| PostgreSQL 8.0.26 Documentation | ||||
|---|---|---|---|---|
| Prev | Fast Backward | Chapter 13. Performance Tips | Fast Forward | Next |
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.