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<chapter id="performance-tips">
<title>Performance Tips</title>
<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>Postgres</productname> performance.
</para>
<sect1 id="using-explain">
<title>Using <command>EXPLAIN</command></title>
<para>
<productname>Postgres</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. You can use the
<command>EXPLAIN</command> command to see what query plan the system
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 numbers that are currently 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 tuples are retrieved, which they may not
be --- a query with a LIMIT will stop short of paying the total cost,
for example).
</para>
</listitem>
<listitem>
<para>
Estimated number of rows output by this plan node (again, without
regard for any LIMIT).
</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 units of disk page fetches. (CPU effort
estimates are converted into disk-page units using some
fairly arbitrary fudge-factors. If you want to experiment with these
factors, see the list of run-time configuration parameters in the
<citetitle>Administrator's Guide</citetitle>.)
</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/optimizer cares about.
In particular, the cost does not consider the time spent transmitting
result tuples to the frontend --- which could be a pretty dominant
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 tuple set, we trust.)
</para>
<para>
Rows output is a little tricky because it is <emphasis>not</emphasis> the
number of rows
processed/scanned by the query --- it is usually less, reflecting the
estimated selectivity of any WHERE-clause constraints that are being
applied at this node. Ideally the top-level rows estimate will
approximate the number of rows actually returned, updated, or deleted
by the query.
</para>
<para>
Here are some examples (using the regress test database after a
vacuum analyze, and 7.2 development sources):
<programlisting>
regression=# explain select * from tenk1;
NOTICE: QUERY PLAN:
Seq Scan on tenk1 (cost=0.00..333.00 rows=10000 width=148)
</programlisting>
</para>
<para>
This is about as straightforward as it gets. If you do
<programlisting>
select * from pg_class where relname = 'tenk1';
</programlisting>
you will find out that <classname>tenk1</classname> has 233 disk
pages and 10000 tuples. So the cost is estimated at 233 page
reads, defined as 1.0 apiece, plus 10000 * <varname>cpu_tuple_cost</varname> which is
currently 0.01 (try <command>show cpu_tuple_cost</command>).
</para>
<para>
Now let's modify the query to add a qualification clause:
<programlisting>
regression=# explain select * from tenk1 where unique1 < 1000;
NOTICE: QUERY PLAN:
Seq Scan on tenk1 (cost=0.00..358.00 rows=1007 width=148)
</programlisting>
The estimate of output rows has gone down because of the 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 WHERE condition.
</para>
<para>
The actual number of rows this query would select is 1000, but the
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>
Modify the query to restrict the qualification even more:
<programlisting>
regression=# explain select * from tenk1 where unique1 < 50;
NOTICE: QUERY PLAN:
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..181.09 rows=49 width=148)
</programlisting>
and you will see that if we make the WHERE condition selective
enough, the planner will
eventually decide that an index scan is cheaper than a sequential scan.
This plan will only have to visit 50 tuples because of the index,
so it wins despite the fact that each individual fetch is more expensive
than reading a whole disk page sequentially.
</para>
<para>
Add another condition to the qualification:
<programlisting>
regression=# explain select * from tenk1 where unique1 < 50 and
regression-# stringu1 = 'xxx';
NOTICE: QUERY PLAN:
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..181.22 rows=1 width=148)
</programlisting>
The added clause <literal>stringu1 = 'xxx'</literal> reduces the output-rows estimate,
but not the cost because we still have to visit the same set of tuples.
</para>
<para>
Let's try joining two tables, using the fields we have been discussing:
<programlisting>
regression=# explain select * from tenk1 t1, tenk2 t2 where t1.unique1 < 50
regression-# and t1.unique2 = t2.unique2;
NOTICE: QUERY PLAN:
Nested Loop (cost=0.00..330.41 rows=49 width=296)
-> Index Scan using tenk1_unique1 on tenk1 t1
(cost=0.00..181.09 rows=49 width=148)
-> Index Scan using tenk2_unique2 on tenk2 t2
(cost=0.00..3.01 rows=1 width=148)
</programlisting>
</para>
<para>
In this nested-loop join, the outer scan is the same index scan we had
in the example before last, and so its cost and row count are the same
because we are applying the <literal>unique1 < 50</literal> WHERE clause 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 unique2 value of the
current
outer-scan tuple is plugged into the inner index scan
to produce an index qualification 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 tuple (49 * 3.01, here), plus a little CPU
time for join processing.
</para>
<para>
In this example the loop'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 WHERE clauses that mention both relations 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 < 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 for each plan type. (This is a crude tool, but useful. See
also <xref linkend="explicit-joins">.)
<programlisting>
regression=# set enable_nestloop = off;
SET VARIABLE
regression=# explain select * from tenk1 t1, tenk2 t2 where t1.unique1 < 50
regression-# and t1.unique2 = t2.unique2;
NOTICE: QUERY PLAN:
Hash Join (cost=181.22..564.83 rows=49 width=296)
-> Seq Scan on tenk2 t2
(cost=0.00..333.00 rows=10000 width=148)
-> Hash (cost=181.09..181.09 rows=49 width=148)
-> Index Scan using tenk1_unique1 on tenk1 t1
(cost=0.00..181.09 rows=49 width=148)
</programlisting>
This plan proposes to extract the 50 interesting rows of <classname>tenk1</classname>
using ye same olde 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> tuple.
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 tuples out until we can
start reading <classname>tenk2</classname>. The total time estimate for the join also
includes a hefty charge for CPU time to probe the hash table
10000 times. Note, however, that we are NOT charging 10000 times 181.09;
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 EXPLAIN ANALYZE. This command actually executes the query,
and then displays the true runtime accumulated within each plan node
along with the same estimated costs that a plain EXPLAIN shows.
For example, we might get a result like this:
<programlisting>
regression=# explain analyze select * from tenk1 t1, tenk2 t2 where t1.unique1 < 50
regression-# and t1.unique2 = t2.unique2;
NOTICE: QUERY PLAN:
Nested Loop (cost=0.00..330.41 rows=49 width=296) (actual time=1.31..28.90 rows=50 loops=1)
-> Index Scan using tenk1_unique1 on tenk1 t1
(cost=0.00..181.09 rows=49 width=148) (actual time=0.69..8.84 rows=50 loops=1)
-> Index Scan using tenk2_unique2 on tenk2 t2
(cost=0.00..3.01 rows=1 width=148) (actual time=0.28..0.31 rows=1 loops=50)
Total runtime: 30.67 msec
</programlisting>
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 of disk fetches; so they are unlikely to match up.
The thing to pay attention to is the ratios.
</para>
<para>
In some query plans, it is possible for a subplan node to be executed more
than once. For example, the inner indexscan is executed once per outer
tuple 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 <quote>total runtime</quote> shown by EXPLAIN ANALYZE includes
executor startup and shutdown time, as well as time spent processing
the result tuples. It does not include parsing, rewriting, or planning
time. For a SELECT query, the total runtime will normally be just a
little larger than the total time reported for the top-level plan node.
For INSERT, UPDATE, and DELETE queries, the total runtime may be
considerably larger, because it includes the time spent processing the
output tuples. In these queries, the time for the top plan node
essentially is the time spent computing the new tuples and/or locating
the old ones, but it doesn't include the time spent making the changes.
</para>
<para>
It is worth noting that EXPLAIN results should not be extrapolated
to situations other than the one you are actually testing; for example,
results on a toy-sized table can't be assumed to apply to large tables.
The planner's cost estimates are not linear and so it may well 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="explicit-joins">
<title>Controlling the Planner with Explicit JOINs</title>
<para>
Beginning with <productname>Postgres</productname> 7.1 it is possible
to control the query planner to some extent by using explicit 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 WHERE clause
a.id = b.id, and then joins C to this joined table, using the other
WHERE clause. Or it could join B to C and then join A to that result.
Or it could join A to C and then join them with B --- but that would
be inefficient, since the full Cartesian product of A and C would have
to be formed, there being no applicable WHERE clause to allow optimization
of the join.
(All joins in the <productname>Postgres</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 may 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 may take an
annoyingly long time. When there are too many input tables, the
<productname>Postgres</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 <varname>GEQO_THRESHOLD</varname> run-time
parameter described in the <citetitle>Administrator's Guide</citetitle>.)
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 much 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.
</para>
<para>
In <productname>Postgres</productname> 7.1, the planner treats all
explicit JOIN syntaxes as constraining the join order, even though
it is not logically necessary to make such a constraint for inner
joins. Therefore, although all of these queries give the same result:
<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>
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>
You do not need to constrain the join order completely in order to
cut search time, because it's OK to use JOIN operators in a plain
FROM list. For example,
<programlisting>
SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...;
</programlisting>
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>
If you have a mix of outer and inner joins in a complex query, you
might not want to constrain the planner's search for a good ordering
of inner joins inside an outer join. You can't do that directly in the
JOIN syntax, but you can get around the syntactic limitation by using
subselects. For example,
<programlisting>
SELECT * FROM d LEFT JOIN
(SELECT * FROM a, b, c WHERE ...) AS ss
ON (...);
</programlisting>
Here, joining D must be the last step in the query plan, but the
planner is free to consider various join orders for A,B,C.
</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 JOIN syntax --- assuming
that you know of a better order, that is. Experimentation is recommended.
</para>
</sect1>
<sect1 id="populate">
<title>Populating a Database</title>
<para>
One may need to do a large number of table insertions when first
populating a database. Here are some tips and techniques for making that as
efficient as possible.
</para>
<sect2 id="disable-autocommit">
<title>Disable Auto-commit</title>
<para>
Turn off auto-commit 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 may 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>Postgres</productname> is doing a lot of work for each
record added.
</para>
</sect2>
<sect2 id="populate-copy-from">
<title>Use COPY FROM</title>
<para>
Use <command>COPY FROM STDIN</command> to load all the records in one
command, instead of using
a series of <command>INSERT</command> commands. This reduces parsing,
planning, etc
overhead a great deal. If you do this then it's not necessary to fool
around with auto-commit, since it's only one command anyway.
</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 with <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 record is loaded.
</para>
<para>
If you are augmenting an existing table, you can <command>DROP
INDEX</command>, load the table, then recreate the index. Of
course, the database performance for other users may 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-analyze">
<title>ANALYZE Afterwards</title>
<para>
It's a good idea to run <command>ANALYZE</command> or <command>VACUUM
ANALYZE</command> anytime you've added or updated a lot of data,
including just after initially populating a table. This ensures that
the planner has up-to-date statistics about the table. With no statistics
or obsolete statistics, the planner may make poor choices of query plans,
leading to bad performance on queries that use your table.
</para>
</sect2>
</sect1>
</chapter>
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