## Chapter 14. Performance Tips

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.

## 14.1. Using `EXPLAIN`

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 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.

### 14.1.1. `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 Section 19.7.2). Traditional practice is to measure the costs in units of disk page fetches; that is, 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 * seq_page_cost) + (rows scanned * 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 * 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
    -------------------------------------------------------------------​-----------
     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
    -------------------------------------------------------------------​-----------
     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
    -------------------------------------------------------------------​----------
     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
    -------------------------------------------------------------------​-----------------------------------
     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
    -------------------------------------------------------------------​------------------
     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
    -------------------------------------------------------------------​------------------
     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
    -------------------------------------------------------------------​-------------------
     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
    -------------------------------------------------------------------​--------------------------
     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
    -------------------------------------------------------------------​-----------------------
     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
    -------------------------------------------------------------------​-----------------------
     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 Section 19.7.1. (This is a crude tool, but useful. See also Section 14.3.) 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
    -------------------------------------------------------------------​-----------------------
     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.

### 14.1.2. `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
    -------------------------------------------------------------------​--------------------------------------------------------------
     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
    -------------------------------------------------------------------​-------------------------------------------------------------------​------
     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
    -------------------------------------------------------------------​--------------------------------------
     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
    -------------------------------------------------------------------​-----------------------------------
     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
    -------------------------------------------------------------------​-------------------------------------------------------
     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
    -------------------------------------------------------------------​--------------------------------------------------------------
     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
    -------------------------------------------------------------------​-------------------------------------------------------------
     Update on tenk1  (cost=5.07..229.46 rows=101 width=250) (actual time=14.628..14.628 rows=0 loops=1)
       ->  Bitmap Heap Scan on tenk1  (cost=5.07..229.46 rows=101 width=250) (actual time=0.101..0.439 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=0.043..0.043 rows=100 loops=1)
                   Index Cond: (unique1 < 100)
     Planning time: 0.079 ms
     Execution time: 14.727 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
    -------------------------------------------------------------------​----------------
     Update on parent  (cost=0.00..24.53 rows=4 width=14)
       Update on parent
       Update on child1
       Update on child2
       Update on child3
       ->  Seq Scan on parent  (cost=0.00..0.00 rows=1 width=14)
             Filter: (f1 = 101)
       ->  Index Scan using child1_f1_key on child1  (cost=0.15..8.17 rows=1 width=14)
             Index Cond: (f1 = 101)
       ->  Index Scan using child2_f1_key on child2  (cost=0.15..8.17 rows=1 width=14)
             Index Cond: (f1 = 101)
       ->  Index Scan using child3_f1_key on child3  (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. (These annotations are new as of PostgreSQL 9.5; in prior versions the reader had to intuit the target tables by inspecting the 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`.

### 14.1.3. 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 pg_test_timing 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
    -------------------------------------------------------------------​------------------------------------------------------------
     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.