[Postgres] How to speed up bulk load

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If you need to load a lot of data, here are the tips that can help you do it faster.

1) COPY

Use COPY to load data, it's optimized for bulk load.

2) Less frequent checkpoints

Consider increasing max_wal_size and checkpoint_timeout temporarily.

Changing them does not require restart.

Increased values lead to increased recovery time in case of failure, but benefit is that checkpoints occur less often, therefore:

1. less stress on disk,
2. less WAL data is written, thanks to decreased number of full page writes of the same pages (when load happens with existing indexes).

3) Larger buffer pool

Increase shared_buffers, if you can.

4) No (or fewer) indexes

If load happens into a new table, create indexes after data load. When loading into an existing table, avoid over-indexing .

Every additional index will significantly slow down the load.

w5) No (or fewer) FKs and triggers

Similarly to indexes, foreign key constraints and triggers may significantly slow down data load – consider (re)creating them after the bulk load.

Triggers can be disabled via ALTER TABLE … DISABLE TRIGGERS ALL – however, if triggers support some consistency checks, you need to make sure that those checks are not violated (e.g., run additional checks after data load). FKs are implemented via implicit triggers, and ALTER TABLE … DISABLE TRIGGERS ALL disables them too – loading data in this state should be done with care.

6) Avoiding WAL writes

If this is a new table, consider completely avoiding WAL writes during the data load. Two options (both have limitations and require understanding that data can be lost if a crash happens):

• Use unlogged table: CREATE UNLOGGED TABLE …. Unlogged tables are not archived, not replicated, they are not persistent (though, they survive normal restarts). However, converting an unlogged table to a normal one takes time (likely, a lot –?worth testing), because he data needs to be written to WAL. More about unlogged tables in this Crunchy Data post ; also, see this StackOverflow discussion .
• Use COPY with wal_level ='minimal'. COPY has to be executed inside the transaction that created the table. In this case, due to wal_level ='minimal', COPY writes won't be written to WAL (as of PG16, this is so only if table is unpartitioned). Additionally, consider using COPY (FREEZE) – this approach also provides a benefit: all tuples are frozen after the data load. Setting wal_level='minimal', unfortunately, requires a restart, and additional changes (archive_mode = 'off', max_wal_senders = 0). Of course, this method doesn't work well in most of the production cases, but can be good for single-server setups. Details for the wal_level='minimal' + COPY (FREEZE) recipe in this Cybertec post .

7) Parallelization

Consider parallelization. This may or may not speed up the process, depending on the bottlenecks of the single-threaded process (e.g., if single-threaded load saturates disk IO, parallelization won't help). Two options:

• Partitioned tables and loading into multiple partitions using multiple workers (Day 20: pg_restore tips ).
• Unpartitioned table and loading in big chunks. Such chunks require preparation of them – it can be CSV split into pieces, or exported ranges of table data using multiple synchronized REPEATABLE READ transactions (working with the same snapshot via SET TRANSACTION SNAPSHOT; see Day 8: How to speed up pg_dump .

If you use TimescaleDB, consider timescaledb-parallel-copy .

Last but not least: after a massive data load, don't forget to run ANALYZE.

Marathon progress: ▓??????????????????? 8.77%

This series is also available in Markdown format: https://gitlab.com/postgres-ai/postgresql-consulting/postgres-howtos