## 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 adding large amounts of data to an existing table, it may be a win to 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. 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 may 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.

### 13.4.5. Increase `maintenance_work_mem`

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.

### 13.4.6. 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 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.

### 13.4.7. 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.

### 13.4.8. Some Notes About pg_dump

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 the first 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` before loading the dump script, and then to 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. [8] 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.

[8] You can get the effect of disabling foreign keys by using the `-X disable-triggers` option [mdash ] but realize that that eliminates, rather than just postponing, foreign key validation, and so it is possible to insert bad data if you use it.