PostgreSQL 8.0.0rc4 Documentation
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# 13.4. Populating a Database

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.

## 13.4.1. Disable Autocommit

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.

## 13.4.2. 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 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.

## 13.4.3. Remove Indexes

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.

## 13.4.4. Increase `maintenance_work_mem`

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.

## 13.4.5. 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 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.

## 13.4.6. 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 may make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics.

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