9.1第十四章

=== 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 deserves an extensive tutorial, which this is not; but here is some basic information.
The structure of a query plan is a tree of plan nodes. Nodes at the bottom level of the tree are table scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes 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. The first line (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: [1]
EXPLAIN SELECT * FROM tenk1;
 QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244)
The numbers that are quoted by EXPLAIN are (left to right):
Estimated start-up cost (time expended before the output scan can start, e.g., time to do the sorting in a sort node)
Estimated total cost (if all rows are retrieved, though they might not be; e.g., a query with a LIMIT clause will stop short of paying the total cost of the Limit plan node's input node)
Estimated number of rows output by this plan node (again, only if executed to completion)
Estimated average width (in bytes) of rows output by this plan node
The costs are measured in arbitrary units determined by the planner's cost parameters (see Section 18.7.2). 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 note that the cost of an upper-level node includes the cost of all its child nodes. It's also important to realize that the cost only reflects things that the planner cares about. In particular, the cost does not consider the time spent transmitting result rows to the client, which could be an important factor in the 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. It is usually less, reflecting the estimated selectivity of 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)
This is about as straightforward as it gets. 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 original 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=7033 width=244)
 Filter: (unique1 < 7000)
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 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 will 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=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)
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 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.)
If the WHERE condition is selective enough, the planner might 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 have an ORDER BY condition that matches the index order.
Add another condition to the WHERE clause:
EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 3 AND stringu1 = 'xxx';
 QUERY PLAN
------------------------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1 (cost=0.00..10.01 rows=1 width=244)
 Index Cond: (unique1 < 3)
 Filter: (stringu1 = 'xxx'::name)
The added condition stringu1 = 'xxx' reduces the output-rows 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.
If there are indexes on several 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=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 < 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: (unique2 = t1.unique2)
In this nested-loop join, the outer (upper) 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 the row count of the outer scan. For the inner (lower) scan, the unique2 value of the current outer-scan row is plugged into the inner index scan to produce an index condition like 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 all cases because 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 cheapest, using the enable/disable flags described in Section 18.7.1. (This is a crude tool, but useful. See also Section 14.3.)
SET enable_nestloop = off;
EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;
 QUERY PLAN
------------------------------------------------------------------------------------------
 Hash Join (cost=232.61..741.67 rows=106 width=488)
 Hash Cond: (t2.unique2 = t1.unique2)
 -> 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 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 for each tenk2 row. The cost to read tenk1 and set up the hash table is a start-up cost for the hash join, since there will be no output 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 232.35; the hash table setup is only done once in this plan type.
It is possible to check the accuracy of the planner's estimated costs by using EXPLAIN ANALYZE. This command actually executes the query, and then displays the true run time accumulated within each plan node along with the same estimated costs that a plain EXPLAIN shows. For example, we might get a result like this:
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: (unique2 = t1.unique2)
 Total runtime: 14.452 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 to pay attention to is whether the ratios of actual time and estimated costs are consistent.
In some query plans, it is possible for a subplan node to be executed more than once. For example, the inner index scan is executed once per outer row in the above nested-loop plan. In such cases, the 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.
The Total runtime shown by EXPLAIN ANALYZE includes executor start-up and shut-down time, but not parsing, rewriting, or planning time. For INSERT, UPDATE, and DELETE commands, the time spent applying the table changes is charged to a top-level Insert, Update, or Delete plan node. (The plan nodes underneath this node represent the work of locating the old rows and/or computing the new ones.) Time spent executing BEFORE triggers, if any, is charged to the related Insert, Update, or Delete node, although time spent executing AFTER triggers is not. The time spent in each trigger (either BEFORE or AFTER) is also shown separately and is included in total run time. Note, however, that deferred constraint triggers will not be executed until end of transaction and are thus not shown by EXPLAIN ANALYZE.
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 formatting costs are not included. Second, the overhead added by EXPLAIN ANALYZE can be significant, especially on machines with slow gettimeofday() kernel calls.
It is worth noting that EXPLAIN results should not be extrapolated to situations other than the one you are actually testing; for example, results on a toy-sized table 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.
Notes
[1]	
Examples in this section are drawn from the regression test database after doing a VACUUM ANALYZE, using 8.2 development sources. You should be able to get similar results if you try the examples yourself, but your estimated costs and row counts might vary slightly because ANALYZE's statistics are random samples rather than exact.
=== 14.2. Statistics Used by the Planner===
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, 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 stand-alone ANALYZE, that is one not part of VACUUM, generates an approximate reltuples value since it does not read every row of the table. The planner will scale the values it finds in pg_class to match the current physical table size, thus obtaining a closer approximation.
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 system catalog. Entries in pg_statistic are updated by the ANALYZE and VACUUM ANALYZE commands, and are always approximate even when freshly updated.
Rather than look at pg_statistic directly, it's better to look at its 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 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 Chapter 57.
=== 14.3. Controlling the Planner with Explicit JOIN Clauses===
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 — 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 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 JOINs, set the 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 — 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.
from_collapse_limit and 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.
=== 14.4. Populating a Database===
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.
14.4.1. Disable Autocommit
When using multiple INSERTs, 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.
14.4.2. Use COPY
Use 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 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 wal_level is minimal as all commands must write WAL otherwise.
14.4.3. Remove 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.
14.4.4. 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 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.
14.4.5. Increase maintenance_work_mem
Temporarily increasing the 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.
14.4.6. Increase checkpoint_segments
Temporarily increasing the checkpoint_segments 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 checkpoint_segments temporarily during bulk data loads, the number of checkpoints that are required can be reduced.
14.4.7. Disable WAL Archival and Streaming Replication
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 wal_level to minimal, archive_mode to off, and max_wal_senders to zero. But note that changing these settings requires a server restart.
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 are designed not to write WAL at all if wal_level is minimal. (They can guarantee crash safety more cheaply by doing an fsync at the end than by writing WAL.) This applies to the following commands:
CREATE TABLE AS SELECT
CREATE INDEX (and variants such as ALTER TABLE ADD PRIMARY KEY)
ALTER TABLE SET TABLESPACE
CLUSTER
COPY FROM, when the target table has been created or truncated earlier in the same transaction
14.4.8. Run ANALYZE Afterwards
Whenever you have significantly altered the distribution of data within a table, running 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 Section 23.1.3 and Section 23.1.5 for more information.
14.4.9. Some Notes About 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 checkpoint_segments.
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.
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 checkpoint_segments 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 Section 23.1.3 and Section 23.1.5 for more information.
Notes
[1]	
You can get the effect of disabling foreign keys by using the --disable-triggers option — 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.
=== 14.5. Non-Durable Settings===
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 abrupt operating system stoppage 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 fsync; there is no need to flush data to disk.
Turn off full_page_writes; there is no need to guard against partial page writes.
Increase checkpoint_segments and checkpoint_timeout ; this reduces the frequency of checkpoints, but increases the storage requirements of /pg_xlog.
Turn off synchronous_commit; there might be no need to write the WAL to disk on every commit. This setting does risk transaction loss (though not data corruption) in case of a crash of the database alone.