Performance Tips
================

performance

Query performance can be affected by many things. Some of these can be
controlled by the user, while others are fundamental to the underlying
design of the system. This chapter provides some hints about
understanding and tuning PostgreSQL performance.

Using `EXPLAIN`
===============

EXPLAIN

query plan

PostgreSQL devises a query plan for each query it receives. 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 planner that tries to choose good plans. You can use the
[`EXPLAIN`](#sql-explain) command to see what query plan the planner
creates for any query. Plan-reading is an art that requires some
experience to master, but this section attempts to cover the basics.

Examples in this section are drawn from the regression test database
after doing a `VACUUM ANALYZE`, using 9.3 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
`ANALYZE`\'s statistics are random samples rather than exact, and
because costs are inherently somewhat platform-dependent.

The examples use `EXPLAIN`\'s default "text" output format, which is
compact and convenient for humans to read. If you want to feed
`EXPLAIN`\'s output to a program for further analysis, you should use
one of its machine-readable output formats (XML, JSON, or YAML) instead.

`EXPLAIN` Basics {#using-explain-basics}
----------------

The structure of a query plan is a tree of plan nodes. 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 table
access methods: sequential scans, index scans, and bitmap index scans.
There are also non-table row sources, such as `VALUES` clauses and
set-returning functions in `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 above 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 `EXPLAIN` 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. Additional lines might
appear, indented from the node\'s summary line, 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.

Here is a trivial example, just to show what the output looks like:

    EXPLAIN SELECT * FROM tenk1;

                             QUERY PLAN
    -------------------------------------------------------------
     Seq Scan on tenk1  (cost=0.00..458.00 rows=10000 width=244)

Since this query has no `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):

-   Estimated start-up cost. This is the time expended before the output
    phase can begin, e.g., time to do the sorting in a sort node.

-   Estimated total cost. This is stated on the assumption that the plan
    node is run to completion, i.e., all available rows are retrieved.
    In practice a node\'s parent node might stop short of reading all
    available rows (see the `LIMIT` example below).

-   Estimated number of rows output by this plan node. Again, the node
    is assumed to be run to completion.

-   Estimated average width of rows output by this plan node (in bytes).

The costs are measured in arbitrary units determined by the planner\'s
cost parameters (see [???](#runtime-config-query-constants)).
Traditional practice is to measure the costs in units of disk page
fetches; that is, [???](#guc-seq-page-cost) is conventionally set to
`1.0` and the other cost parameters are set relative to that. The
examples in this section are run with the default cost parameters.

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 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
real 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.)

The `rows` value is a little tricky because it is not the number of rows
processed or scanned by the plan node, but rather the number emitted by
the node. This is often less than the number scanned, as a result of
filtering by any `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.

Returning to our example:

    EXPLAIN SELECT * FROM tenk1;

                             QUERY PLAN
    -------------------------------------------------------------
     Seq Scan on tenk1  (cost=0.00..458.00 rows=10000 width=244)

These numbers are derived very straightforwardly. If you do:

    SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';

you will find that `tenk1` has 358 disk pages and 10000 rows. The
estimated cost is computed as (disk pages read \*
[???](#guc-seq-page-cost)) + (rows scanned \*
[???](#guc-cpu-tuple-cost)). By default, `seq_page_cost` is 1.0 and
`cpu_tuple_cost` is 0.01, so the estimated cost is (358 \* 1.0) + (10000
\* 0.01) = 458.

Now let\'s modify the query to add a `WHERE` condition:

    EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000;

                             QUERY PLAN
    ------------------------------------------------------------
     Seq Scan on tenk1  (cost=0.00..483.00 rows=7001 width=244)
       Filter: (unique1 < 7000)

Notice that the `EXPLAIN` output shows the `WHERE` clause being applied
as a "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 only the ones that pass the condition. The estimate of output
rows has been reduced 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 (by 10000 \* [???](#guc-cpu-operator-cost),
to be exact) to reflect the extra CPU time spent checking the `WHERE`
condition.

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 can change after each `ANALYZE` command, because the
statistics produced by `ANALYZE` are taken from a randomized sample of
the table.

Now, let\'s make the condition more restrictive:

    EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100;

                                      QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------
     Bitmap Heap Scan on tenk1  (cost=5.07..229.20 rows=101 width=244)
       Recheck Cond: (unique1 < 100)
       ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0)
             Index Cond: (unique1 < 100)

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
condition, and then the upper plan node actually fetches those rows from
the table itself. Fetching rows separately is much more 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 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 before reading
them, to minimize the cost of separate fetches. The "bitmap" mentioned
in the node names is the mechanism that does the sorting.)

Now let\'s add another condition to the `WHERE` clause:

    EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND stringu1 = 'xxx';

                                      QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------
     Bitmap Heap Scan on tenk1  (cost=5.04..229.43 rows=1 width=244)
       Recheck Cond: (unique1 < 100)
       Filter: (stringu1 = 'xxx'::name)
       ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0)
             Index Cond: (unique1 < 100)

The added condition `stringu1 = 'xxx'` reduces the output row count
estimate, but not the cost because we still have to visit the same set
of rows. Notice that the `stringu1` clause cannot be applied as an index
condition, since this index is only on the `unique1` column. Instead it
is applied as a filter on the rows retrieved by the index. Thus the cost
has actually gone up slightly to reflect this extra checking.

In some cases the planner will prefer a "simple" index scan plan:

    EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42;

                                     QUERY PLAN
    -------------------------------------------------------------------ZWSP----------
     Index Scan using tenk1_unique1 on tenk1  (cost=0.29..8.30 rows=1 width=244)
       Index Cond: (unique1 = 42)

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 `ORDER BY` condition that
matches the index order, because then no extra sorting step is needed to
satisfy the `ORDER BY`. In this example, adding `ORDER BY unique1` would
use the same plan because the index already implicitly provides the
requested ordering.

The planner may implement an `ORDER BY` clause in several ways. The
above example shows that such an ordering clause may be implemented
implicitly. The planner may also add an explicit `sort` step:

    EXPLAIN SELECT * FROM tenk1 ORDER BY unique1;
                                QUERY PLAN
    -------------------------------------------------------------------
     Sort  (cost=1109.39..1134.39 rows=10000 width=244)
       Sort Key: unique1
       ->  Seq Scan on tenk1  (cost=0.00..445.00 rows=10000 width=244)

If a part of the plan guarantees an ordering on a prefix of the required
sort keys, then the planner may instead decide to use an
`incremental sort` step:

    EXPLAIN SELECT * FROM tenk1 ORDER BY four, ten LIMIT 100;
                                                  QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------------------------------
     Limit  (cost=521.06..538.05 rows=100 width=244)
       ->  Incremental Sort  (cost=521.06..2220.95 rows=10000 width=244)
             Sort Key: four, ten
             Presorted Key: four
             ->  Index Scan using index_tenk1_on_four on tenk1  (cost=0.29..1510.08 rows=10000 width=244)

Compared to regular sorts, sorting incrementally allows returning tuples
before the entire result set has been sorted, which particularly enables
optimizations with `LIMIT` queries. It may also reduce memory usage and
the likelihood of spilling sorts to disk, but it comes at the cost of
the increased overhead of splitting the result set into multiple sorting
batches.

If there are separate indexes on several of the columns referenced in
`WHERE`, the planner might choose to use an AND or OR combination of the
indexes:

    EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000;

                                         QUERY PLAN
    -------------------------------------------------------------------ZWSP------------------
     Bitmap Heap Scan on tenk1  (cost=25.08..60.21 rows=10 width=244)
       Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))
       ->  BitmapAnd  (cost=25.08..25.08 rows=10 width=0)
             ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0)
                   Index Cond: (unique1 < 100)
             ->  Bitmap Index Scan on tenk1_unique2  (cost=0.00..19.78 rows=999 width=0)
                   Index Cond: (unique2 > 9000)

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.

Here is an example showing the effects of `LIMIT`:

    EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2;

                                         QUERY PLAN
    -------------------------------------------------------------------ZWSP------------------
     Limit  (cost=0.29..14.48 rows=2 width=244)
       ->  Index Scan using tenk1_unique2 on tenk1  (cost=0.29..71.27 rows=10 width=244)
             Index Cond: (unique2 > 9000)
             Filter: (unique1 < 100)

This is the same query as above, but we added a `LIMIT` so that not all
the rows need be retrieved, and the planner changed its mind about 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.

Let\'s try joining two tables, using the columns we have been
discussing:

    EXPLAIN SELECT *
    FROM tenk1 t1, tenk2 t2
    WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2;

                                          QUERY PLAN
    -------------------------------------------------------------------ZWSP-------------------
     Nested Loop  (cost=4.65..118.62 rows=10 width=488)
       ->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.47 rows=10 width=244)
             Recheck Cond: (unique1 < 10)
             ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=0)
                   Index Cond: (unique1 < 10)
       ->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..7.91 rows=1 width=244)
             Index Cond: (unique2 = t1.unique2)

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 "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 `SELECT ... WHERE unique1 < 10` because we
are applying the `WHERE` clause `unique1 < 10` at that node. The
`t1.unique2 = t2.unique2` clause is not relevant yet, so it doesn\'t
affect the row count of the outer scan. The nested-loop join node will
run its second, or "inner" child once for each row obtained from the
outer child. Column values from the current outer row can be plugged
into the inner scan; here, the `t1.unique2` value from the outer row is
available, so we get a plan and costs similar to what we saw above for a
simple `SELECT ... WHERE t2.unique2 = 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 index scans on
`t2`.) 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 (10 \* 7.91, here), plus a little CPU time for join processing.

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
there can be additional `WHERE` clauses that mention both tables and so
can only be applied at the join point, not to either input scan. Here\'s
an example:

    EXPLAIN SELECT *
    FROM tenk1 t1, tenk2 t2
    WHERE t1.unique1 < 10 AND t2.unique2 < 10 AND t1.hundred < t2.hundred;

                                             QUERY PLAN
    -------------------------------------------------------------------ZWSP--------------------------
     Nested Loop  (cost=4.65..49.46 rows=33 width=488)
       Join Filter: (t1.hundred < t2.hundred)
       ->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.47 rows=10 width=244)
             Recheck Cond: (unique1 < 10)
             ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=0)
                   Index Cond: (unique1 < 10)
       ->  Materialize  (cost=0.29..8.51 rows=10 width=244)
             ->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..8.46 rows=10 width=244)
                   Index Cond: (unique2 < 10)

The condition `t1.hundred < t2.hundred` can\'t be tested in the
`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.

Notice that here the planner has chosen to "materialize" the inner
relation of the join, by putting a Materialize plan node atop it. This
means that the `t2` index scan will be done just once, even though the
nested-loop join node needs to read that data ten times, once for each
row from the outer relation. The Materialize node saves the data in
memory as it\'s read, and then returns the data from memory on each
subsequent pass.

When dealing with outer joins, you might see join plan nodes with both
"Join Filter" and plain "Filter" conditions attached. Join Filter
conditions come from the outer join\'s `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.

If we change the query\'s selectivity a bit, we might get a very
different join plan:

    EXPLAIN SELECT *
    FROM tenk1 t1, tenk2 t2
    WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;

                                            QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------------------
     Hash Join  (cost=230.47..713.98 rows=101 width=488)
       Hash Cond: (t2.unique2 = t1.unique2)
       ->  Seq Scan on tenk2 t2  (cost=0.00..445.00 rows=10000 width=244)
       ->  Hash  (cost=229.20..229.20 rows=101 width=244)
             ->  Bitmap Heap Scan on tenk1 t1  (cost=5.07..229.20 rows=101 width=244)
                   Recheck Cond: (unique1 < 100)
                   ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0)
                         Index Cond: (unique1 < 100)

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 `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.

Another possible type of join is a merge join, illustrated here:

    EXPLAIN SELECT *
    FROM tenk1 t1, onek t2
    WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;

                                            QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------------------
     Merge Join  (cost=198.11..268.19 rows=10 width=488)
       Merge Cond: (t1.unique2 = t2.unique2)
       ->  Index Scan using tenk1_unique2 on tenk1 t1  (cost=0.29..656.28 rows=101 width=244)
             Filter: (unique1 < 100)
       ->  Sort  (cost=197.83..200.33 rows=1000 width=244)
             Sort Key: t2.unique2
             ->  Seq Scan on onek t2  (cost=0.00..148.00 rows=1000 width=244)

Merge join requires its input data to be sorted on the join keys. In
this plan the `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 `onek`, because there are many more rows to be visited in that
table. (Sequential-scan-and-sort frequently beats an index scan for
sorting many rows, because of the nonsequential disk access required by
the index scan.)

One way to look at variant plans is to force the planner to disregard
whatever strategy it thought was the cheapest, using the enable/disable
flags described in [???](#runtime-config-query-enable). (This is a crude
tool, but useful. See also [Controlling the Planner with Explicit
Clauses](#explicit-joins).) For example, if we\'re unconvinced that
sequential-scan-and-sort is the best way to deal with table `onek` in
the previous example, we could try

    SET enable_sort = off;

    EXPLAIN SELECT *
    FROM tenk1 t1, onek t2
    WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;

                                            QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------------------
     Merge Join  (cost=0.56..292.65 rows=10 width=488)
       Merge Cond: (t1.unique2 = t2.unique2)
       ->  Index Scan using tenk1_unique2 on tenk1 t1  (cost=0.29..656.28 rows=101 width=244)
             Filter: (unique1 < 100)
       ->  Index Scan using onek_unique2 on onek t2  (cost=0.28..224.79 rows=1000 width=244)

which shows that the planner thinks that sorting `onek` by
index-scanning is about 12% more expensive than
sequential-scan-and-sort. Of course, the next question is whether it\'s
right about that. We can investigate that using `EXPLAIN ANALYZE`, as
discussed below.

`EXPLAIN ANALYZE` {#using-explain-analyze}
-----------------

It is possible to check the accuracy of the planner\'s estimates by
using `EXPLAIN`\'s `ANALYZE` option. With this option, `EXPLAIN`
actually executes the query, and then displays the true row counts and
true run time accumulated within each plan node, along with the same
estimates that a plain `EXPLAIN` shows. For example, we might get a
result like this:

    EXPLAIN ANALYZE SELECT *
    FROM tenk1 t1, tenk2 t2
    WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2;

                                                               QUERY PLAN
    -------------------------------------------------------------------ZWSP--------------------------------------------------------------
     Nested Loop  (cost=4.65..118.62 rows=10 width=488) (actual time=0.128..0.377 rows=10 loops=1)
       ->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.47 rows=10 width=244) (actual time=0.057..0.121 rows=10 loops=1)
             Recheck Cond: (unique1 < 10)
             ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=0) (actual time=0.024..0.024 rows=10 loops=1)
                   Index Cond: (unique1 < 10)
       ->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..7.91 rows=1 width=244) (actual time=0.021..0.022 rows=1 loops=10)
             Index Cond: (unique2 = t1.unique2)
     Planning time: 0.181 ms
     Execution time: 0.501 ms

Note that the "actual time" values are in milliseconds of real time,
whereas the `cost` estimates are expressed in arbitrary units; so they
are unlikely to match up. The thing that\'s usually most important to
look for is whether the estimated row counts are reasonably close to
reality. In this example the estimates were all dead-on, but that\'s
quite unusual in practice.

In some query plans, it is possible for a subplan node to be executed
more than once. For example, the inner index scan will be executed once
per outer row in the above nested-loop plan. In such cases, the `loops`
value reports the total number of executions of the node, and the actual
time and rows values shown are averages per-execution. This is done to
make the numbers comparable with the way that the cost estimates are
shown. Multiply by the `loops` value to get the total time actually
spent in the node. In the above example, we spent a total of 0.220
milliseconds executing the index scans on `tenk2`.

In some cases `EXPLAIN ANALYZE` shows additional execution statistics
beyond the plan node execution times and row counts. For example, Sort
and Hash nodes provide extra information:

    EXPLAIN ANALYZE SELECT *
    FROM tenk1 t1, tenk2 t2
    WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2 ORDER BY t1.fivethous;

                                                                     QUERY PLAN
    -------------------------------------------------------------------ZWSP-------------------------------------------------------------------ZWSP------
     Sort  (cost=717.34..717.59 rows=101 width=488) (actual time=7.761..7.774 rows=100 loops=1)
       Sort Key: t1.fivethous
       Sort Method: quicksort  Memory: 77kB
       ->  Hash Join  (cost=230.47..713.98 rows=101 width=488) (actual time=0.711..7.427 rows=100 loops=1)
             Hash Cond: (t2.unique2 = t1.unique2)
             ->  Seq Scan on tenk2 t2  (cost=0.00..445.00 rows=10000 width=244) (actual time=0.007..2.583 rows=10000 loops=1)
             ->  Hash  (cost=229.20..229.20 rows=101 width=244) (actual time=0.659..0.659 rows=100 loops=1)
                   Buckets: 1024  Batches: 1  Memory Usage: 28kB
                   ->  Bitmap Heap Scan on tenk1 t1  (cost=5.07..229.20 rows=101 width=244) (actual time=0.080..0.526 rows=100 loops=1)
                         Recheck Cond: (unique1 < 100)
                         ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0) (actual time=0.049..0.049 rows=100 loops=1)
                               Index Cond: (unique1 < 100)
     Planning time: 0.194 ms
     Execution time: 8.008 ms

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.)

Another type of extra information is the number of rows removed by a
filter condition:

    EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE ten < 7;

                                                   QUERY PLAN
    -------------------------------------------------------------------ZWSP--------------------------------------
     Seq Scan on tenk1  (cost=0.00..483.00 rows=7000 width=244) (actual time=0.016..5.107 rows=7000 loops=1)
       Filter: (ten < 7)
       Rows Removed by Filter: 3000
     Planning time: 0.083 ms
     Execution time: 5.905 ms

These counts can be particularly valuable for filter conditions applied
at join nodes. The "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.

A case similar to filter conditions occurs with "lossy" index scans. For
example, consider this search for polygons containing a specific point:

    EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)';

                                                  QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------------------------------
     Seq Scan on polygon_tbl  (cost=0.00..1.05 rows=1 width=32) (actual time=0.044..0.044 rows=0 loops=1)
       Filter: (f1 @> '((0.5,2))'::polygon)
       Rows Removed by Filter: 4
     Planning time: 0.040 ms
     Execution time: 0.083 ms

The planner thinks (quite correctly) that this sample table is too small
to bother with an index scan, so we have a plain sequential scan in
which all the rows got rejected by the filter condition. But if we force
an index scan to be used, we see:

    SET enable_seqscan TO off;

    EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)';

                                                            QUERY PLAN
    -------------------------------------------------------------------ZWSP-------------------------------------------------------
     Index Scan using gpolygonind on polygon_tbl  (cost=0.13..8.15 rows=1 width=32) (actual time=0.062..0.062 rows=0 loops=1)
       Index Cond: (f1 @> '((0.5,2))'::polygon)
       Rows Removed by Index Recheck: 1
     Planning time: 0.034 ms
     Execution time: 0.144 ms

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 "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.

`EXPLAIN` has a `BUFFERS` option that can be used with `ANALYZE` to get
even more run time statistics:

    EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000;

                                                               QUERY PLAN
    -------------------------------------------------------------------ZWSP--------------------------------------------------------------
     Bitmap Heap Scan on tenk1  (cost=25.08..60.21 rows=10 width=244) (actual time=0.323..0.342 rows=10 loops=1)
       Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))
       Buffers: shared hit=15
       ->  BitmapAnd  (cost=25.08..25.08 rows=10 width=0) (actual time=0.309..0.309 rows=0 loops=1)
             Buffers: shared hit=7
             ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1)
                   Index Cond: (unique1 < 100)
                   Buffers: shared hit=2
             ->  Bitmap Index Scan on tenk1_unique2  (cost=0.00..19.78 rows=999 width=0) (actual time=0.227..0.227 rows=999 loops=1)
                   Index Cond: (unique2 > 9000)
                   Buffers: shared hit=5
     Planning time: 0.088 ms
     Execution time: 0.423 ms

The numbers provided by `BUFFERS` help to identify which parts of the
query are the most I/O-intensive.

Keep in mind that because `EXPLAIN ANALYZE` 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 `EXPLAIN`
data. If you want to analyze a data-modifying query without changing
your tables, you can roll the command back afterwards, for example:

    BEGIN;

    EXPLAIN ANALYZE UPDATE tenk1 SET hundred = hundred + 1 WHERE unique1 < 100;

                                                               QUERY PLAN
    -------------------------------------------------------------------ZWSP-------------------------------------------------------------
     Update on tenk1  (cost=5.08..230.08 rows=0 width=0) (actual time=3.791..3.792 rows=0 loops=1)
       ->  Bitmap Heap Scan on tenk1  (cost=5.08..230.08 rows=102 width=10) (actual time=0.069..0.513 rows=100 loops=1)
             Recheck Cond: (unique1 < 100)
             Heap Blocks: exact=90
             ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.05 rows=102 width=0) (actual time=0.036..0.037 rows=300 loops=1)
                   Index Cond: (unique1 < 100)
     Planning Time: 0.113 ms
     Execution Time: 3.850 ms

    ROLLBACK;

As seen in this example, when the query is an `INSERT`, `UPDATE`, or
`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 run time (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.

When an `UPDATE` or `DELETE` command affects an inheritance hierarchy,
the output might look like this:

    EXPLAIN UPDATE parent SET f2 = f2 + 1 WHERE f1 = 101;
                                                  QUERY PLAN
    -------------------------------------------------------------------ZWSP-----------------------------------
     Update on parent  (cost=0.00..24.59 rows=0 width=0)
       Update on parent parent_1
       Update on child1 parent_2
       Update on child2 parent_3
       Update on child3 parent_4
       ->  Result  (cost=0.00..24.59 rows=4 width=14)
             ->  Append  (cost=0.00..24.54 rows=4 width=14)
                   ->  Seq Scan on parent parent_1  (cost=0.00..0.00 rows=1 width=14)
                         Filter: (f1 = 101)
                   ->  Index Scan using child1_pkey on child1 parent_2  (cost=0.15..8.17 rows=1 width=14)
                         Index Cond: (f1 = 101)
                   ->  Index Scan using child2_pkey on child2 parent_3  (cost=0.15..8.17 rows=1 width=14)
                         Index Cond: (f1 = 101)
                   ->  Index Scan using child3_pkey on child3 parent_4  (cost=0.15..8.17 rows=1 width=14)
                         Index Cond: (f1 = 101)

In this example the Update node needs to consider three child tables as
well as the originally-mentioned parent table. So there are four input
scanning subplans, one per table. For clarity, the Update node is
annotated to show the specific target tables that will be updated, in
the same order as the corresponding subplans.

The `Planning time` shown by `EXPLAIN
    ANALYZE` is the time it took to generate the query plan from the
parsed query and optimize it. It does not include parsing or rewriting.

The `Execution time` shown by `EXPLAIN
    ANALYZE` includes executor start-up and shut-down time, as well as
the time to run any triggers that are fired, but it does not include
parsing, rewriting, or planning time. Time spent executing `BEFORE`
triggers, if any, is included in the time for the related Insert,
Update, or Delete node; but time spent executing `AFTER` triggers is not
counted there because `AFTER` triggers are fired after completion of the
whole plan. The total time spent in each trigger (either `BEFORE` or
`AFTER`) is also shown separately. Note that deferred constraint
triggers will not be executed until end of transaction and are thus not
considered at all by `EXPLAIN ANALYZE`.

Caveats {#using-explain-caveats}
-------

There are two significant ways in which run times measured by
`EXPLAIN ANALYZE` can deviate from normal execution of the same query.
First, since no output rows are delivered to the client, network
transmission costs and I/O conversion costs are not included. Second,
the measurement overhead added by `EXPLAIN
    ANALYZE` can be significant, especially on machines with slow
`gettimeofday()` operating-system calls. You can use the
[???](#pgtesttiming) tool to measure the overhead of timing on your
system.

`EXPLAIN` results should not be extrapolated to situations much
different from the one you are actually testing; for example, results on
a toy-sized table cannot be assumed to apply to large tables. The
planner\'s cost estimates are not linear and so it might choose a
different plan for a larger or smaller table. An extreme example is that
on a table that only occupies one disk page, you\'ll nearly always get a
sequential scan plan whether indexes are available or not. The planner
realizes that it\'s going to take one disk page read to process the
table in any case, so there\'s no value in expending additional page
reads to look at an index. (We saw this happening in the `polygon_tbl`
example above.)

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 `LIMIT` or similar effect. For example,
in the `LIMIT` query we used before,

    EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2;

                                                              QUERY PLAN
    -------------------------------------------------------------------ZWSP------------------------------------------------------------
     Limit  (cost=0.29..14.71 rows=2 width=244) (actual time=0.177..0.249 rows=2 loops=1)
       ->  Index Scan using tenk1_unique2 on tenk1  (cost=0.29..72.42 rows=10 width=244) (actual time=0.174..0.244 rows=2 loops=1)
             Index Cond: (unique2 > 9000)
             Filter: (unique1 < 100)
             Rows Removed by Filter: 287
     Planning time: 0.096 ms
     Execution time: 0.336 ms

the estimated cost and row count 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 row count is only 2 and
the run time 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.

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
`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. `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 row count for the inner child plan node can be significantly
larger than the number of rows that are actually in the inner relation.

BitmapAnd and BitmapOr nodes always report their actual row counts as
zero, due to implementation limitations.

Normally, `EXPLAIN` will display every plan node created by the planner.
However, there are cases where the executor can determine that certain
nodes need not be executed because they cannot produce any rows, based
on parameter values that were not available at planning time. (Currently
this can only happen for child nodes of an Append or MergeAppend node
that is scanning a partitioned table.) When this happens, those plan
nodes are omitted from the `EXPLAIN` output and a `Subplans
    Removed: N` annotation appears instead.

Statistics Used by the Planner {#planner-stats}
==============================

statistics

of the planner

Single-Column Statistics
------------------------

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.

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
[pg\_class](#catalog-pg-class), in the columns reltuples and relpages.
We can look at it with queries similar to this one:

    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)

Here we can see that tenk1 contains 10000 rows, as do its indexes, but
the indexes are (unsurprisingly) much smaller than the table.

For efficiency reasons, reltuples and relpages are not updated
on-the-fly, and so they usually contain somewhat out-of-date values.
They are updated by `VACUUM`, `ANALYZE`, and a few DDL commands such as
`CREATE INDEX`. A `VACUUM` or `ANALYZE` operation that does not scan the
entire table (which is commonly the case) will incrementally update the
reltuples 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 pg\_class to match the current physical table
size, thus obtaining a closer approximation.

pg\_statistic

Most queries retrieve only a fraction of the rows in a table, due to
`WHERE` clauses that restrict the rows to be examined. The planner thus
needs to make an estimate of the selectivity of `WHERE` clauses, that
is, the fraction of rows that match each condition in the `WHERE`
clause. The information used for this task is stored in the
[pg\_statistic](#catalog-pg-statistic) system catalog. Entries in
pg\_statistic are updated by the `ANALYZE` and `VACUUM
   ANALYZE` commands, and are always approximate even when freshly
updated.

pg\_stats

Rather than look at pg\_statistic directly, it\'s better to look at its
view [pg\_stats](#view-pg-stats) when examining the statistics manually.
pg\_stats is designed to be more easily readable. Furthermore, pg\_stats
is readable by all, whereas pg\_statistic 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
pg\_stats view is restricted to show only rows about tables that the
current user can read.) For example, we might do:

    SELECT attname, inherited, n_distinct,
           array_to_string(most_common_vals, E'\n') as most_common_vals
    FROM pg_stats
    WHERE tablename = 'road';

     attname | inherited | n_distinct |          most_common_vals
    ---------+-----------+------------+------------------------------------
     name    | f         |  -0.363388 | I- 580                        Ramp+
             |           |            | I- 880                        Ramp+
             |           |            | Sp Railroad                       +
             |           |            | I- 580                            +
             |           |            | I- 680                        Ramp
     name    | t         |  -0.284859 | I- 880                        Ramp+
             |           |            | I- 580                        Ramp+
             |           |            | I- 680                        Ramp+
             |           |            | I- 580                            +
             |           |            | State Hwy 13                  Ramp
    (2 rows)

Note that two rows are displayed for the same column, one corresponding
to the complete inheritance hierarchy starting at the `road` table
(`inherited`=`t`), and another one including only the `road` table
itself (`inherited`=`f`).

The amount of information stored in pg\_statistic by `ANALYZE`, in
particular the maximum number of entries in the most\_common\_vals and
histogram\_bounds arrays for each column, can be set on a
column-by-column basis using the `ALTER TABLE SET STATISTICS` command,
or globally by setting the [???](#guc-default-statistics-target)
configuration variable. The default limit is presently 100 entries.
Raising the limit might allow more accurate planner estimates to be
made, particularly for columns with irregular data distributions, at the
price of consuming more space in pg\_statistic and slightly more time to
compute the estimates. Conversely, a lower limit might be sufficient for
columns with simple data distributions.

Further details about the planner\'s use of statistics can be found in
[???](#planner-stats-details).

Extended Statistics {#planner-stats-extended}
-------------------

statistics

of the planner

correlation

in the query planner

pg\_statistic\_ext

pg\_statistic\_ext\_data

It is common to see slow queries running bad execution plans because
multiple columns used in the query clauses are correlated. The planner
normally assumes that multiple conditions are independent of each other,
an assumption that does not hold when column values are correlated.
Regular statistics, because of their per-individual-column nature,
cannot capture any knowledge about cross-column correlation. However,
PostgreSQL has the ability to compute multivariate statistics, which can
capture such information.

Because the number of possible column combinations is very large, it\'s
impractical to compute multivariate statistics automatically. Instead,
extended statistics objects, more often called just statistics objects,
can be created to instruct the server to obtain statistics across
interesting sets of columns.

Statistics objects are created using the
[`CREATE STATISTICS`](#sql-createstatistics) command. Creation of such
an object merely creates a catalog entry expressing interest in the
statistics. Actual data collection is performed by `ANALYZE` (either a
manual command, or background auto-analyze). The collected values can be
examined in the
[pg\_statistic\_ext\_data](#catalog-pg-statistic-ext-data) catalog.

`ANALYZE` computes extended statistics based on the same sample of table
rows that it takes for computing regular single-column statistics. Since
the sample size is increased by increasing the statistics target for the
table or any of its columns (as described in the previous section), a
larger statistics target will normally result in more accurate extended
statistics, as well as more time spent calculating them.

The following subsections describe the kinds of extended statistics that
are currently supported.

### Functional Dependencies

The simplest kind of extended statistics tracks functional dependencies,
a concept used in definitions of database normal forms. We say that
column b is functionally dependent on column a if knowledge of the value
of a is sufficient to determine the value of b, that is there are no two
rows having the same value of a but different values of b. In a fully
normalized database, functional dependencies should exist only on
primary keys and superkeys. However, in practice many data sets are not
fully normalized for various reasons; intentional denormalization for
performance reasons is a common example. Even in a fully normalized
database, there may be partial correlation between some columns, which
can be expressed as partial functional dependency.

The existence of functional dependencies directly affects the accuracy
of estimates in certain queries. If a query contains conditions on both
the independent and the dependent column(s), the conditions on the
dependent columns do not further reduce the result size; but without
knowledge of the functional dependency, the query planner will assume
that the conditions are independent, resulting in underestimating the
result size.

To inform the planner about functional dependencies, `ANALYZE` can
collect measurements of cross-column dependency. Assessing the degree of
dependency between all sets of columns would be prohibitively expensive,
so data collection is limited to those groups of columns appearing
together in a statistics object defined with the `dependencies` option.
It is advisable to create `dependencies` statistics only for column
groups that are strongly correlated, to avoid unnecessary overhead in
both `ANALYZE` and later query planning.

Here is an example of collecting functional-dependency statistics:

    CREATE STATISTICS stts (dependencies) ON city, zip FROM zipcodes;

    ANALYZE zipcodes;

    SELECT stxname, stxkeys, stxddependencies
      FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid)
      WHERE stxname = 'stts';
     stxname | stxkeys |             stxddependencies             
    ---------+---------+------------------------------------------
     stts    | 1 5     | {"1 => 5": 1.000000, "5 => 1": 0.423130}
    (1 row)

Here it can be seen that column 1 (zip code) fully determines column 5
(city) so the coefficient is 1.0, while city only determines zip code
about 42% of the time, meaning that there are many cities (58%) that are
represented by more than a single ZIP code.

When computing the selectivity for a query involving functionally
dependent columns, the planner adjusts the per-condition selectivity
estimates using the dependency coefficients so as not to produce an
underestimate.

#### Limitations of Functional Dependencies

Functional dependencies are currently only applied when considering
simple equality conditions that compare columns to constant values, and
`IN` clauses with constant values. They are not used to improve
estimates for equality conditions comparing two columns or comparing a
column to an expression, nor for range clauses, `LIKE` or any other type
of condition.

When estimating with functional dependencies, the planner assumes that
conditions on the involved columns are compatible and hence redundant.
If they are incompatible, the correct estimate would be zero rows, but
that possibility is not considered. For example, given a query like

    SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '94105';

the planner will disregard the city clause as not changing the
selectivity, which is correct. However, it will make the same assumption
about

    SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '90210';

even though there will really be zero rows satisfying this query.
Functional dependency statistics do not provide enough information to
conclude that, however.

In many practical situations, this assumption is usually satisfied; for
example, there might be a GUI in the application that only allows
selecting compatible city and ZIP code values to use in a query. But if
that\'s not the case, functional dependencies may not be a viable
option.

### Multivariate N-Distinct Counts

Single-column statistics store the number of distinct values in each
column. Estimates of the number of distinct values when combining more
than one column (for example, for `GROUP BY a, b`) are frequently wrong
when the planner only has single-column statistical data, causing it to
select bad plans.

To improve such estimates, `ANALYZE` can collect n-distinct statistics
for groups of columns. As before, it\'s impractical to do this for every
possible column grouping, so data is collected only for those groups of
columns appearing together in a statistics object defined with the
`ndistinct` option. Data will be collected for each possible combination
of two or more columns from the set of listed columns.

Continuing the previous example, the n-distinct counts in a table of ZIP
codes might look like the following:

    CREATE STATISTICS stts2 (ndistinct) ON city, state, zip FROM zipcodes;

    ANALYZE zipcodes;

    SELECT stxkeys AS k, stxdndistinct AS nd
      FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid)
      WHERE stxname = 'stts2';
    -[ RECORD 1 ]------------------------------------------------------ZWSP--
    k  | 1 2 5
    nd | {"1, 2": 33178, "1, 5": 33178, "2, 5": 27435, "1, 2, 5": 33178}
    (1 row)

This indicates that there are three combinations of columns that have
33178 distinct values: ZIP code and state; ZIP code and city; and ZIP
code, city and state (the fact that they are all equal is expected given
that ZIP code alone is unique in this table). On the other hand, the
combination of city and state has only 27435 distinct values.

It\'s advisable to create `ndistinct` statistics objects only on
combinations of columns that are actually used for grouping, and for
which misestimation of the number of groups is resulting in bad plans.
Otherwise, the `ANALYZE` cycles are just wasted.

### Multivariate MCV Lists

Another type of statistic stored for each column are most-common value
lists. This allows very accurate estimates for individual columns, but
may result in significant misestimates for queries with conditions on
multiple columns.

To improve such estimates, `ANALYZE` can collect MCV lists on
combinations of columns. Similarly to functional dependencies and
n-distinct coefficients, it\'s impractical to do this for every possible
column grouping. Even more so in this case, as the MCV list (unlike
functional dependencies and n-distinct coefficients) does store the
common column values. So data is collected only for those groups of
columns appearing together in a statistics object defined with the `mcv`
option.

Continuing the previous example, the MCV list for a table of ZIP codes
might look like the following (unlike for simpler types of statistics, a
function is required for inspection of MCV contents):

    CREATE STATISTICS stts3 (mcv) ON city, state FROM zipcodes;

    ANALYZE zipcodes;

    SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid),
                    pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts3';

     index |         values         | nulls | frequency | base_frequency 
    -------+------------------------+-------+-----------+----------------
         0 | {Washington, DC}       | {f,f} |  0.003467 |        2.7e-05
         1 | {Apo, AE}              | {f,f} |  0.003067 |        1.9e-05
         2 | {Houston, TX}          | {f,f} |  0.002167 |       0.000133
         3 | {El Paso, TX}          | {f,f} |     0.002 |       0.000113
         4 | {New York, NY}         | {f,f} |  0.001967 |       0.000114
         5 | {Atlanta, GA}          | {f,f} |  0.001633 |        3.3e-05
         6 | {Sacramento, CA}       | {f,f} |  0.001433 |        7.8e-05
         7 | {Miami, FL}            | {f,f} |    0.0014 |          6e-05
         8 | {Dallas, TX}           | {f,f} |  0.001367 |        8.8e-05
         9 | {Chicago, IL}          | {f,f} |  0.001333 |        5.1e-05
       ...
    (99 rows)

This indicates that the most common combination of city and state is
Washington in DC, with actual frequency (in the sample) about 0.35%. The
base frequency of the combination (as computed from the simple
per-column frequencies) is only 0.0027%, resulting in two orders of
magnitude under-estimates.

It\'s advisable to create MCV statistics objects only on combinations of
columns that are actually used in conditions together, and for which
misestimation of the number of groups is resulting in bad plans.
Otherwise, the `ANALYZE` and planning cycles are just wasted.

Controlling the Planner with Explicit `JOIN` Clauses {#explicit-joins}
====================================================

join

controlling the order

It is possible to control the query planner to some extent by using the
explicit `JOIN` syntax. To see why this matters, we first need some
background.

In a simple join query, such as:

    SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;

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`
condition `a.id = b.id`, and then joins C to this joined table, using
the other `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
`WHERE` clause to allow optimization of the join. (All joins in the
PostgreSQL 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.

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
PostgreSQL planner will switch from exhaustive search to a genetic
probabilistic search through a limited number of possibilities. (The
switch-over threshold is set by the [???](#guc-geqo-threshold) run-time
parameter.) The genetic search takes less time, but it won\'t
necessarily find the best possible plan.

When the query involves outer joins, the planner has less freedom than
it does for plain (inner) joins. For example, consider:

    SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);

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:

    SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id);

it is valid to join A to either B or C first. Currently, only
`FULL JOIN` completely constrains the join order. Most practical cases
involving `LEFT JOIN` or `RIGHT JOIN` can be rearranged to some extent.

Explicit inner join syntax (`INNER JOIN`, `CROSS
   JOIN`, or unadorned `JOIN`) is semantically the same as listing the
input relations in `FROM`, so it does not constrain the join order.

Even though most kinds of `JOIN` don\'t completely constrain the join
order, it is possible to instruct the PostgreSQL query planner to treat
all `JOIN` clauses as constraining the join order anyway. For example,
these three queries are logically equivalent:

    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);

But if we tell the planner to honor the `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.

To force the planner to follow the join order laid out by explicit
`JOIN`s, set the [???](#guc-join-collapse-limit) run-time parameter to
1. (Other possible values are discussed below.)

You do not need to constrain the join order completely in order to cut
search time, because it\'s OK to use `JOIN` operators within items of a
plain `FROM` list. For example, consider:

    SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...;

With `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.

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 MDASH assuming that
you know of a better order, that is. Experimentation is recommended.

A closely related issue that affects planning time is collapsing of
subqueries into their parent query. For example, consider:

    SELECT *
    FROM x, y,
        (SELECT * FROM a, b, c WHERE something) AS ss
    WHERE somethingelse;

This situation might arise from use of a view that contains a join; the
view\'s `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:

    SELECT * FROM x, y, a, b, c WHERE something AND somethingelse;

This usually results in a better plan than planning the subquery
separately. (For example, the outer `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 `from_collapse_limit`
`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.

[???](#guc-from-collapse-limit) and [???](#guc-join-collapse-limit) are
similarly named because they do almost the same thing: one controls when
the planner will "flatten out" subqueries, and the other controls when
it will flatten out explicit joins. Typically you would either set
`join_collapse_limit` equal to `from_collapse_limit` (so that explicit
joins and subqueries act similarly) or set `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.

Populating a Database {#populate}
=====================

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.

Disable Autocommit
------------------

autocommit

bulk-loading data

When using multiple `INSERT`s, turn off autocommit and just do one
commit at the end. (In plain SQL, this means issuing `BEGIN` at the
start and `COMMIT` 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, PostgreSQL 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.

Use `COPY` {#populate-copy-from}
----------

Use [`COPY`](#sql-copy) to load all the rows in one command, instead of
using a series of `INSERT` commands. The `COPY` command is optimized for
loading large numbers of rows; it is less flexible than `INSERT`, but
incurs significantly less overhead for large data loads. Since `COPY` is
a single command, there is no need to disable autocommit if you use this
method to populate a table.

If you cannot use `COPY`, it might help to use [`PREPARE`](#sql-prepare)
to create a prepared `INSERT` statement, and then use `EXECUTE` as many
times as required. This avoids some of the overhead of repeatedly
parsing and planning `INSERT`. Different interfaces provide this
facility in different ways; look for "prepared statements" in the
interface documentation.

Note that loading a large number of rows using `COPY` is almost always
faster than using `INSERT`, even if `PREPARE` is used and multiple
insertions are batched into a single transaction.

`COPY` is fastest when used within the same transaction as an earlier
`CREATE TABLE` or `TRUNCATE` command. In such cases no WAL needs to be
written, because in case of an error, the files containing the newly
loaded data will be removed anyway. However, this consideration only
applies when [???](#guc-wal-level) is `minimal` as all commands must
write WAL otherwise.

Remove Indexes {#populate-rm-indexes}
--------------

If you are loading a freshly created table, the fastest method is to
create the table, bulk load the table\'s data using `COPY`, 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.

If you are adding large amounts of data to an existing table, it might
be a win to drop the indexes, load the table, and then recreate the
indexes. Of course, the database performance for other users might
suffer during the time the indexes are missing. One should also think
twice before dropping a unique index, since the error checking afforded
by the unique constraint will be lost while the index is missing.

Remove Foreign Key Constraints {#populate-rm-fkeys}
------------------------------

Just as with indexes, a foreign key constraint can be checked "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.

What\'s more, when you load data into a table with existing foreign key
constraints, each new row requires an entry in the server\'s list of
pending trigger events (since it is the firing of a trigger that checks
the row\'s foreign key constraint). Loading many millions of rows can
cause the trigger event queue to overflow available memory, leading to
intolerable swapping or even outright failure of the command. Therefore
it may be *necessary*, not just desirable, to drop and re-apply foreign
keys when loading large amounts of data. If temporarily removing the
constraint isn\'t acceptable, the only other recourse may be to split up
the load operation into smaller transactions.

Increase `maintenance_work_mem` {#populate-work-mem}
-------------------------------

Temporarily increasing the [???](#guc-maintenance-work-mem)
configuration variable when loading large amounts of data can lead to
improved performance. This will help to speed up `CREATE
    INDEX` commands and `ALTER TABLE ADD FOREIGN KEY` commands. It
won\'t do much for `COPY` itself, so this advice is only useful when you
are using one or both of the above techniques.

Increase `max_wal_size` {#populate-max-wal-size}
-----------------------

Temporarily increasing the [???](#guc-max-wal-size) configuration
variable can also make large data loads faster. This is because loading
a large amount of data into PostgreSQL will cause checkpoints to occur
more often than the normal checkpoint frequency (specified by the
`checkpoint_timeout` configuration variable). Whenever a checkpoint
occurs, all dirty pages must be flushed to disk. By increasing
`max_wal_size` temporarily during bulk data loads, the number of
checkpoints that are required can be reduced.

Disable WAL Archival and Streaming Replication {#populate-pitr}
----------------------------------------------

When loading large amounts of data into an installation that uses WAL
archiving or streaming replication, it might be faster to take a new
base backup after the load has completed than to process a large amount
of incremental WAL data. To prevent incremental WAL logging while
loading, disable archiving and streaming replication, by setting
[???](#guc-wal-level) to `minimal`, [???](#guc-archive-mode) to `off`,
and [???](#guc-max-wal-senders) to zero. But note that changing these
settings requires a server restart, and makes any base backups taken
before unavailable for archive recovery and standby server, which may
lead to data loss.

Aside from avoiding the time for the archiver or WAL sender to process
the WAL data, doing this will actually make certain commands faster,
because they do not to write WAL at all if `wal_level` is `minimal` and
the current subtransaction (or top-level transaction) created or
truncated the table or index they change. (They can guarantee crash
safety more cheaply by doing an `fsync` at the end than by writing WAL.)

Run `ANALYZE` Afterwards {#populate-analyze}
------------------------

Whenever you have significantly altered the distribution of data within
a table, running [`ANALYZE`](#sql-analyze) is strongly recommended. This
includes bulk loading large amounts of data into the table. Running
`ANALYZE` (or `VACUUM ANALYZE`) ensures that the planner has up-to-date
statistics about the table. With no statistics or obsolete statistics,
the planner might make poor decisions during query planning, leading to
poor performance on any tables with inaccurate or nonexistent
statistics. Note that if the autovacuum daemon is enabled, it might run
`ANALYZE` automatically; see [???](#vacuum-for-statistics) and
[???](#autovacuum) for more information.

Some Notes about pg\_dump {#populate-pg-dump}
-------------------------

Dump scripts generated by pg\_dump automatically apply several, but not
all, of the above guidelines. To reload a pg\_dump dump as quickly as
possible, you need to do a few extra things manually. (Note that these
points apply while *restoring* a dump, not while *creating* it. The same
points apply whether loading a text dump with psql or using pg\_restore
to load from a pg\_dump archive file.)

By default, pg\_dump uses `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:

-   Set appropriate (i.e., larger than normal) values for
    `maintenance_work_mem` and `max_wal_size`.

-   If using WAL archiving or streaming replication, consider disabling
    them during the restore. To do that, set `archive_mode` to `off`,
    `wal_level` to `minimal`, and `max_wal_senders` to zero before
    loading the dump. Afterwards, set them back to the right values and
    take a fresh base backup.

-   Experiment with the parallel dump and restore modes of both pg\_dump
    and pg\_restore and find the optimal number of concurrent jobs to
    use. Dumping and restoring in parallel by means of the `-j` option
    should give you a significantly higher performance over the serial
    mode.

-   Consider whether the whole dump should be restored as a single
    transaction. To do that, pass the `-1` or `--single-transaction`
    command-line option to psql or 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. `COPY` commands will run fastest if you use a
    single transaction and have WAL archiving turned off.

-   If multiple CPUs are available in the database server, consider
    using pg\_restore\'s `--jobs` option. This allows concurrent data
    loading and index creation.

-   Run `ANALYZE` afterwards.

A data-only dump will still use `COPY`, but it does not drop or recreate
indexes, and it does not normally touch foreign keys. [^1] 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 `max_wal_size` while loading the data, but don\'t bother
increasing `maintenance_work_mem`; rather, you\'d do that while manually
recreating indexes and foreign keys afterwards. And don\'t forget to
`ANALYZE` when you\'re done; see [???](#vacuum-for-statistics) and
[???](#autovacuum) for more information.

Non-Durable Settings {#non-durability}
====================

non-durable

Durability is a database feature that guarantees the recording of
committed transactions even if the server crashes or loses power.
However, durability adds significant database overhead, so if your site
does not require such a guarantee, PostgreSQL can be configured to run
much faster. The following are configuration changes you can make to
improve performance in such cases. Except as noted below, durability is
still guaranteed in case of a crash of the database software; only an
abrupt operating system crash creates a risk of data loss or corruption
when these settings are used.

-   Place the database cluster\'s data directory in a memory-backed file
    system (i.e., RAM disk). This eliminates all database disk I/O, but
    limits data storage to the amount of available memory (and perhaps
    swap).

-   Turn off [???](#guc-fsync); there is no need to flush data to disk.

-   Turn off [???](#guc-synchronous-commit); there might be no need to
    force WAL writes to disk on every commit. This setting does risk
    transaction loss (though not data corruption) in case of a crash of
    the *database*.

-   Turn off [???](#guc-full-page-writes); there is no need to guard
    against partial page writes.

-   Increase [???](#guc-max-wal-size) and
    [???](#guc-checkpoint-timeout); this reduces the frequency of
    checkpoints, but increases the storage requirements of `/pg_wal`.

-   Create [unlogged tables](#sql-createtable-unlogged) to avoid WAL
    writes, though it makes the tables non-crash-safe.

[^1]: You can get the effect of disabling foreign keys by using the
    `--disable-triggers` option MDASH but realize that that eliminates,
    rather than just postpones, foreign key validation, and so it is
    possible to insert bad data if you use it.
