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.
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 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 does not apply when archive_mode is set, as all commands must write WAL in that case.
If you are loading a freshly created table, the fastest way 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 index, load the table, and then recreate the index. Of course, the database performance for other users might be adversely affected during the time that the index is missing. One should also think twice before dropping unique indexes, since the error checking afforded by the unique constraint will be lost while the index is missing.
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.
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.
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.
When loading large amounts of data into an installation that uses WAL archiving, you might want to disable archiving (turn off the archive_mode configuration variable) while loading. 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. But note that turning archive_mode on or off requires a server restart.
Aside from avoiding the time for the archiver to process the
WAL data, doing this will actually make certain commands
faster, because they are designed not to write WAL at all if
archive_mode is off. (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
COPY FROM, when the target table has been created or truncated earlier in the same transaction
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.
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 when 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, consider disabling it during the restore. To do that, turn off archive_mode before loading the dump script, and afterwards turn it back on 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.
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.  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.
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.
I'm not a professional DBA, so I found myself reviewing this page looking for performance optimization tips. When I was done reviewing the suggestions and other optimization resources, I found my performance for loading was not improved as much as I'd like.
We're populating a large database for analytics on the order of billions of rows. Each row is small, and there is nothing complicated about the schema. As per the advice above I've disabled all indexes, increased checkpoints, turned archiving off, and it was still taking over a minute (77 seconds) and some for a million rows.
So I took a hard look at the config file, and extrapolated some information someone else gave in another forum, and found that switching one parameter in the 'postgresql.conf' file, 'wal_buffers' up to 16MB, and that million rows loaded in ~3 seconds. Additionally, that improvement scaled. I'm happily loading hundreds of millions of rows in a matter of minutes.
So, hopefully you'll benefit from this advice if your use case involves loading very large uncomplicated fact tables.