| Postgres-XC 1.2devel Documentation | ||||
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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.
When using multiple INSERTs, 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 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, Postgres-XC 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 might 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. Different interfaces provide this facility in different ways; look for "prepared statements" in the interface documentation.
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.
COPY is fastest when used within the same transaction as an earlier CREATE TABLE or TRUNCATE 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 wal_level is minimal as all commands must write WAL otherwise.
If you are loading a freshly created table, the fastest method 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 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.
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 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.
Note: XCONLY: The following description applies only to Postgres-XC.
In Postgres-XC, when referring a distributed table, only the distribution column can be specified as a referred column. In this case, the referred table must be distributed by HASH or MODULO. There's no such restriction when referring to a replicated table.
Temporarily increasing the checkpoint_segments configuration variable can also make large data loads faster. This is because loading a large amount of data into Postgres-XC 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.
Note: XCONLY: The following description applies only to Postgres-XC.
Please note that you should tune the configuration variables in all the nodes involved. Maybe you need to tune this just for Datanodes. Coordinator's database will be updated almost only by DDLs.
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 wal_level to minimal, archive_mode to off, and max_wal_senders to zero. 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
are designed not to write WAL at all if wal_level
is minimal. (They can guarantee crash safety more cheaply
by doing an fsync at the end than by writing WAL.)
This applies to the following commands:
CREATE TABLE AS SELECT
CREATE INDEX (and variants such as ALTER TABLE ADD PRIMARY KEY)
ALTER TABLE SET TABLESPACE
CLUSTER
COPY FROM, when the target table has been created or truncated earlier in the same transaction
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 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 run ANALYZE automatically; see Section 22.1.3 and Section 22.1.6 for more information.
Note: XCONLY: The following description applies only to Postgres-XC.
In Postgres-XC, manual VACUUM will be populated to all the Datanodes as well. However, you should configure autovacuum for each Coordinator and Datanodes.
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 whether loading a text dump with psql or 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 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.
If using WAL archiving or streaming replication, consider disabling them during the restore. To do that, set archive_mode to off, wal_level to minimal, and max_wal_senders to 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 -j option 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 -1 or --single-transaction command-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. COPY commands 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 --jobs option. This allows concurrent data loading and index creation.
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. [1] 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; see Section 22.1.3 and Section 22.1.6 for more information.
| [1] | You can get the effect of disabling foreign keys by using the --disable-triggers 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. |