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Update and extend the EXPLAIN-related documentation.

Browse Source 
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.
This commit is contained in:
Tom Lane 2011-09-28 19:39:54 -04:00
parent cc4ff8742b
commit a32dd16459
5 changed files with 638 additions and 251 deletions
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8
doc/src/sgml/config.sgml
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@ -2533,8 +2533,8 @@ SET ENABLE_SEQSCAN TO OFF;
<para> <para>
Sets the planner's estimate of the cost of a disk page fetch Sets the planner's estimate of the cost of a disk page fetch
that is part of a series of sequential fetches. The default is 1.0. that is part of a series of sequential fetches. The default is 1.0.
This value can be overridden for a particular tablespace by setting This value can be overridden for tables and indexes in a particular
the tablespace parameter of the same name tablespace by setting the tablespace parameter of the same name
(see <xref linkend="sql-altertablespace">). (see <xref linkend="sql-altertablespace">).
</para> </para>
</listitem> </listitem>
@ -2549,8 +2549,8 @@ SET ENABLE_SEQSCAN TO OFF;
<para> <para>
Sets the planner's estimate of the cost of a Sets the planner's estimate of the cost of a
non-sequentially-fetched disk page. The default is 4.0. non-sequentially-fetched disk page. The default is 4.0.
This value can be overridden for a particular tablespace by setting This value can be overridden for tables and indexes in a particular
the tablespace parameter of the same name tablespace by setting the tablespace parameter of the same name
(see <xref linkend="sql-altertablespace">). (see <xref linkend="sql-altertablespace">).
</para> </para>

758
doc/src/sgml/perform.sgml
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@ -31,84 +31,100 @@
plan to match the query structure and the properties of the data plan to match the query structure and the properties of the data
is absolutely critical for good performance, so the system includes is absolutely critical for good performance, so the system includes
a complex <firstterm>planner</> that tries to choose good plans. a complex <firstterm>planner</> that tries to choose good plans.
You can use the You can use the <xref linkend="sql-explain"> command
<xref linkend="sql-explain"> command
to see what query plan the planner creates for any query. to see what query plan the planner creates for any query.
Plan-reading is an art that deserves an extensive tutorial, which Plan-reading is an art that requires some experience to master,
this is not; but here is some basic information. but this section attempts to cover the basics.
</para> </para>
<para>
Examples in this section are drawn from the regression test database
after doing a <command>VACUUM ANALYZE</>, using 9.2 development sources.
You should be able to get similar results if you try the examples
yourself, but your estimated costs and row counts might vary slightly
because <command>ANALYZE</>'s statistics are random samples rather
than exact, and because costs are inherently somewhat platform-dependent.
</para>
<para>
The examples use <command>EXPLAIN</>'s default <quote>text</> output
format, which is compact and convenient for humans to read.
If you want to feed <command>EXPLAIN</>'s output to a program for further
analysis, you should use one of its machine-readable output formats
(XML, JSON, or YAML) instead.
</para>
<sect2 id="using-explain-basics">
<title><command>EXPLAIN</command> Basics</title>
<para> <para>
The structure of a query plan is a tree of <firstterm>plan nodes</>. The structure of a query plan is a tree of <firstterm>plan nodes</>.
Nodes at the bottom level of the tree are table scan nodes: they return raw rows Nodes at the bottom level of the tree are scan nodes: they return raw rows
from a table. There are different types of scan nodes for different from a table. There are different types of scan nodes for different
table access methods: sequential scans, index scans, and bitmap index table access methods: sequential scans, index scans, and bitmap index
scans. If the query requires joining, aggregation, sorting, or other scans. There are also non-table row sources, such as <literal>VALUES</>
clauses and set-returning functions in <literal>FROM</>, which have their
own scan node types.
If the query requires joining, aggregation, sorting, or other
operations on the raw rows, then there will be additional nodes operations on the raw rows, then there will be additional nodes
above the scan nodes to perform these operations. Again, above the scan nodes to perform these operations. Again,
there is usually more than one possible way to do these operations, there is usually more than one possible way to do these operations,
so different node types can appear here too. The output so different node types can appear here too. The output
of <command>EXPLAIN</command> has one line for each node in the plan of <command>EXPLAIN</command> has one line for each node in the plan
tree, showing the basic node type plus the cost estimates that the planner tree, showing the basic node type plus the cost estimates that the planner
made for the execution of that plan node. The first line (topmost node) made for the execution of that plan node. Additional lines might appear,
has the estimated total execution cost for the plan; it is this number indented from the node's summary line,
that the planner seeks to minimize. to show additional properties of the node.
The very first line (the summary line for the topmost
node) has the estimated total execution cost for the plan; it is this
number that the planner seeks to minimize.
</para> </para>
<para> <para>
Here is a trivial example, just to show what the output looks like: Here is a trivial example, just to show what the output looks like:
<footnote>
<para>
Examples in this section are drawn from the regression test database
after doing a <command>VACUUM ANALYZE</>, using 8.2 development sources.
You should be able to get similar results if you try the examples yourself,
but your estimated costs and row counts might vary slightly
because <command>ANALYZE</>'s statistics are random samples rather
than exact.
</para>
</footnote>
<programlisting> <screen>
EXPLAIN SELECT * FROM tenk1; EXPLAIN SELECT * FROM tenk1;
QUERY PLAN QUERY PLAN
------------------------------------------------------------- -------------------------------------------------------------
Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244)
</programlisting> </screen>
</para> </para>
<para> <para>
The numbers that are quoted by <command>EXPLAIN</command> are (left Since this query has no <literal>WHERE</> clause, it must scan all the
rows of the table, so the planner has chosen to use a simple sequential
scan plan. The numbers that are quoted in parentheses are (left
to right): to right):
<itemizedlist> <itemizedlist>
<listitem> <listitem>
<para> <para>
Estimated start-up cost (time expended before the output scan can start, Estimated start-up cost. This is the time expended before the output
e.g., time to do the sorting in a sort node) phase can begin, e.g., time to do the sorting in a sort node.
</para> </para>
</listitem> </listitem>
<listitem> <listitem>
<para> <para>
Estimated total cost (if all rows are retrieved, though they might Estimated total cost. This is stated on the assumption that the plan
not be; e.g., a query with a <literal>LIMIT</> clause will stop node is run to completion, i.e., all available rows are retrieved.
short of paying the total cost of the <literal>Limit</> plan node's In practice a node's parent node might stop short of reading all
input node) available rows (see the <literal>LIMIT</> example below).
</para> </para>
</listitem> </listitem>
<listitem> <listitem>
<para> <para>
Estimated number of rows output by this plan node (again, only if Estimated number of rows output by this plan node. Again, the node
executed to completion) is assumed to be run to completion.
</para> </para>
</listitem> </listitem>
<listitem> <listitem>
<para> <para>
Estimated average width (in bytes) of rows output by this plan Estimated average width of rows output by this plan node (in bytes).
node
</para> </para>
</listitem> </listitem>
</itemizedlist> </itemizedlist>
@ -120,12 +136,12 @@ EXPLAIN SELECT * FROM tenk1;
Traditional practice is to measure the costs in units of disk page Traditional practice is to measure the costs in units of disk page
fetches; that is, <xref linkend="guc-seq-page-cost"> is conventionally fetches; that is, <xref linkend="guc-seq-page-cost"> is conventionally
set to <literal>1.0</> and the other cost parameters are set relative set to <literal>1.0</> and the other cost parameters are set relative
to that. (The examples in this section are run with the default cost to that. The examples in this section are run with the default cost
parameters.) parameters.
</para> </para>
<para> <para>
It's important to note that the cost of an upper-level node includes It's important to understand that the cost of an upper-level node includes
the cost of all its child nodes. It's also important to realize that the cost of all its child nodes. It's also important to realize that
the cost only reflects things that the planner cares about. the cost only reflects things that the planner cares about.
In particular, the cost does not consider the time spent transmitting In particular, the cost does not consider the time spent transmitting
@ -136,30 +152,29 @@ EXPLAIN SELECT * FROM tenk1;
</para> </para>
<para> <para>
The <literal>rows</> value is a little tricky The <literal>rows</> value is a little tricky because it is
because it is <emphasis>not</emphasis> the not the number of rows processed or scanned by the
number of rows processed or scanned by the plan node. It is usually less, plan node, but rather the number emitted by the node. This is often
reflecting the estimated selectivity of any <literal>WHERE</>-clause less than the number scanned, as a result of filtering by any
conditions that are being <literal>WHERE</>-clause conditions that are being applied at the node.
applied at the node. Ideally the top-level rows estimate will Ideally the top-level rows estimate will approximate the number of rows
approximate the number of rows actually returned, updated, or deleted actually returned, updated, or deleted by the query.
by the query.
</para> </para>
<para> <para>
Returning to our example: Returning to our example:
<programlisting> <screen>
EXPLAIN SELECT * FROM tenk1; EXPLAIN SELECT * FROM tenk1;
QUERY PLAN QUERY PLAN
------------------------------------------------------------- -------------------------------------------------------------
Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244)
</programlisting> </screen>
</para> </para>
<para> <para>
This is about as straightforward as it gets. If you do: These numbers are derived very straightforwardly. If you do:
<programlisting> <programlisting>
SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1'; SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';
@ -174,23 +189,24 @@ SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';
</para> </para>
<para> <para>
Now let's modify the original query to add a <literal>WHERE</> condition: Now let's modify the query to add a <literal>WHERE</> condition:
<programlisting> <screen>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 7000; EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 7000;
QUERY PLAN QUERY PLAN
------------------------------------------------------------ ------------------------------------------------------------
Seq Scan on tenk1 (cost=0.00..483.00 rows=7033 width=244) Seq Scan on tenk1 (cost=0.00..483.00 rows=7001 width=244)
Filter: (unique1 &lt; 7000) Filter: (unique1 &lt; 7000)
</programlisting> </screen>
Notice that the <command>EXPLAIN</> output shows the <literal>WHERE</> Notice that the <command>EXPLAIN</> output shows the <literal>WHERE</>
clause being applied as a <quote>filter</> condition; this means that clause being applied as a <quote>filter</> condition attached to the Seq
Scan plan node. This means that
the plan node checks the condition for each row it scans, and outputs the plan node checks the condition for each row it scans, and outputs
only the ones that pass the condition. only the ones that pass the condition.
The estimate of output rows has been reduced because of the <literal>WHERE</> The estimate of output rows has been reduced because of the
clause. <literal>WHERE</> clause.
However, the scan will still have to visit all 10000 rows, so the cost However, the scan will still have to visit all 10000 rows, so the cost
hasn't decreased; in fact it has gone up a bit (by 10000 * <xref hasn't decreased; in fact it has gone up a bit (by 10000 * <xref
linkend="guc-cpu-operator-cost">, to be exact) to reflect the extra CPU linkend="guc-cpu-operator-cost">, to be exact) to reflect the extra CPU
@ -200,7 +216,7 @@ EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 7000;
<para> <para>
The actual number of rows this query would select is 7000, but the <literal>rows</> The actual number of rows this query would select is 7000, but the <literal>rows</>
estimate is only approximate. If you try to duplicate this experiment, estimate is only approximate. If you try to duplicate this experiment,
you will probably get a slightly different estimate; moreover, it will you will probably get a slightly different estimate; moreover, it can
change after each <command>ANALYZE</command> command, because the change after each <command>ANALYZE</command> command, because the
statistics produced by <command>ANALYZE</command> are taken from a statistics produced by <command>ANALYZE</command> are taken from a
randomized sample of the table. randomized sample of the table.
@ -209,22 +225,22 @@ EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 7000;
<para> <para>
Now, let's make the condition more restrictive: Now, let's make the condition more restrictive:
<programlisting> <screen>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100; EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100;
QUERY PLAN QUERY PLAN
------------------------------------------------------------------------------ ------------------------------------------------------------------------------
Bitmap Heap Scan on tenk1 (cost=2.37..232.35 rows=106 width=244) Bitmap Heap Scan on tenk1 (cost=5.03..229.17 rows=101 width=244)
Recheck Cond: (unique1 &lt; 100) Recheck Cond: (unique1 &lt; 100)
-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0) -&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
Index Cond: (unique1 &lt; 100) Index Cond: (unique1 &lt; 100)
</programlisting> </screen>
Here the planner has decided to use a two-step plan: the bottom plan Here the planner has decided to use a two-step plan: the child plan
node visits an index to find the locations of rows matching the index node visits an index to find the locations of rows matching the index
condition, and then the upper plan node actually fetches those rows condition, and then the upper plan node actually fetches those rows
from the table itself. Fetching the rows separately is much more from the table itself. Fetching rows separately is much more
expensive than sequentially reading them, but because not all the pages expensive than reading them sequentially, but because not all the pages
of the table have to be visited, this is still cheaper than a sequential of the table have to be visited, this is still cheaper than a sequential
scan. (The reason for using two plan levels is that the upper plan scan. (The reason for using two plan levels is that the upper plan
node sorts the row locations identified by the index into physical order node sorts the row locations identified by the index into physical order
@ -234,65 +250,67 @@ EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100;
</para> </para>
<para> <para>
If the <literal>WHERE</> condition is selective enough, the planner might Now let's add another condition to the <literal>WHERE</> clause:
switch to a <quote>simple</> index scan plan:
<programlisting> <screen>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 3; EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND stringu1 = 'xxx';
QUERY PLAN QUERY PLAN
------------------------------------------------------------------------------ ------------------------------------------------------------------------------
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..10.00 rows=2 width=244) Bitmap Heap Scan on tenk1 (cost=5.01..229.40 rows=1 width=244)
Index Cond: (unique1 &lt; 3) Recheck Cond: (unique1 &lt; 100)
</programlisting>
In this case the table rows are fetched in index order, which makes them
even more expensive to read, but there are so few that the extra cost
of sorting the row locations is not worth it. You'll most often see
this plan type for queries that fetch just a single row, and for queries
that have an <literal>ORDER BY</> condition that matches the index
order.
</para>
<para>
Add another condition to the <literal>WHERE</> clause:
<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 3 AND stringu1 = 'xxx';
QUERY PLAN
------------------------------------------------------------------------------
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..10.01 rows=1 width=244)
Index Cond: (unique1 &lt; 3)
Filter: (stringu1 = 'xxx'::name) Filter: (stringu1 = 'xxx'::name)
</programlisting> -&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
Index Cond: (unique1 &lt; 100)
</screen>
The added condition <literal>stringu1 = 'xxx'</literal> reduces the The added condition <literal>stringu1 = 'xxx'</literal> reduces the
output-rows estimate, but not the cost because we still have to visit the output-rowcount estimate, but not the cost because we still have to visit
same set of rows. Notice that the <literal>stringu1</> clause the same set of rows. Notice that the <literal>stringu1</> clause
cannot be applied as an index condition (since this index is only on cannot be applied as an index condition, since this index is only on
the <literal>unique1</> column). Instead it is applied as a filter on the <literal>unique1</> column. Instead it is applied as a filter on
the rows retrieved by the index. Thus the cost has actually gone up the rows retrieved by the index. Thus the cost has actually gone up
slightly to reflect this extra checking. slightly to reflect this extra checking.
</para> </para>
<para> <para>
If there are indexes on several columns referenced in <literal>WHERE</>, the In some cases the planner will prefer a <quote>simple</> index scan plan:
planner might choose to use an AND or OR combination of the indexes:
<programlisting> <screen>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42;
QUERY PLAN
-----------------------------------------------------------------------------
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..8.27 rows=1 width=244)
Index Cond: (unique1 = 42)
</screen>
In this type of plan the table rows are fetched in index order, which
makes them even more expensive to read, but there are so few that the
extra cost of sorting the row locations is not worth it. You'll most
often see this plan type for queries that fetch just a single row. It's
also often used for queries that have an <literal>ORDER BY</> condition
that matches the index order, because then no extra sort step is needed to
satisfy the <literal>ORDER BY</>.
</para>
<para>
If there are indexes on several columns referenced in <literal>WHERE</>,
the planner might choose to use an AND or OR combination of the indexes:
<screen>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND unique2 &gt; 9000; EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND unique2 &gt; 9000;
QUERY PLAN QUERY PLAN
------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------
Bitmap Heap Scan on tenk1 (cost=11.27..49.11 rows=11 width=244) Bitmap Heap Scan on tenk1 (cost=25.01..60.14 rows=10 width=244)
Recheck Cond: ((unique1 &lt; 100) AND (unique2 &gt; 9000)) Recheck Cond: ((unique1 &lt; 100) AND (unique2 &gt; 9000))
-&gt; BitmapAnd (cost=11.27..11.27 rows=11 width=0) -&gt; BitmapAnd (cost=25.01..25.01 rows=10 width=0)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0) -&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
Index Cond: (unique1 &lt; 100) Index Cond: (unique1 &lt; 100)
-&gt; Bitmap Index Scan on tenk1_unique2 (cost=0.00..8.65 rows=1042 width=0) -&gt; Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.74 rows=999 width=0)
Index Cond: (unique2 &gt; 9000) Index Cond: (unique2 &gt; 9000)
</programlisting> </screen>
But this requires visiting both indexes, so it's not necessarily a win But this requires visiting both indexes, so it's not necessarily a win
compared to using just one index and treating the other condition as compared to using just one index and treating the other condition as
@ -301,51 +319,178 @@ EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND unique2 &gt; 9000;
</para> </para>
<para> <para>
Let's try joining two tables, using the columns we have been discussing: Here is an example showing the effects of <literal>LIMIT</>:
<programlisting> <screen>
EXPLAIN SELECT * EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND unique2 &gt; 9000 LIMIT 2;
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;
QUERY PLAN QUERY PLAN
-------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------
Nested Loop (cost=2.37..553.11 rows=106 width=488) Limit (cost=0.00..14.25 rows=2 width=244)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244) -&gt; Index Scan using tenk1_unique2 on tenk1 (cost=0.00..71.23 rows=10 width=244)
Recheck Cond: (unique1 &lt; 100) Index Cond: (unique2 &gt; 9000)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0) Filter: (unique1 &lt; 100)
Index Cond: (unique1 &lt; 100) </screen>
-&gt; Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..3.01 rows=1 width=244)
Index Cond: (unique2 = t1.unique2)
</programlisting>
</para> </para>
<para> <para>
In this nested-loop join, the outer (upper) scan is the same bitmap index scan we This is the same query as above, but we added a <literal>LIMIT</> so that
saw earlier, and so its cost and row count are the same because we are not all the rows need be retrieved, and the planner changed its mind about
applying the <literal>WHERE</> clause <literal>unique1 &lt; 100</literal> what to do. Notice that the total cost and row count of the Index Scan
node are shown as if it were run to completion. However, the Limit node
is expected to stop after retrieving only a fifth of those rows, so its
total cost is only a fifth as much, and that's the actual estimated cost
of the query. This plan is preferred over adding a Limit node to the
previous plan because the Limit could not avoid paying the startup cost
of the bitmap scan, so the total cost would be something over 25 units
with that approach.
</para>
<para>
Let's try joining two tables, using the columns we have been discussing:
<screen>
EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 10 AND t1.unique2 = t2.unique2;
QUERY PLAN
--------------------------------------------------------------------------------------
Nested Loop (cost=4.33..118.25 rows=10 width=488)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=4.33..39.44 rows=10 width=244)
Recheck Cond: (unique1 &lt; 10)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.33 rows=10 width=0)
Index Cond: (unique1 &lt; 10)
-&gt; Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..7.87 rows=1 width=244)
Index Cond: (unique2 = t1.unique2)
</screen>
</para>
<para>
In this plan, we have a nested-loop join node with two table scans as
inputs, or children. The indentation of the node summary lines reflects
the plan tree structure. The join's first, or <quote>outer</>, child
is a bitmap scan similar to those we saw before. Its cost and row count
are the same as we'd get from <literal>SELECT ... WHERE unique1 &lt; 10</>
because we are
applying the <literal>WHERE</> clause <literal>unique1 &lt; 10</literal>
at that node. at that node.
The <literal>t1.unique2 = t2.unique2</literal> clause is not relevant yet, The <literal>t1.unique2 = t2.unique2</literal> clause is not relevant yet,
so it doesn't affect the row count of the outer scan. For the inner (lower) scan, the so it doesn't affect the row count of the outer scan. The nested-loop
<literal>unique2</> value of the current outer-scan row is plugged into join node will run its second,
the inner index scan to produce an index condition like or <quote>inner</> child once for each row obtained from the outer child.
<literal>unique2 = <replaceable>constant</replaceable></literal>. Column values from the current outer row can be plugged into the inner
So we get the same inner-scan plan and costs that we'd get from, say, scan; here, the <literal>t1.unique2</> value from the outer row is available,
<literal>EXPLAIN SELECT * FROM tenk2 WHERE unique2 = 42</literal>. The so we get a plan and costs similar to what we saw above for a simple
<literal>SELECT ... WHERE t2.unique2 = <replaceable>constant</></> case.
(The estimated cost is actually a bit lower than what was seen above,
as a result of caching that's expected to occur during the repeated
indexscans on <literal>t2</>.) The
costs of the loop node are then set on the basis of the cost of the outer costs of the loop node are then set on the basis of the cost of the outer
scan, plus one repetition of the inner scan for each outer row (106 * 3.01, scan, plus one repetition of the inner scan for each outer row (10 * 7.87,
here), plus a little CPU time for join processing. here), plus a little CPU time for join processing.
</para> </para>
<para> <para>
In this example the join's output row count is the same as the product In this example the join's output row count is the same as the product
of the two scans' row counts, but that's not true in all cases because of the two scans' row counts, but that's not true in all cases because
you can have <literal>WHERE</> clauses that mention both tables there can be additional <literal>WHERE</> clauses that mention both tables
and so can only be applied at the join point, not to either input scan. and so can only be applied at the join point, not to either input scan.
For example, if we added For example, if we add one more condition:
<literal>WHERE ... AND t1.hundred &lt; t2.hundred</literal>,
that would decrease the output row count of the join node, but not change <screen>
either input scan. EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 10 AND t1.unique2 = t2.unique2 AND t1.hundred &lt; t2.hundred;
QUERY PLAN
--------------------------------------------------------------------------------------
Nested Loop (cost=4.33..118.28 rows=3 width=488)
Join Filter: (t1.hundred &lt; t2.hundred)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=4.33..39.44 rows=10 width=244)
Recheck Cond: (unique1 &lt; 10)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.33 rows=10 width=0)
Index Cond: (unique1 &lt; 10)
-&gt; Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..7.87 rows=1 width=244)
Index Cond: (unique2 = t1.unique2)
</screen>
The extra condition <literal>t1.hundred &lt; t2.hundred</literal> can't be
tested in the <literal>tenk2_unique2</> index, so it's applied at the
join node. This reduces the estimated output row count of the join node,
but does not change either input scan.
</para>
<para>
When dealing with outer joins, you might see join plan nodes with both
<quote>Join Filter</> and plain <quote>Filter</> conditions attached.
Join Filter conditions come from the outer join's <literal>ON</> clause,
so a row that fails the Join Filter condition could still get emitted as
a null-extended row. But a plain Filter condition is applied after the
outer-join rules and so acts to remove rows unconditionally. In an inner
join there is no semantic difference between these types of filters.
</para>
<para>
If we change the query's selectivity a bit, we might get a very different
join plan:
<screen>
EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;
QUERY PLAN
------------------------------------------------------------------------------------------
Hash Join (cost=230.43..713.94 rows=101 width=488)
Hash Cond: (t2.unique2 = t1.unique2)
-&gt; Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244)
-&gt; Hash (cost=229.17..229.17 rows=101 width=244)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=5.03..229.17 rows=101 width=244)
Recheck Cond: (unique1 &lt; 100)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.01 rows=101 width=0)
Index Cond: (unique1 &lt; 100)
</screen>
</para>
<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 &lt; 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)
-&gt; Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.00..656.25 rows=101 width=244)
Filter: (unique1 &lt; 100)
-&gt; Sort (cost=197.83..200.33 rows=1000 width=244)
Sort Key: t2.unique2
-&gt; 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>
<para> <para>
@ -354,111 +499,283 @@ WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;
flags described in <xref linkend="runtime-config-query-enable">. flags described in <xref linkend="runtime-config-query-enable">.
(This is a crude tool, but useful. See (This is a crude tool, but useful. See
also <xref linkend="explicit-joins">.) 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;
<programlisting>
SET enable_nestloop = off;
EXPLAIN SELECT * EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2 FROM tenk1 t1, onek t2
WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2; WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;
QUERY PLAN QUERY PLAN
------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------
Hash Join (cost=232.61..741.67 rows=106 width=488) Merge Join (cost=0.00..292.36 rows=10 width=488)
Hash Cond: (t2.unique2 = t1.unique2) Merge Cond: (t1.unique2 = t2.unique2)
-&gt; Seq Scan on tenk2 t2 (cost=0.00..458.00 rows=10000 width=244) -&gt; Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.00..656.25 rows=101 width=244)
-&gt; Hash (cost=232.35..232.35 rows=106 width=244) Filter: (unique1 &lt; 100)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244) -&gt; Index Scan using onek_unique2 on onek t2 (cost=0.00..224.76 rows=1000 width=244)
Recheck Cond: (unique1 &lt; 100) </screen>
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
Index Cond: (unique1 &lt; 100)
</programlisting>
This plan proposes to extract the 100 interesting rows of <classname>tenk1</classname> which shows that the planner thinks that sorting <literal>onek</> by
using that same old index scan, stash them into an in-memory hash table, indexscanning is about 12% more expensive than seqscan-and-sort.
and then do a sequential scan of <classname>tenk2</classname>, probing into the hash table Of course, the next question is whether it's right about that.
for possible matches of <literal>t1.unique2 = t2.unique2</literal> for each <classname>tenk2</classname> row. We can investigate that using <command>EXPLAIN ANALYZE</>, as discussed
The cost to read <classname>tenk1</classname> and set up the hash table is a start-up below.
cost for the hash join, since there will be no output until we can
start reading <classname>tenk2</classname>. The total time estimate for the join also
includes a hefty charge for the CPU time to probe the hash table
10000 times. Note, however, that we are <emphasis>not</emphasis> charging 10000 times 232.35;
the hash table setup is only done once in this plan type.
</para> </para>
</sect2>
<sect2 id="using-explain-analyze">
<title><command>EXPLAIN ANALYZE</command></title>
<para> <para>
It is possible to check the accuracy of the planner's estimated costs It is possible to check the accuracy of the planner's estimates
by using <command>EXPLAIN ANALYZE</>. This command actually executes the query, by using <command>EXPLAIN</>'s <literal>ANALYZE</> option. With this
and then displays the true run time accumulated within each plan node option, <command>EXPLAIN</> actually executes the query, and then displays
along with the same estimated costs that a plain <command>EXPLAIN</command> shows. the true row counts and true run time accumulated within each plan node,
For example, we might get a result like this: along with the same estimates that a plain <command>EXPLAIN</command>
shows. For example, we might get a result like this:
<screen> <screen>
EXPLAIN ANALYZE SELECT * EXPLAIN ANALYZE SELECT *
FROM tenk1 t1, tenk2 t2 FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2; WHERE t1.unique1 &lt; 10 AND t1.unique2 = t2.unique2;
QUERY PLAN QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=2.37..553.11 rows=106 width=488) (actual time=1.392..12.700 rows=100 loops=1) Nested Loop (cost=4.33..118.25 rows=10 width=488) (actual time=0.370..1.126 rows=10 loops=1)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244) (actual time=0.878..2.367 rows=100 loops=1) -&gt; 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 &lt; 100) Recheck Cond: (unique1 &lt; 10)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0) (actual time=0.546..0.546 rows=100 loops=1) -&gt; 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 &lt; 100) Index Cond: (unique1 &lt; 10)
-&gt; Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..3.01 rows=1 width=244) (actual time=0.067..0.078 rows=1 loops=100) -&gt; 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) Index Cond: (unique2 = t1.unique2)
Total runtime: 14.452 ms Total runtime: 2.414 ms
</screen> </screen>
Note that the <quote>actual time</quote> values are in milliseconds of Note that the <quote>actual time</quote> values are in milliseconds of
real time, whereas the <literal>cost</> estimates are expressed in real time, whereas the <literal>cost</> estimates are expressed in
arbitrary units; so they are unlikely to match up. arbitrary units; so they are unlikely to match up.
The thing to pay attention to is whether the ratios of actual time and The thing that's usually most important to look for is whether the
estimated costs are consistent. 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>
<para> <para>
In some query plans, it is possible for a subplan node to be executed more In some query plans, it is possible for a subplan node to be executed more
than once. For example, the inner index scan is executed once per outer than once. For example, the inner index scan will be executed once per
row in the above nested-loop plan. In such cases, the outer row in the above nested-loop plan. In such cases, the
<literal>loops</> value reports the <literal>loops</> value reports the
total number of executions of the node, and the actual time and rows 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 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 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 <literal>loops</> value to get the total time actually spent in
the node. 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 &lt; 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
-&gt; 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)
-&gt; Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) (actual time=0.046..46.219 rows=10000 loops=1)
-&gt; 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
-&gt; 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 &lt; 100)
-&gt; 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 &lt; 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 &lt; 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 &lt; 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 @&gt; 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 @&gt; '((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 @&gt; 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 @&gt; '((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 &lt; 100 AND unique2 &gt; 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 &lt; 100) AND (unique2 &gt; 9000))
Buffers: shared hit=16
-&gt; BitmapAnd (cost=25.07..25.07 rows=10 width=0) (actual time=2.967..2.967 rows=0 loops=1)
Buffers: shared hit=7
-&gt; 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 &lt; 100)
Buffers: shared hit=2
-&gt; 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 &gt; 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 &lt; 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)
-&gt; 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 &lt; 100)
-&gt; 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 &lt; 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>
<para> <para>
The <literal>Total runtime</literal> shown by <command>EXPLAIN The <literal>Total runtime</literal> shown by <command>EXPLAIN
ANALYZE</command> includes executor start-up and shut-down time, but not ANALYZE</command> includes executor start-up and shut-down time, as well
parsing, rewriting, or planning time. For <command>INSERT</>, as the time to run any triggers that are fired, but it does not include
<command>UPDATE</>, and <command>DELETE</> commands, the time spent parsing, rewriting, or planning time.
applying the table changes is charged to a top-level Insert, Update, Time spent executing <literal>BEFORE</> triggers, if any, is included in
or Delete plan node. (The plan nodes underneath this node represent the time for the related Insert, Update, or Delete node; but time
the work of locating the old rows and/or computing the new ones.) spent executing <literal>AFTER</> triggers is not counted there because
Time spent executing <literal>BEFORE</> triggers, if any, is charged to <literal>AFTER</> triggers are fired after completion of the whole plan.
the related Insert, Update, or Delete node, although time spent executing The total time spent in each trigger
<literal>AFTER</> triggers is not. The time spent in each trigger (either <literal>BEFORE</> or <literal>AFTER</>) is also shown separately.
(either <literal>BEFORE</> or <literal>AFTER</>) is also shown separately Note that deferred constraint triggers will not be executed
and is included in total run time. until end of transaction and are thus not shown at all by
Note, however, that deferred constraint triggers will not be executed
until end of transaction and are thus not shown by
<command>EXPLAIN ANALYZE</command>. <command>EXPLAIN ANALYZE</command>.
</para> </para>
</sect2>
<sect2 id="using-explain-caveats">
<title>Caveats</title>
<para> <para>
There are two significant ways in which run times measured by There are two significant ways in which run times measured by
<command>EXPLAIN ANALYZE</command> can deviate from normal execution of <command>EXPLAIN ANALYZE</command> can deviate from normal execution of
the same query. First, since no output rows are delivered to the client, the same query. First, since no output rows are delivered to the client,
network transmission costs and I/O formatting costs are not included. network transmission costs and I/O conversion costs are not included.
Second, the overhead added by <command>EXPLAIN ANALYZE</command> can be Second, the measurement overhead added by <command>EXPLAIN
significant, especially on machines with slow <function>gettimeofday()</> ANALYZE</command> can be significant, especially on machines with slow
kernel calls. <function>gettimeofday()</> operating-system calls.
</para> </para>
<para> <para>
It is worth noting that <command>EXPLAIN</> results should not be extrapolated <command>EXPLAIN</> results should not be extrapolated to situations
to situations other than the one you are actually testing; for example, 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. 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 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 a different plan for a larger or smaller table. An extreme example
@ -466,8 +783,59 @@ WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;
always get a sequential scan plan whether indexes are available or not. 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 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 process the table in any case, so there's no value in expending additional
page reads to look at an index. page reads to look at an index. (We saw this happening in the
<literal>polygon_tbl</> example above.)
</para> </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 &lt; 100 AND unique2 &gt; 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)
-&gt; 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 &gt; 9000)
Filter: (unique1 &lt; 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>
<sect1 id="planner-stats"> <sect1 id="planner-stats">
@ -519,10 +887,12 @@ WHERE relname LIKE 'tenk1%';
and <structfield>relpages</structfield> are not updated on-the-fly, and <structfield>relpages</structfield> are not updated on-the-fly,
and so they usually contain somewhat out-of-date values. and so they usually contain somewhat out-of-date values.
They are updated by <command>VACUUM</>, <command>ANALYZE</>, and a They are updated by <command>VACUUM</>, <command>ANALYZE</>, and a
few DDL commands such as <command>CREATE INDEX</>. A stand-alone few DDL commands such as <command>CREATE INDEX</>. A <command>VACUUM</>
<command>ANALYZE</>, that is one not part of <command>VACUUM</>, or <command>ANALYZE</> operation that does not scan the entire table
generates an approximate <structfield>reltuples</structfield> value (which is commonly the case) will incrementally update the
since it does not read every row of the table. The planner <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> will scale the values it finds in <structname>pg_class</structname>
to match the current physical table size, thus obtaining a closer to match the current physical table size, thus obtaining a closer
approximation. approximation.

6
doc/src/sgml/planstats.sgml
View File

@ -67,9 +67,9 @@ SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';
not requiring a table scan). If that is different from not requiring a table scan). If that is different from
<structfield>relpages</structfield> then <structfield>relpages</structfield> then
<structfield>reltuples</structfield> is scaled accordingly to <structfield>reltuples</structfield> is scaled accordingly to
arrive at a current number-of-rows estimate. In this case the values arrive at a current number-of-rows estimate. In this case the value of
are correct so the rows estimate is the same as <structfield>relpages</structfield> is up-to-date so the rows estimate is
<structfield>reltuples</structfield>. the same as <structfield>reltuples</structfield>.
</para> </para>
<para> <para>

15
doc/src/sgml/ref/analyze.sgml
View File

@ -102,8 +102,9 @@ ANALYZE [ VERBOSE ] [ <replaceable class="PARAMETER">table</replaceable> [ ( <re
just after making major changes in the contents of a table. Accurate just after making major changes in the contents of a table. Accurate
statistics will help the planner to choose the most appropriate query statistics will help the planner to choose the most appropriate query
plan, and thereby improve the speed of query processing. A common plan, and thereby improve the speed of query processing. A common
strategy is to run <xref linkend="sql-vacuum"> strategy for read-mostly databases is to run <xref linkend="sql-vacuum">
and <command>ANALYZE</command> once a day during a low-usage time of day. and <command>ANALYZE</command> once a day during a low-usage time of day.
(This will not be sufficient if there is heavy update activity.)
</para> </para>
<para> <para>
@ -181,11 +182,13 @@ ANALYZE [ VERBOSE ] [ <replaceable class="PARAMETER">table</replaceable> [ ( <re
If the table being analyzed has one or more children, If the table being analyzed has one or more children,
<command>ANALYZE</command> will gather statistics twice: once on the <command>ANALYZE</command> will gather statistics twice: once on the
rows of the parent table only, and a second time on the rows of the rows of the parent table only, and a second time on the rows of the
parent table with all of its children. The autovacuum daemon, however, parent table with all of its children. This second set of statistics
will only consider inserts or updates on the parent table when deciding is needed when planning queries that traverse the entire inheritance
whether to trigger an automatic analyze. If that table is rarely tree. The autovacuum daemon, however, will only consider inserts or
inserted into or updated, the inheritance statistics will not be up to date updates on the parent table itself when deciding whether to trigger an
unless you run <command>ANALYZE</command> manually. automatic analyze for that table. If that table is rarely inserted into
or updated, the inheritance statistics will not be up to date unless you
run <command>ANALYZE</command> manually.
</para> </para>
</refsect1> </refsect1>

80
doc/src/sgml/ref/explain.sgml
View File

@ -60,11 +60,12 @@ EXPLAIN [ ANALYZE ] [ VERBOSE ] <replaceable class="parameter">statement</replac
<para> <para>
The most critical part of the display is the estimated statement execution The most critical part of the display is the estimated statement execution
cost, which is the planner's guess at how long it will take to run the cost, which is the planner's guess at how long it will take to run the
statement (measured in units of disk page fetches). Actually two numbers statement (measured in cost units that are arbitrary, but conventionally
are shown: the start-up time before the first row can be returned, and mean disk page fetches). Actually two numbers
the total time to return all the rows. For most queries the total time are shown: the start-up cost before the first row can be returned, and
the total cost to return all the rows. For most queries the total cost
is what matters, but in contexts such as a subquery in <literal>EXISTS</literal>, the planner is what matters, but in contexts such as a subquery in <literal>EXISTS</literal>, the planner
will choose the smallest start-up time instead of the smallest total time will choose the smallest start-up cost instead of the smallest total cost
(since the executor will stop after getting one row, anyway). (since the executor will stop after getting one row, anyway).
Also, if you limit the number of rows to return with a <literal>LIMIT</literal> clause, Also, if you limit the number of rows to return with a <literal>LIMIT</literal> clause,
the planner makes an appropriate interpolation between the endpoint the planner makes an appropriate interpolation between the endpoint
@ -72,10 +73,11 @@ EXPLAIN [ ANALYZE ] [ VERBOSE ] <replaceable class="parameter">statement</replac
</para> </para>
<para> <para>
The <literal>ANALYZE</literal> option causes the statement to be actually executed, not only The <literal>ANALYZE</literal> option causes the statement to be actually
planned. The total elapsed time expended within each plan node (in executed, not only planned. Then actual runtime statistics are added to
milliseconds) and total number of rows it actually returned are added to the display, including the total elapsed time expended within each plan
the display. This is useful for seeing whether the planner's estimates node (in milliseconds) and the total number of rows it actually returned.
This is useful for seeing whether the planner's estimates
are close to reality. are close to reality.
</para> </para>
@ -116,8 +118,8 @@ ROLLBACK;
<term><literal>ANALYZE</literal></term> <term><literal>ANALYZE</literal></term>
<listitem> <listitem>
<para> <para>
Carry out the command and show the actual run times. This Carry out the command and show actual run times and other statistics.
parameter defaults to <literal>FALSE</literal>. This parameter defaults to <literal>FALSE</literal>.
</para> </para>
</listitem> </listitem>
</varlistentry> </varlistentry>
@ -154,12 +156,16 @@ ROLLBACK;
Include information on buffer usage. Specifically, include the number of Include information on buffer usage. Specifically, include the number of
shared blocks hits, reads, and writes, the number of local blocks hits, shared blocks hits, reads, and writes, the number of local blocks hits,
reads, and writes, and the number of temp blocks reads and writes. reads, and writes, and the number of temp blocks reads and writes.
Shared blocks, local blocks, and temp blocks contain tables and indexes, A <quote>hit</> means that a read was avoided because the block was
temporary tables and temporary indexes, and disk blocks used in sort and found already in cache when needed.
materialized plans, respectively. The number of blocks shown for an Shared blocks contain data from regular tables and indexes;
local blocks contain data from temporary tables and indexes;
while temp blocks contain short-term working data used in sorts, hashes,
Materialize plan nodes, and similar cases.
The number of blocks shown for an
upper-level node includes those used by all its child nodes. In text upper-level node includes those used by all its child nodes. In text
format, only non-zero values are printed. This parameter may only be format, only non-zero values are printed. This parameter may only be
used with <literal>ANALYZE</literal> parameter. It defaults to used when <literal>ANALYZE</literal> is also enabled. It defaults to
<literal>FALSE</literal>. <literal>FALSE</literal>.
</para> </para>
</listitem> </listitem>
@ -206,35 +212,43 @@ ROLLBACK;
</refsect1> </refsect1>
<refsect1> <refsect1>
<title>Notes</title> <title>Outputs</title>
<para> <para>
There is only sparse documentation on the optimizer's use of cost The command's result is a textual description of the plan selected
information in <productname>PostgreSQL</productname>. Refer to for the <replaceable class="parameter">statement</replaceable>,
<xref linkend="using-explain"> for more information. optionally annotated with execution statistics.
<xref linkend="using-explain"> describes the information provided.
</para> </para>
</refsect1>
<refsect1>
<title>Notes</title>
<para> <para>
In order to allow the <productname>PostgreSQL</productname> query In order to allow the <productname>PostgreSQL</productname> query
planner to make reasonably informed decisions when optimizing planner to make reasonably informed decisions when optimizing
queries, the <xref linkend="sql-analyze"> queries, the <link
statement should be run to record statistics about the distribution linkend="catalog-pg-statistic"><structname>pg_statistic</structname></link>
of data within the table. If you have not done this (or if the data should be up-to-date for all tables used in the query. Normally
statistical distribution of the data in the table has changed the <link linkend="autovacuum">autovacuum daemon</link> will take care
significantly since the last time <command>ANALYZE</command> was of that automatically. But if a table has recently had substantial
run), the estimated costs are unlikely to conform to the real changes in its contents, you might need to do a manual
properties of the query, and consequently an inferior query plan <xref linkend="sql-analyze"> rather than wait for autovacuum to catch up
might be chosen. with the changes.
</para> </para>
<para> <para>
In order to measure the run-time cost of each node in the execution In order to measure the run-time cost of each node in the execution
plan, the current implementation of <command>EXPLAIN plan, the current implementation of <command>EXPLAIN
ANALYZE</command> can add considerable profiling overhead to query ANALYZE</command> adds profiling overhead to query execution.
execution. As a result, running <command>EXPLAIN ANALYZE</command> As a result, running <command>EXPLAIN ANALYZE</command>
on a query can sometimes take significantly longer than executing on a query can sometimes take significantly longer than executing
the query normally. The amount of overhead depends on the nature of the query normally. The amount of overhead depends on the nature of
the query. the query, as well as the platform being used. The worst case occurs
for plan nodes that in themselves require very little time per
execution, and on machines that have relatively slow operating
system calls for obtaining the time of day.
</para> </para>
</refsect1> </refsect1>
@ -256,7 +270,7 @@ EXPLAIN SELECT * FROM foo;
</para> </para>
<para> <para>
Here is the same query, with JSON formatting: Here is the same query, with JSON output formatting:
<programlisting> <programlisting>
EXPLAIN (FORMAT JSON) SELECT * FROM foo; EXPLAIN (FORMAT JSON) SELECT * FROM foo;
QUERY PLAN QUERY PLAN
@ -295,7 +309,7 @@ EXPLAIN SELECT * FROM foo WHERE i = 4;
</para> </para>
<para> <para>
Here is the same query, but in YAML output: Here is the same query, but in YAML format:
<programlisting> <programlisting>
EXPLAIN (FORMAT YAML) SELECT * FROM foo WHERE i='4'; EXPLAIN (FORMAT YAML) SELECT * FROM foo WHERE i='4';
QUERY PLAN QUERY PLAN
@ -314,10 +328,10 @@ EXPLAIN (FORMAT YAML) SELECT * FROM foo WHERE i='4';
(1 row) (1 row)
</programlisting> </programlisting>
XML output is left as an exercise to the reader. XML format is left as an exercise for the reader.
</para> </para>
<para> <para>
Here is the same plan with costs suppressed: Here is the same plan with cost estimates suppressed:
<programlisting> <programlisting>
EXPLAIN (COSTS FALSE) SELECT * FROM foo WHERE i = 4; EXPLAIN (COSTS FALSE) SELECT * FROM foo WHERE i = 4;