mirrors/postgres
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
postgres/doc/src/sgml/perform.sgml
Tom Lane fba8113c1b Teach CLUSTER to skip writing WAL if not needed (ie, not using archiving) 
--- Simon.
Also, code review and cleanup for the previous COPY-no-WAL patches --- Tom.
2007-03-29 00:15:39 +00:00

1044 lines
45 KiB
Plaintext
Raw Blame History

<!-- $PostgreSQL: pgsql/doc/src/sgml/perform.sgml,v 1.64 2007/03/29 00:15:36 tgl Exp $ -->
<chapter id="performance-tips">
<title>Performance Tips</title>
<indexterm zone="performance-tips">
<primary>performance</primary>
</indexterm>
<para>
Query performance can be affected by many things. Some of these can
be manipulated by the user, while others are fundamental to the underlying
design of the system. This chapter provides some hints about understanding
and tuning <productname>PostgreSQL</productname> performance.
</para>
<sect1 id="using-explain">
<title>Using <command>EXPLAIN</command></title>
<indexterm zone="using-explain">
<primary>EXPLAIN</primary>
</indexterm>
<indexterm zone="using-explain">
<primary>query plan</primary>
</indexterm>
<para>
<productname>PostgreSQL</productname> devises a <firstterm>query
plan</firstterm> for each query it is given. Choosing the right
plan to match the query structure and the properties of the data
is absolutely critical for good performance, so the system includes
a complex <firstterm>planner</> that tries to select good plans.
You can use the
<xref linkend="sql-explain" endterm="sql-explain-title"> command
to see what query plan the planner creates for any query.
Plan-reading is an art that deserves an extensive tutorial, which
this is not; but here is some basic information.
</para>
<para>
The structure of a query plan is a tree of <firstterm>plan nodes</>.
Nodes at the bottom level are table scan nodes: they return raw rows
from a table. There are different types of scan nodes for different
table access methods: sequential scans, index scans, and bitmap index
scans. If the query requires joining, aggregation, sorting, or other
operations on the raw rows, then there will be additional nodes
<quote>atop</> the scan nodes to perform these operations. Again,
there is usually more than one possible way to do these operations,
so different node types can appear here too. The output
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
made for the execution of that plan node. The first line (topmost node)
has the estimated total execution cost for the plan; it is this number
that the planner seeks to minimize.
</para>
<para>
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 will probably vary slightly
because <command>ANALYZE</>'s statistics are random samples rather
than being exact.
</para>
</footnote>
<programlisting>
EXPLAIN SELECT * FROM tenk1;
QUERY PLAN
-------------------------------------------------------------
Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244)
</programlisting>
</para>
<para>
The numbers that are quoted by <command>EXPLAIN</command> are:
<itemizedlist>
<listitem>
<para>
Estimated start-up cost (Time expended before output scan can start,
e.g., time to do the sorting in a sort node.)
</para>
</listitem>
<listitem>
<para>
Estimated total cost (If all rows were to be retrieved, though they might
not be: for example, a query with a <literal>LIMIT</> clause will stop
short of paying the total cost of the <literal>Limit</> plan node's
input node.)
</para>
</listitem>
<listitem>
<para>
Estimated number of rows output by this plan node (Again, only if
executed to completion.)
</para>
</listitem>
<listitem>
<para>
Estimated average width (in bytes) of rows output by this plan
node
</para>
</listitem>
</itemizedlist>
</para>
<para>
The costs are measured in arbitrary units determined by the planner's
cost parameters (see <xref linkend="runtime-config-query-constants">).
Traditional practice is to measure the costs in units of disk page
fetches; that is, <xref linkend="guc-seq-page-cost"> is conventionally
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
parameters.
</para>
<para>
It's important to note 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 only reflects things that the planner cares about.
In particular, the cost does not consider the time spent transmitting
result rows to the client, which could be an important
factor in the true elapsed time; but the planner ignores it because
it cannot change it by altering the plan. (Every correct plan will
output the same row set, we trust.)
</para>
<para>
Rows output is a little tricky because it is <emphasis>not</emphasis> the
number of rows processed or scanned by the plan node. It is usually less,
reflecting the estimated selectivity of any <literal>WHERE</>-clause
conditions that are being
applied at the node. Ideally the top-level rows estimate will
approximate the number of rows actually returned, updated, or deleted
by the query.
</para>
<para>
Returning to our example:
<programlisting>
EXPLAIN SELECT * FROM tenk1;
QUERY PLAN
-------------------------------------------------------------
Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244)
</programlisting>
</para>
<para>
This is about as straightforward as it gets. If you do:
<programlisting>
SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';
</programlisting>
you will find out that <classname>tenk1</classname> has 358 disk
pages and 10000 rows. So the cost is estimated at 358 page
reads, costing <xref linkend="guc-seq-page-cost"> apiece (1.0 by
default), plus 10000 * <xref linkend="guc-cpu-tuple-cost"> which is
0.01 by default.
</para>
<para>
Now let's modify the query to add a <literal>WHERE</> condition:
<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 7000;
QUERY PLAN
------------------------------------------------------------
Seq Scan on tenk1 (cost=0.00..483.00 rows=7033 width=244)
Filter: (unique1 &lt; 7000)
</programlisting>
Notice that the <command>EXPLAIN</> output shows the <literal>WHERE</>
clause being applied as a <quote>filter</> condition; this means that
the plan node checks the condition for each row it scans, and outputs
only the ones that pass the condition.
The estimate of output rows has gone down because of the <literal>WHERE</>
clause.
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 to reflect the extra CPU
time spent checking the <literal>WHERE</> condition.
</para>
<para>
The actual number of rows this query would select is 7000, but the rows
estimate is only approximate. If you try to duplicate this experiment,
you will probably get a slightly different estimate; moreover, it will
change after each <command>ANALYZE</command> command, because the
statistics produced by <command>ANALYZE</command> are taken from a
randomized sample of the table.
</para>
<para>
Now, let's make the condition more restrictive:
<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100;
QUERY PLAN
------------------------------------------------------------------------------
Bitmap Heap Scan on tenk1 (cost=2.37..232.35 rows=106 width=244)
Recheck Cond: (unique1 &lt; 100)
-> Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
Index Cond: (unique1 &lt; 100)
</programlisting>
Here the planner has decided to use a two-step plan: the bottom plan
node visits an index to find the locations of rows matching the index
condition, and then the upper plan node actually fetches those rows
from the table itself. Fetching the rows separately is much more
expensive than sequentially reading them, but because not all the pages
of the table have to be visited, this is still cheaper than a sequential
scan. (The reason for using two levels of plan is that the upper plan
node sorts the row locations identified by the index into physical order
before reading them, so as to minimize the costs of the separate fetches.
The <quote>bitmap</> mentioned in the node names is the mechanism that
does the sorting.)
</para>
<para>
If the <literal>WHERE</> condition is selective enough, the planner might
switch to a <quote>simple</> index scan plan:
<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 3;
QUERY PLAN
------------------------------------------------------------------------------
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..10.00 rows=2 width=244)
Index Cond: (unique1 &lt; 3)
</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 request 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)
</programlisting>
The added condition <literal>stringu1 = 'xxx'</literal> reduces the
output-rows estimate, but not the cost because we still have to visit the
same set of rows. Notice that the <literal>stringu1</> clause
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 rows retrieved by the index. Thus the cost has actually gone up
a little bit to reflect this extra checking.
</para>
<para>
If there are indexes on several columns used in <literal>WHERE</>, the
planner might choose to use an AND or OR combination of the indexes:
<programlisting>
EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND unique2 &gt; 9000;
QUERY PLAN
-------------------------------------------------------------------------------------
Bitmap Heap Scan on tenk1 (cost=11.27..49.11 rows=11 width=244)
Recheck Cond: ((unique1 &lt; 100) AND (unique2 &gt; 9000))
-&gt; BitmapAnd (cost=11.27..11.27 rows=11 width=0)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
Index Cond: (unique1 &lt; 100)
-&gt; Bitmap Index Scan on tenk1_unique2 (cost=0.00..8.65 rows=1042 width=0)
Index Cond: (unique2 &gt; 9000)
</programlisting>
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
a filter. If you vary the ranges involved you'll see the plan change
accordingly.
</para>
<para>
Let's try joining two tables, using the columns we have been discussing:
<programlisting>
EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;
QUERY PLAN
--------------------------------------------------------------------------------------
Nested Loop (cost=2.37..553.11 rows=106 width=488)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244)
Recheck Cond: (unique1 &lt; 100)
-&gt; Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
Index Cond: (unique1 &lt; 100)
-&gt; Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..3.01 rows=1 width=244)
Index Cond: ("outer".unique2 = t2.unique2)
</programlisting>
</para>
<para>
In this nested-loop join, the outer scan is the same bitmap index scan we
saw earlier, and so its cost and row count are the same because we are
applying the <literal>WHERE</> clause <literal>unique1 &lt; 100</literal>
at that node.
The <literal>t1.unique2 = t2.unique2</literal> clause is not relevant yet,
so it doesn't affect row count of the outer scan. For the inner scan, the
<literal>unique2</> value of the current outer-scan row is plugged into
the inner index scan to produce an index condition like
<literal>t2.unique2 = <replaceable>constant</replaceable></literal>.
So we get the same inner-scan plan and costs that we'd get from, say,
<literal>EXPLAIN SELECT * FROM tenk2 WHERE unique2 = 42</literal>. The
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,
here), plus a little CPU time for join processing.
</para>
<para>
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 general, because
in general you can have <literal>WHERE</> clauses that mention both tables
and so can only be applied at the join point, not to either input scan.
For example, if we added
<literal>WHERE ... AND t1.hundred &lt; t2.hundred</literal>,
that would decrease the output row count of the join node, but not change
either input scan.
</para>
<para>
One way to look at variant plans is to force the planner to disregard
whatever strategy it thought was the winner, 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">.)
<programlisting>
SET enable_nestloop = off;
EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;
QUERY PLAN
------------------------------------------------------------------------------------------
Hash Join (cost=232.61..741.67 rows=106 width=488)
Hash Cond: ("outer".unique2 = "inner".unique2)
-&gt; Seq Scan on tenk2 t2 (cost=0.00..458.00 rows=10000 width=244)
-&gt; Hash (cost=232.35..232.35 rows=106 width=244)
-&gt; Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244)
Recheck Cond: (unique1 &lt; 100)
-&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>
using that same old index scan, stash them into an in-memory hash table,
and then do a sequential scan of <classname>tenk2</classname>, probing into the hash table
for possible matches of <literal>t1.unique2 = t2.unique2</literal> at each <classname>tenk2</classname> row.
The cost to read <classname>tenk1</classname> and set up the hash table is entirely start-up
cost for the hash join, since we won't get any rows out 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>
It is possible to check on the accuracy of the planner's estimated costs
by using <command>EXPLAIN ANALYZE</>. This command actually executes the query,
and then displays the true run time accumulated within each plan node
along with the same estimated costs 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 &lt; 100 AND t1.unique2 = t2.unique2;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=2.37..553.11 rows=106 width=488) (actual time=1.392..12.700 rows=100 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)
Recheck Cond: (unique1 &lt; 100)
-&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)
Index Cond: (unique1 &lt; 100)
-&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)
Index Cond: ("outer".unique2 = t2.unique2)
Total runtime: 14.452 ms
</screen>
Note that the <quote>actual time</quote> values are in milliseconds of
real time, whereas the <quote>cost</quote> estimates are expressed in
arbitrary units; so they are unlikely to match up.
The thing to pay attention to is whether the ratios of actual time and
estimated costs are consistent.
</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 is executed once per outer
row in the above nested-loop plan. In such cases, the
<quote>loops</quote> 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 <quote>loops</quote> value to get the total time actually spent in
the node.
</para>
<para>
The <literal>Total runtime</literal> shown by <command>EXPLAIN
ANALYZE</command> includes executor start-up and shut-down time, as well
as time spent processing the result rows. It does not include parsing,
rewriting, or planning time. For a <command>SELECT</> query, the total
run time will normally be just a little larger than the total time
reported for the top-level plan node. For <command>INSERT</>,
<command>UPDATE</>, and <command>DELETE</> commands, the total run time
might be considerably larger, because it includes the time spent processing
the result rows. In these commands, the time for the top plan node
essentially is the time spent computing the new rows and/or locating the
old ones, but it doesn't include the time spent applying the changes.
Time spent firing triggers, if any, is also outside the top plan node,
and is shown separately for each trigger.
</para>
<para>
It is worth noting that <command>EXPLAIN</> results should not be extrapolated
to situations other than 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.
</para>
</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 stand-alone
<command>ANALYZE</>, that is one not part of <command>VACUUM</>,
generates an approximate <structfield>reltuples</structfield> value
since it does not read every row of the table. 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 having <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 <structname>pg_stats</structname>
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, n_distinct, most_common_vals FROM pg_stats WHERE tablename = 'road';
attname | n_distinct | most_common_vals
---------+------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
name | -0.467008 | {"I- 580 Ramp","I- 880 Ramp","Sp Railroad ","I- 580 ","I- 680 Ramp","I- 80 Ramp","14th St ","5th St ","Mission Blvd","I- 880 "}
thepath | 20 | {"[(-122.089,37.71),(-122.0886,37.711)]"}
(2 rows)
</screen>
</para>
<para>
<structname>pg_stats</structname> is described in detail in
<xref linkend="view-pg-stats">.
</para>
<para>
The amount of information stored in <structname>pg_statistic</structname>,
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 10 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
appropriate for columns with simple data distributions.
</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 &mdash; 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
&mdash; 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</> subselects, 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 subselects
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>
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" endterm="sql-copy-title"> 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" endterm="sql-prepare-title"> 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 does not apply when
<xref linkend="guc-archive-command"> is set, as all commands
must write WAL in that case.
</para>
</sect2>
<sect2 id="populate-rm-indexes">
<title>Remove Indexes</title>
<para>
If you are loading a freshly created table, the fastest way 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 index,
load the table, and then recreate the index. Of course, the
database performance for other users might be adversely affected
during the time that the index is missing. One should also think
twice before dropping unique indexes, 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>
</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>Turn off <varname>archive_command</varname></title>
<para>
When loading large amounts of data into an installation that uses
WAL archiving, you might want to disable archiving (unset the
<xref linkend="guc-archive-command"> configuration variable)
while loading. 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.
</para>
<para>
Aside from avoiding the time for the archiver 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>archive_command</varname>
is unset. (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"
endterm="sql-analyze-title"> 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.
</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 when 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, consider disabling it during the restore.
To do that, unset <varname>archive_command</varname> before loading the
dump script, and afterwards restore <varname>archive_command</varname>
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>
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 &mdash; but realize that
that eliminates, rather than just postponing, 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.
</para>
</sect2>
</sect1>
</chapter>
Reference in New Issue View Git Blame Copy Permalink