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
s, turn off autocommit and just do
one commit at the end. (In plain
SQL, this means issuing
at the start and
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,
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
all the rows in one command, instead of using a series of
command is optimized for loading large numbers of rows; it is less
, but incurs significantly
less overhead for large data loads. Since
is a single command, there is no need to disable autocommit if you
use this method to populate a table.
If you cannot use
, it might help to use
to create a
statement, and then use
as many times as required. This avoids
some of the overhead of repeatedly parsing and planning
. Different interfaces provide this facility
in different ways; look for
in the interface
Note that loading a large number of rows using
is almost always faster than using
, even if
is used and
multiple insertions are batched into a single transaction.
is fastest when used within the same
transaction as an earlier
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
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
, 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.
Temporarily increasing the
configuration variable when loading large amounts of data can
lead to improved performance. This will help to speed up
ALTER TABLE ADD FOREIGN KEY
It won't do much for
itself, so this advice is
only useful when you are using one or both of the above techniques.
Temporarily increasing the
configuration variable can also
make large data loads faster. This is because loading a large
amount of data into
cause checkpoints to occur more often than the normal checkpoint
frequency (specified by the
configuration variable). Whenever a checkpoint occurs, all dirty
pages must be flushed to disk. By increasing
temporarily during bulk
data loads, the number of checkpoints that are required can be
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
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 do not to write WAL at all if
and the current subtransaction (or top-level
transaction) created or truncated the table or index they change. (They
can guarantee crash safety more cheaply by doing
at the end than by writing WAL.)
Whenever you have significantly altered the distribution of data
within a table, running
is strongly recommended. This
includes bulk loading large amounts of data into the table. Running
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
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 restore 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.)
, 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
If using WAL archiving or streaming replication, consider disabling them during the restore. To do that, set
max_wal_sendersto zero before loading the dump. Afterwards, set them back to the right values and take a fresh base backup.
Experiment with the parallel dump and restore modes of both pg_dump and pg_restore and find the optimal number of concurrent jobs to use. Dumping and restoring in parallel by means of the
-joption should give you a significantly higher performance over the serial mode.
Consider whether the whole dump should be restored as a single transaction. To do that, pass the
--single-transactioncommand-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.
COPYcommands 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
--jobsoption. This allows concurrent data loading and index creation.
A data-only dump will still use
, but it does not
drop or recreate indexes, and it does not normally touch foreign
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
while loading the data, but don't bother increasing
; rather, you'd do that while
manually recreating indexes and foreign keys afterwards.
And don't forget to
when you're done; see
for more information.
You can get the effect of disabling foreign keys by using
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.