|
|
by <command>ANALYZE</>, in particular the maximum number of entries in the
|
|
<structfield>most_common_vals</> and <structfield>histogram_bounds</>
|
|
arrays for each column, can be set on a
|
|
column-by-column basis using the <command>ALTER TABLE SET STATISTICS</>
|
|
command, or globally by setting the
|
|
<xref linkend="guc-default-statistics-target"> configuration variable.
|
|
The default limit is presently 100 entries. Raising the limit
|
|
might allow more accurate planner estimates to be made, particularly for
|
|
columns with irregular data distributions, at the price of consuming
|
|
more space in <structname>pg_statistic</structname> and slightly more
|
|
time to compute the estimates. Conversely, a lower limit might be
|
|
sufficient for columns with simple data distributions.
|
|
</para>
|
|
|
|
<para>
|
|
Further details about the planner's use of statistics can be found in
|
|
<xref linkend="planner-stats-details">.
|
|
</para>
|
|
|
|
</sect1>
|
|
|
|
<sect1 id="explicit-joins">
|
|
<title>Controlling the Planner with Explicit <literal>JOIN</> Clauses</title>
|
|
|
|
<indexterm zone="explicit-joins">
|
|
<primary>join</primary>
|
|
<secondary>controlling the order</secondary>
|
|
</indexterm>
|
|
|
|
<para>
|
|
It is possible
|
|
to control the query planner to some extent by using the explicit <literal>JOIN</>
|
|
syntax. To see why this matters, we first need some background.
|
|
</para>
|
|
|
|
<para>
|
|
In a simple join query, such as:
|
|
<programlisting>
|
|
SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;
|
|
</programlisting>
|
|
the planner is free to join the given tables in any order. For
|
|
example, it could generate a query plan that joins A to B, using
|
|
the <literal>WHERE</> condition <literal>a.id = b.id</>, and then
|
|
joins C to this joined table, using the other <literal>WHERE</>
|
|
condition. Or it could join B to C and then join A to that result.
|
|
Or it could join A to C and then join them with B — but that
|
|
would be inefficient, since the full Cartesian product of A and C
|
|
would have to be formed, there being no applicable condition in the
|
|
<literal>WHERE</> clause to allow optimization of the join. (All
|
|
joins in the <productname>PostgreSQL</productname> executor happen
|
|
between two input tables, so it's necessary to build up the result
|
|
in one or another of these fashions.) The important point is that
|
|
these different join possibilities give semantically equivalent
|
|
results but might have hugely different execution costs. Therefore,
|
|
the planner will explore all of them to try to find the most
|
|
efficient query plan.
|
|
</para>
|
|
|
|
<para>
|
|
When a query only involves two or three tables, there aren't many join
|
|
orders to worry about. But the number of possible join orders grows
|
|
exponentially as the number of tables expands. Beyond ten or so input
|
|
tables it's no longer practical to do an exhaustive search of all the
|
|
possibilities, and even for six or seven tables planning might take an
|
|
annoyingly long time. When there are too many input tables, the
|
|
<productname>PostgreSQL</productname> planner will switch from exhaustive
|
|
search to a <firstterm>genetic</firstterm> probabilistic search
|
|
through a limited number of possibilities. (The switch-over threshold is
|
|
set by the <xref linkend="guc-geqo-threshold"> run-time
|
|
parameter.)
|
|
The genetic search takes less time, but it won't
|
|
necessarily find the best possible plan.
|
|
</para>
|
|
|
|
<para>
|
|
When the query involves outer joins, the planner has less freedom
|
|
than it does for plain (inner) joins. For example, consider:
|
|
<programlisting>
|
|
SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);
|
|
</programlisting>
|
|
Although this query's restrictions are superficially similar to the
|
|
previous example, the semantics are different because a row must be
|
|
emitted for each row of A that has no matching row in the join of B and C.
|
|
Therefore the planner has no choice of join order here: it must join
|
|
B to C and then join A to that result. Accordingly, this query takes
|
|
less time to plan than the previous query. In other cases, the planner
|
|
might be able to determine that more than one join order is safe.
|
|
For example, given:
|
|
<programlisting>
|
|
SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id);
|
|
</programlisting>
|
|
it is valid to join A to either B or C first. Currently, only
|
|
<literal>FULL JOIN</> completely constrains the join order. Most
|
|
practical cases involving <literal>LEFT JOIN</> or <literal>RIGHT JOIN</>
|
|
can be rearranged to some extent.
|
|
</para>
|
|
|
|
<para>
|
|
Explicit inner join syntax (<literal>INNER JOIN</>, <literal>CROSS
|
|
JOIN</>, or unadorned <literal>JOIN</>) is semantically the same as
|
|
listing the input relations in <literal>FROM</>, so it does not
|
|
constrain the join order.
|
|
</para>
|
|
|
|
<para>
|
|
Even though most kinds of <literal>JOIN</> don't completely constrain
|
|
the join order, it is possible to instruct the
|
|
<productname>PostgreSQL</productname> query planner to treat all
|
|
<literal>JOIN</> clauses as constraining the join order anyway.
|
|
For example, these three queries are logically equivalent:
|
|
<programlisting>
|
|
SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;
|
|
SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id;
|
|
SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);
|
|
</programlisting>
|
|
But if we tell the planner to honor the <literal>JOIN</> order,
|
|
the second and third take less time to plan than the first. This effect
|
|
is not worth worrying about for only three tables, but it can be a
|
|
lifesaver with many tables.
|
|
</para>
|
|
|
|
<para>
|
|
To force the planner to follow the join order laid out by explicit
|
|
<literal>JOIN</>s,
|
|
set the <xref linkend="guc-join-collapse-limit"> run-time parameter to 1.
|
|
(Other possible values are discussed below.)
|
|
</para>
|
|
|
|
<para>
|
|
You do not need to constrain the join order completely in order to
|
|
cut search time, because it's OK to use <literal>JOIN</> operators
|
|
within items of a plain <literal>FROM</> list. For example, consider:
|
|
<programlisting>
|
|
SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...;
|
|
</programlisting>
|
|
With <varname>join_collapse_limit</> = 1, this
|
|
forces the planner to join A to B before joining them to other tables,
|
|
but doesn't constrain its choices otherwise. In this example, the
|
|
number of possible join orders is reduced by a factor of 5.
|
|
</para>
|
|
|
|
<para>
|
|
Constraining the planner's search in this way is a useful technique
|
|
both for reducing planning time and for directing the planner to a
|
|
good query plan. If the planner chooses a bad join order by default,
|
|
you can force it to choose a better order via <literal>JOIN</> syntax
|
|
— assuming that you know of a better order, that is. Experimentation
|
|
is recommended.
|
|
</para>
|
|
|
|
<para>
|
|
A closely related issue that affects planning time is collapsing of
|
|
subqueries into their parent query. For example, consider:
|
|
<programlisting>
|
|
SELECT *
|
|
FROM x, y,
|
|
(SELECT * FROM a, b, c WHERE something) AS ss
|
|
WHERE somethingelse;
|
|
</programlisting>
|
|
This situation might arise from use of a view that contains a join;
|
|
the view's <literal>SELECT</> rule will be inserted in place of the view
|
|
reference, yielding a query much like the above. Normally, the planner
|
|
will try to collapse the subquery into the parent, yielding:
|
|
<programlisting>
|
|
SELECT * FROM x, y, a, b, c WHERE something AND somethingelse;
|
|
</programlisting>
|
|
This usually results in a better plan than planning the subquery
|
|
separately. (For example, the outer <literal>WHERE</> conditions might be such that
|
|
joining X to A first eliminates many rows of A, thus avoiding the need to
|
|
form the full logical output of the subquery.) But at the same time,
|
|
we have increased the planning time; here, we have a five-way join
|
|
problem replacing two separate three-way join problems. Because of the
|
|
exponential growth of the number of possibilities, this makes a big
|
|
difference. The planner tries to avoid getting stuck in huge join search
|
|
problems by not collapsing a subquery if more than <varname>from_collapse_limit</>
|
|
<literal>FROM</> items would result in the parent
|
|
query. You can trade off planning time against quality of plan by
|
|
adjusting this run-time parameter up or down.
|
|
</para>
|
|
|
|
<para>
|
|
<xref linkend="guc-from-collapse-limit"> and <xref
|
|
linkend="guc-join-collapse-limit">
|
|
are similarly named because they do almost the same thing: one controls
|
|
when the planner will <quote>flatten out</> subqueries, and the
|
|
other controls when it will flatten out explicit joins. Typically
|
|
you would either set <varname>join_collapse_limit</> equal to
|
|
<varname>from_collapse_limit</> (so that explicit joins and subqueries
|
|
act similarly) or set <varname>join_collapse_limit</> to 1 (if you want
|
|
to control join order with explicit joins). But you might set them
|
|
differently if you are trying to fine-tune the trade-off between planning
|
|
time and run time.
|
|
</para>
|
|
</sect1>
|
|
|
|
<sect1 id="populate">
|
|
<title>Populating a Database</title>
|
|
|
|
<para>
|
|
One might need to insert a large amount of data when first populating
|
|
a database. This section contains some suggestions on how to make
|
|
this process as efficient as possible.
|
|
</para>
|
|
|
|
<sect2 id="disable-autocommit">
|
|
<title>Disable Autocommit</title>
|
|
|
|
<indexterm>
|
|
<primary>autocommit</primary>
|
|
<secondary>bulk-loading data</secondary>
|
|
</indexterm>
|
|
|
|
<para>
|
|
When using multiple <command>INSERT</>s, turn off autocommit and just do
|
|
one commit at the end. (In plain
|
|
SQL, this means issuing <command>BEGIN</command> at the start and
|
|
<command>COMMIT</command> at the end. Some client libraries might
|
|
do this behind your back, in which case you need to make sure the
|
|
library does it when you want it done.) If you allow each
|
|
insertion to be committed separately,
|
|
<productname>PostgreSQL</productname> is doing a lot of work for
|
|
each row that is added. An additional benefit of doing all
|
|
insertions in one transaction is that if the insertion of one row
|
|
were to fail then the insertion of all rows inserted up to that
|
|
point would be rolled back, so you won't be stuck with partially
|
|
loaded data.
|
|
</para>
|
|
</sect2>
|
|
|
|
<sect2 id="populate-copy-from">
|
|
<title>Use <command>COPY</command></title>
|
|
|
|
<para>
|
|
Use <xref linkend="sql-copy"> to load
|
|
all the rows in one command, instead of using a series of
|
|
<command>INSERT</command> commands. The <command>COPY</command>
|
|
command is optimized for loading large numbers of rows; it is less
|
|
flexible than <command>INSERT</command>, but incurs significantly
|
|
less overhead for large data loads. Since <command>COPY</command>
|
|
is a single command, there is no need to disable autocommit if you
|
|
use this method to populate a table.
|
|
</para>
|
|
|
|
<para>
|
|
If you cannot use <command>COPY</command>, it might help to use <xref
|
|
linkend="sql-prepare"> to create a
|
|
prepared <command>INSERT</command> statement, and then use
|
|
<command>EXECUTE</command> as many times as required. This avoids
|
|
some of the overhead of repeatedly parsing and planning
|
|
<command>INSERT</command>. Different interfaces provide this facility
|
|
in different ways; look for <quote>prepared statements</> in the interface
|
|
documentation.
|
|
</para>
|
|
|
|
<para>
|
|
Note that loading a large number of rows using
|
|
<command>COPY</command> is almost always faster than using
|
|
<command>INSERT</command>, even if <command>PREPARE</> is used and
|
|
multiple insertions are batched into a single transaction.
|
|
</para>
|
|
|
|
<para>
|
|
<command>COPY</command> is fastest when used within the same
|
|
transaction as an earlier <command>CREATE TABLE</command> or
|
|
<command>TRUNCATE</command> command. In such cases no WAL
|
|
needs to be written, because in case of an error, the files
|
|
containing the newly loaded data will be removed anyway.
|
|
However, this consideration only applies when
|
|
<xref linkend="guc-wal-level"> is <literal>minimal</> as all commands
|
|
must write WAL otherwise.
|
|
</para>
|
|
|
|
</sect2>
|
|
|
|
<sect2 id="populate-rm-indexes">
|
|
<title>Remove Indexes</title>
|
|
|
|
<para>
|
|
If you are loading a freshly created table, the fastest method is to
|
|
create the table, bulk load the table's data using
|
|
<command>COPY</command>, then create any indexes needed for the
|
|
table. Creating an index on pre-existing data is quicker than
|
|
updating it incrementally as each row is loaded.
|
|
</para>
|
|
|
|
<para>
|
|
If you are adding large amounts of data to an existing table,
|
|
it might be a win to drop the indexes,
|
|
load the table, and then recreate the indexes. Of course, the
|
|
database performance for other users might suffer
|
|
during the time the indexes are missing. One should also think
|
|
twice before dropping a unique index, since the error checking
|
|
afforded by the unique constraint will be lost while the index is
|
|
missing.
|
|
</para>
|
|
</sect2>
|
|
|
|
<sect2 id="populate-rm-fkeys">
|
|
<title>Remove Foreign Key Constraints</title>
|
|
|
|
<para>
|
|
Just as with indexes, a foreign key constraint can be checked
|
|
<quote>in bulk</> more efficiently than row-by-row. So it might be
|
|
useful to drop foreign key constraints, load data, and re-create
|
|
the constraints. Again, there is a trade-off between data load
|
|
speed and loss of error checking while the constraint is missing.
|
|
</para>
|
|
|
|
<para>
|
|
What's more, when you load data into a table with existing foreign key
|
|
constraints, each new row requires an entry in the server's list of
|
|
pending trigger events (since it is the firing of a trigger that checks
|
|
the row's foreign key constraint). Loading many millions of rows can
|
|
cause the trigger event queue to overflow available memory, leading to
|
|
intolerable swapping or even outright failure of the command. Therefore
|
|
it may be <emphasis>necessary</>, not just desirable, to drop and re-apply
|
|
foreign keys when loading large amounts of data. If temporarily removing
|
|
the constraint isn't acceptable, the only other recourse may be to split
|
|
up the load operation into smaller transactions.
|
|
</para>
|
|
</sect2>
|
|
|
|
<sect2 id="populate-work-mem">
|
|
<title>Increase <varname>maintenance_work_mem</varname></title>
|
|
|
|
<para>
|
|
Temporarily increasing the <xref linkend="guc-maintenance-work-mem">
|
|
configuration variable when loading large amounts of data can
|
|
lead to improved performance. This will help to speed up <command>CREATE
|
|
INDEX</> commands and <command>ALTER TABLE ADD FOREIGN KEY</> commands.
|
|
It won't do much for <command>COPY</> itself, so this advice is
|
|
only useful when you are using one or both of the above techniques.
|
|
</para>
|
|
</sect2>
|
|
|
|
<sect2 id="populate-checkpoint-segments">
|
|
<title>Increase <varname>checkpoint_segments</varname></title>
|
|
|
|
<para>
|
|
Temporarily increasing the <xref
|
|
linkend="guc-checkpoint-segments"> configuration variable can also
|
|
make large data loads faster. This is because loading a large
|
|
amount of data into <productname>PostgreSQL</productname> will
|
|
cause checkpoints to occur more often than the normal checkpoint
|
|
frequency (specified by the <varname>checkpoint_timeout</varname>
|
|
configuration variable). Whenever a checkpoint occurs, all dirty
|
|
pages must be flushed to disk. By increasing
|
|
<varname>checkpoint_segments</varname> temporarily during bulk
|
|
data loads, the number of checkpoints that are required can be
|
|
reduced.
|
|
</para>
|
|
</sect2>
|
|
|
|
<sect2 id="populate-pitr">
|
|
<title>Disable WAL Archival and Streaming Replication</title>
|
|
|
|
<para>
|
|
When loading large amounts of data into an installation that uses
|
|
WAL archiving or streaming replication, it might be faster to take a
|
|
new base backup after the load has completed than to process a large
|
|
amount of incremental WAL data. To prevent incremental WAL logging
|
|
while loading, disable archiving and streaming replication, by setting
|
|
<xref linkend="guc-wal-level"> to <literal>minimal</>,
|
|
<xref linkend="guc-archive-mode"> to <literal>off</>, and
|
|
<xref linkend="guc-max-wal-senders"> to zero.
|
|
But note that changing these settings requires a server restart.
|
|
</para>
|
|
|
|
<para>
|
|
Aside from avoiding the time for the archiver or WAL sender to
|
|
process the WAL data,
|
|
doing this will actually make certain commands faster, because they
|
|
are designed not to write WAL at all if <varname>wal_level</varname>
|
|
is <literal>minimal</>. (They can guarantee crash safety more cheaply
|
|
by doing an <function>fsync</> at the end than by writing WAL.)
|
|
This applies to the following commands:
|
|
<itemizedlist>
|
|
<listitem>
|
|
<para>
|
|
<command>CREATE TABLE AS SELECT</command>
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
<command>CREATE INDEX</command> (and variants such as
|
|
<command>ALTER TABLE ADD PRIMARY KEY</command>)
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
<command>ALTER TABLE SET TABLESPACE</command>
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
<command>CLUSTER</command>
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
<command>COPY FROM</command>, when the target table has been
|
|
created or truncated earlier in the same transaction
|
|
</para>
|
|
</listitem>
|
|
</itemizedlist>
|
|
</para>
|
|
</sect2>
|
|
|
|
<sect2 id="populate-analyze">
|
|
<title>Run <command>ANALYZE</command> Afterwards</title>
|
|
|
|
<para>
|
|
Whenever you have significantly altered the distribution of data
|
|
within a table, running <xref linkend="sql-analyze"> is strongly recommended. This
|
|
includes bulk loading large amounts of data into the table. Running
|
|
<command>ANALYZE</command> (or <command>VACUUM ANALYZE</command>)
|
|
ensures that the planner has up-to-date statistics about the
|
|
table. With no statistics or obsolete statistics, the planner might
|
|
make poor decisions during query planning, leading to poor
|
|
performance on any tables with inaccurate or nonexistent
|
|
statistics. Note that if the autovacuum daemon is enabled, it might
|
|
run <command>ANALYZE</command> automatically; see
|
|
<xref linkend="vacuum-for-statistics">
|
|
and <xref linkend="autovacuum"> for more information.
|
|
</para>
|
|
</sect2>
|
|
|
|
<sect2 id="populate-pg-dump">
|
|
<title>Some Notes About <application>pg_dump</></title>
|
|
|
|
<para>
|
|
Dump scripts generated by <application>pg_dump</> automatically apply
|
|
several, but not all, of the above guidelines. To reload a
|
|
<application>pg_dump</> dump as quickly as possible, you need to
|
|
do a few extra things manually. (Note that these points apply while
|
|
<emphasis>restoring</> a dump, not while <emphasis>creating</> it.
|
|
The same points apply whether loading a text dump with
|
|
<application>psql</> or using <application>pg_restore</> to load
|
|
from a <application>pg_dump</> archive file.)
|
|
</para>
|
|
|
|
<para>
|
|
By default, <application>pg_dump</> uses <command>COPY</>, and when
|
|
it is generating a complete schema-and-data dump, it is careful to
|
|
load data before creating indexes and foreign keys. So in this case
|
|
several guidelines are handled automatically. What is left
|
|
for you to do is to:
|
|
<itemizedlist>
|
|
<listitem>
|
|
<para>
|
|
Set appropriate (i.e., larger than normal) values for
|
|
<varname>maintenance_work_mem</varname> and
|
|
<varname>checkpoint_segments</varname>.
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
If using WAL archiving or streaming replication, consider disabling
|
|
them during the restore. To do that, set <varname>archive_mode</>
|
|
to <literal>off</>,
|
|
<varname>wal_level</varname> to <literal>minimal</>, and
|
|
<varname>max_wal_senders</> to zero before loading the dump.
|
|
Afterwards, set them back to the right values and take a fresh
|
|
base backup.
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
Consider whether the whole dump should be restored as a single
|
|
transaction. To do that, pass the <option>-1</> or
|
|
<option>--single-transaction</> command-line option to
|
|
<application>psql</> or <application>pg_restore</>. When using this
|
|
mode, even the smallest of errors will rollback the entire restore,
|
|
possibly discarding many hours of processing. Depending on how
|
|
interrelated the data is, that might seem preferable to manual cleanup,
|
|
or not. <command>COPY</> commands will run fastest if you use a single
|
|
transaction and have WAL archiving turned off.
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
If multiple CPUs are available in the database server, consider using
|
|
<application>pg_restore</>'s <option>--jobs</> option. This
|
|
allows concurrent data loading and index creation.
|
|
</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>
|
|
Run <command>ANALYZE</> afterwards.
|
|
</para>
|
|
</listitem>
|
|
</itemizedlist>
|
|
</para>
|
|
|
|
<para>
|
|
A data-only dump will still use <command>COPY</>, but it does not
|
|
drop or recreate indexes, and it does not normally touch foreign
|
|
keys.
|
|
|
|
<footnote>
|
|
<para>
|
|
You can get the effect of disabling foreign keys by using
|
|
the <option>--disable-triggers</> option — but realize that
|
|
that eliminates, rather than just postpones, foreign key
|
|
validation, and so it is possible to insert bad data if you use it.
|
|
</para>
|
|
</footnote>
|
|
|
|
So when loading a data-only dump, it is up to you to drop and recreate
|
|
indexes and foreign keys if you wish to use those techniques.
|
|
It's still useful to increase <varname>checkpoint_segments</varname>
|
|
while loading the data, but don't bother increasing
|
|
<varname>maintenance_work_mem</varname>; rather, you'd do that while
|
|
manually recreating indexes and foreign keys afterwards.
|
|
And don't forget to <command>ANALYZE</> when you're done; see
|
|
<xref linkend="vacuum-for-statistics">
|
|
and <xref linkend="autovacuum"> for more information.
|
|
</para>
|
|
</sect2>
|
|
</sect1>
|
|
|
|
<sect1 id="non-durability">
|
|
<title>Non-Durable Settings</title>
|
|
|
|
<indexterm zone="non-durability">
|
|
<primary>non-durable</primary>
|
|
</indexterm>
|
|
|
|
<para>
|
|
Durability is a database feature that guarantees the recording of
|
|
committed transactions even if the server crashes or loses
|
|
power. However, durability adds significant database overhead,
|
|
so if your site does not require such a guarantee,
|
|
<productname>PostgreSQL</productname> can be configured to run
|
|
much faster. The following are configuration changes you can make
|
|
to improve performance in such cases. Except as noted below, durability
|
|
is still guaranteed in case of a crash of the database software;
|
|
only abrupt operating system stoppage creates a risk of data loss
|
|
or corruption when these settings are used.
|
|
|
|
<itemizedlist>
|
|
<listitem>
|
|
<para>
|
|
Place the database cluster's data directory in a memory-backed
|
|
file system (i.e. <acronym>RAM</> disk). This eliminates all
|
|
database disk I/O, but limits data storage to the amount of
|
|
available memory (and perhaps swap).
|
|
</para>
|
|
</listitem>
|
|
|
|
<listitem>
|
|
<para>
|
|
Turn off <xref linkend="guc-fsync">; there is no need to flush
|
|
data to disk.
|
|
</para>
|
|
</listitem>
|
|
|
|
<listitem>
|
|
<para>
|
|
Turn off <xref linkend="guc-full-page-writes">; there is no need
|
|
to guard against partial page writes.
|
|
</para>
|
|
</listitem>
|
|
|
|
<listitem>
|
|
<para>
|
|
Increase <xref linkend="guc-checkpoint-segments"> and <xref
|
|
linkend="guc-checkpoint-timeout"> ; this reduces the frequency
|
|
of checkpoints, but increases the storage requirements of
|
|
<filename>/pg_xlog</>.
|
|
</para>
|
|
</listitem>
|
|
|
|
<listitem>
|
|
<para>
|
|
Turn off <xref linkend="guc-synchronous-commit">; there might be no
|
|
need to write the <acronym>WAL</acronym> to disk on every
|
|
commit. This setting does risk transaction loss (though not data
|
|
corruption) in case of a crash of the <emphasis>database</> alone.
|
|
</para>
|
|
</listitem>
|
|
</itemizedlist>
|
|
</para>
|
|
</sect1>
|
|
|
|
</chapter>
|