linkend="guc-cpu-tuple-cost"> which is
- currently 0.01 (try SHOW cpu_tuple_cost).
+ typically 0.01 (try SHOW cpu_tuple_cost).
Now let's modify the query to add a WHERE condition:
-EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 1000;
+EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000;
QUERY PLAN
------------------------------------------------------------
- Seq Scan on tenk1 (cost=0.00..358.00 rows=1033 width=148)
- Filter: (unique1 < 1000)
+ Seq Scan on tenk1 (cost=0.00..483.00 rows=7033 width=244)
+ Filter: (unique1 < 7000)
- The estimate of output rows has gone down because of the WHERE clause.
+ Notice that the EXPLAIN output shows the WHERE
+ clause being applied as a filter condition; this means that
+ the plan node checks the condition for each row it scans, and outputs
+ only the ones that pass the condition.
+ The estimate of output rows has gone down because of the WHERE
+ clause.
However, the scan will still have to visit all 10000 rows, so the cost
hasn't decreased; in fact it has gone up a bit to reflect the extra CPU
time spent checking the WHERE condition.
- The actual number of rows this query would select is 1000, but the
+ The actual number of rows this query would select is 7000, but the rows
estimate is only approximate. If you try to duplicate this experiment,
you will probably get a slightly different estimate; moreover, it will
change after each ANALYZE command, because the
- Modify the query to restrict the condition even more:
+ Now, let's make the condition more restrictive:
-EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 50;
+EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100;
- QUERY PLAN
--------------------------------------------------------------------------------
- Index Scan using tenk1_unique1 on tenk1 (cost=0.00..179.33 rows=49 width=148)
- Index Cond: (unique1 < 50)
+ QUERY PLAN
+------------------------------------------------------------------------------
+ Bitmap Heap Scan on tenk1 (cost=2.37..232.35 rows=106 width=244)
+ Recheck Cond: (unique1 < 100)
+ -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
+ Index Cond: (unique1 < 100)
- and you will see that if we make the WHERE condition selective
- enough, the planner will
- eventually decide that an index scan is cheaper than a sequential scan.
- This plan will only have to visit 50 rows because of the index,
- so it wins despite the fact that each individual fetch is more expensive
- than reading a whole disk page sequentially.
+ Here the planner has decided to use a two-step plan: the bottom plan
+ node visits an index to find the locations of rows matching the index
+ condition, and then the upper plan node actually fetches those rows
+ from the table itself. Fetching the rows separately is much more
+ expensive than sequentially reading them, but because not all the pages
+ of the table have to be visited, this is still cheaper than a sequential
+ scan. (The reason for using two levels of plan is that the upper plan
+ node sorts the row locations identified by the index into physical order
+ before reading them, so as to minimize the costs of the separate fetches.
+ The bitmap mentioned in the node names is the mechanism that
+ does the sorting.)
+
+
+ If the WHERE condition is selective enough, the planner may
+ switch to a simple index scan plan:
+
+EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 3;
+
+ QUERY PLAN
+------------------------------------------------------------------------------
+ Index Scan using tenk1_unique1 on tenk1 (cost=0.00..10.00 rows=2 width=244)
+ Index Cond: (unique1 < 3)
+
+
+ In this case the table rows are fetched in index order, which makes them
+ even more expensive to read, but there are so few that the extra cost
+ of sorting the row locations is not worth it. You'll most often see
+ this plan type for queries that fetch just a single row, and for queries
+ that request an ORDER BY condition that matches the index
+ order.
Add another condition to the WHERE clause:
-EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 50 AND stringu1 = 'xxx';
+EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 3 AND stringu1 = 'xxx';
QUERY PLAN
--------------------------------------------------------------------------------
- Index Scan using tenk1_unique1 on tenk1 (cost=0.00..179.45 rows=1 width=148)
- Index Cond: (unique1 < 50)
+------------------------------------------------------------------------------
+ Index Scan using tenk1_unique1 on tenk1 (cost=0.00..10.01 rows=1 width=244)
+ Index Cond: (unique1 < 3)
Filter: (stringu1 = 'xxx'::name)
a little bit to reflect this extra checking.
+ If there are indexes on several columns used 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=11.27..49.11 rows=11 width=244)
+ Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))
+ -> BitmapAnd (cost=11.27..11.27 rows=11 width=0)
+ -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
+ Index Cond: (unique1 < 100)
+ -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..8.65 rows=1042 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.
+
+
Let's try joining two tables, using the columns we have been discussing:
-EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 50 AND t1.unique2 = t2.unique2;
-
- QUERY PLAN
-----------------------------------------------------------------------------
- Nested Loop (cost=0.00..327.02 rows=49 width=296)
- -> Index Scan using tenk1_unique1 on tenk1 t1
- (cost=0.00..179.33 rows=49 width=148)
- Index Cond: (unique1 < 50)
- -> Index Scan using tenk2_unique2 on tenk2 t2
- (cost=0.00..3.01 rows=1 width=148)
+EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;
+
+ QUERY PLAN
+--------------------------------------------------------------------------------------
+ Nested Loop (cost=2.37..553.11 rows=106 width=488)
+ -> Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244)
+ Recheck Cond: (unique1 < 100)
+ -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
+ Index Cond: (unique1 < 100)
+ -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..3.01 rows=1 width=244)
Index Cond: ("outer".unique2 = t2.unique2)
- In this nested-loop join, the outer scan is the same index scan we had
- in the example before last, and so its cost and row count are the same
- because we are applying the WHERE clause unique1 < 50 at that node.
- The t1.unique2 = t2.unique2 clause is not relevant yet, so it doesn't
- affect row count of the outer scan. For the inner scan, the unique2 value of the
- current
- outer-scan row is plugged into the inner index scan
- to produce an index condition like
- t2.unique2 = constant. So we get the
- same inner-scan plan and costs that we'd get from, say, EXPLAIN SELECT
- * FROM tenk2 WHERE unique2 = 42. 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 (49 * 3.01, here), plus a little CPU
- time for join processing.
+ In this nested-loop join, the outer scan is the same bitmap index scan we
+ saw earlier, and so its cost and row count are the same because we are
+ applying the WHERE clause unique1 < 100
+ at that node.
+ The t1.unique2 = t2.unique2 clause is not relevant yet,
+ so it doesn't affect row count of the outer scan. For the inner scan, the
+ unique2 value of the current outer-scan row is plugged into
+ the inner index scan to produce an index condition like
+ t2.unique2 = constant.
+ So we get the same inner-scan plan and costs that we'd get from, say,
+ EXPLAIN SELECT * FROM tenk2 WHERE unique2 = 42. The
+ costs of the loop node are then set on the basis of the cost of the outer
+ scan, plus one repetition of the inner scan for each outer row (106 * 3.01,
+ here), plus a little CPU time for join processing.
In this example the join's output row count is the same as the product
of the two scans' row counts, but that's not true in general, because
- in general you can have WHERE clauses that mention both tables and
- so can only be applied at the join point, not to either input scan.
- For example, if we added WHERE ... AND t1.hundred < t2.hundred,
+ in general you can have WHERE clauses that mention both tables
+ and so can only be applied at the join point, not to either input scan.
+ For example, if we added
+ WHERE ... AND t1.hundred < t2.hundred,
that would decrease the output row count of the join node, but not change
either input scan.
One way to look at variant plans is to force the planner to disregard
whatever strategy it thought was the winner, using the enable/disable
- flags for each plan type. (This is a crude tool, but useful. See
+ flags described in .
+ (This is a crude tool, but useful. See
also .)
SET enable_nestloop = off;
-EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 50 AND t1.unique2 = t2.unique2;
+EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;
- QUERY PLAN
---------------------------------------------------------------------------
- Hash Join (cost=179.45..563.06 rows=49 width=296)
+ QUERY PLAN
+------------------------------------------------------------------------------------------
+ Hash Join (cost=232.61..741.67 rows=106 width=488)
Hash Cond: ("outer".unique2 = "inner".unique2)
- -> Seq Scan on tenk2 t2 (cost=0.00..333.00 rows=10000 width=148)
- -> Hash (cost=179.33..179.33 rows=49 width=148)
- -> Index Scan using tenk1_unique1 on tenk1 t1
- (cost=0.00..179.33 rows=49 width=148)
- Index Cond: (unique1 < 50)
+ -> Seq Scan on tenk2 t2 (cost=0.00..458.00 rows=10000 width=244)
+ -> Hash (cost=232.35..232.35 rows=106 width=244)
+ -> Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244)
+ Recheck Cond: (unique1 < 100)
+ -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0)
+ Index Cond: (unique1 < 100)
- This plan proposes to extract the 50 interesting rows of tenk1
- using ye same olde index scan, stash them into an in-memory hash table,
+ This plan proposes to extract the 100 interesting rows of tenk1
+ using that same old index scan, stash them into an in-memory hash table,
and then do a sequential scan of tenk2, probing into the hash table
for possible matches of t1.unique2 = t2.unique2 at each tenk2 row.
The cost to read tenk1 and set up the hash table is entirely start-up
cost for the hash join, since we won't get any rows out until we can
start reading tenk2. The total time estimate for the join also
includes a hefty charge for the CPU time to probe the hash table
- 10000 times. Note, however, that we are not charging 10000 times 179.33;
+ 10000 times. Note, however, that we are not charging 10000 times 232.35;
the hash table setup is only done once in this plan type.
For example, we might get a result like this:
-EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 50 AND t1.unique2 = t2.unique2;
-
- QUERY PLAN
--------------------------------------------------------------------------------
- Nested Loop (cost=0.00..327.02 rows=49 width=296)
- (actual time=1.181..29.822 rows=50 loops=1)
- -> Index Scan using tenk1_unique1 on tenk1 t1
- (cost=0.00..179.33 rows=49 width=148)
- (actual time=0.630..8.917 rows=50 loops=1)
- Index Cond: (unique1 < 50)
- -> Index Scan using tenk2_unique2 on tenk2 t2
- (cost=0.00..3.01 rows=1 width=148)
- (actual time=0.295..0.324 rows=1 loops=50)
+EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;
+
+ QUERY PLAN
+----------------------------------------------------------------------------------------------------------------------------------
+ Nested Loop (cost=2.37..553.11 rows=106 width=488) (actual time=1.392..12.700 rows=100 loops=1)
+ -> Bitmap Heap Scan on tenk1 t1 (cost=2.37..232.35 rows=106 width=244) (actual time=0.878..2.367 rows=100 loops=1)
+ Recheck Cond: (unique1 < 100)
+ -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..2.37 rows=106 width=0) (actual time=0.546..0.546 rows=100 loops=1)
+ Index Cond: (unique1 < 100)
+ -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.00..3.01 rows=1 width=244) (actual time=0.067..0.078 rows=1 loops=100)
Index Cond: ("outer".unique2 = t2.unique2)
- Total runtime: 31.604 ms
+ Total runtime: 14.452 ms
Note that the actual time
values are in milliseconds of
SELECT relname, relkind, reltuples, relpages FROM pg_class WHERE relname LIKE 'tenk1%';
- relname | relkind | reltuples | relpages
----------------+---------+-----------+----------
- tenk1 | r | 10000 | 233
- tenk1_hundred | i | 10000 | 30
- tenk1_unique1 | i | 10000 | 30
- tenk1_unique2 | i | 10000 | 30
-(4 rows)
+ 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
stored in the pg_statistic
system catalog. Entries in pg_statistic
are updated by the ANALYZE and VACUUM
- ANALYZE commands and are always approximate even when freshly
+ ANALYZE commands, and are always approximate even when freshly
updated.
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, but that
+ Or it could join A to C and then join them with B — but that
would be inefficient, since the full Cartesian product of A and C
would have to be formed, there being no applicable condition in the
WHERE clause to allow optimization of the join. (All
- If you are augmenting an existing table, you can drop the index,
- load the table, and then recreate the index. Of course, the
+ If you are adding large amounts of data to an existing table,
+ it may be a win to drop the index,
+ load the table, and then recreate the index. Of course, the
database performance for other users may be adversely affected
during the time that the index is missing. One should also think
twice before dropping unique indexes, since the error checking
+
+
Remove Foreign Key Constraints
+
+ Just as with indexes, a foreign key constraint can be checked
+ in bulk more efficiently than row-by-row. So it may be
+ useful to drop foreign key constraints, load data, and re-create
+ the constraints. Again, there is a tradeoff between data load
+ speed and loss of error checking while the constraint is missing.
+
+
+
Increase <varname>maintenance_work_mem</varname>
Temporarily increasing the
configuration variable when loading large amounts of data can
- lead to improved performance. This is because when a B-tree index
- is created from scratch, the existing content of the table needs
- to be sorted. Allowing the merge sort to use more memory
- means that fewer merge passes will be required. A larger setting for
- maintenance_work_mem may also speed up validation
- of foreign-key constraints.
+ 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.
Temporarily increasing the
linkend="guc-checkpoint-segments"> configuration variable can also
make large data loads faster. This is because loading a large
- amount of data into
PostgreSQL can+ 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
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