One may 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 may
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 may 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
.
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.
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 augmenting an existing table, you can 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 afforded by the unique constraint will be lost while the index is missing.
Temporarily increasing the maintenance_work_mem
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.
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 can
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.
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 may
make poor decisions during query planning, leading to poor
performance on any tables with inaccurate or nonexistent
statistics.