In Postgres, [full-page writes] (https://www.postgresql.org/docs/devel/static/runtime-config-wal.html#RUNTIME-CONFIG-WAL-SETTINGS). which are in short complete images of a page added in WAL after the first modification of this page after a checkpoint, can be an origin of WAL bloat for applications manipulating many relation pages. Note that full-page writes are critical to ensure data consistency in case particularly if a crash happens during a page write, making perhaps this page made of both new and old data.
In Postgres 9.5, the following patch has landed to leverage this quantity of “recovery journal” data, by adding the possibility to compress full-page writes in WAL (full commit message is shortened for this post and can be found [here] (https://git.postgresql.org/gitweb/?p=postgresql.git;a=commitdiff;h=57aa5b2bb11a4dbfdfc0f92370e0742ae5aa367b)):
commit: 57aa5b2bb11a4dbfdfc0f92370e0742ae5aa367b author: Fujii Masao <firstname.lastname@example.org> date: Wed, 11 Mar 2015 15:52:24 +0900 Add GUC to enable compression of full page images stored in WAL. When newly-added GUC parameter, wal_compression, is on, the PostgreSQL server compresses a full page image written to WAL when full_page_writes is on or during a base backup. A compressed page image will be decompressed during WAL replay. Turning this parameter on can reduce the WAL volume without increasing the risk of unrecoverable data corruption, but at the cost of some extra CPU spent on the compression during WAL logging and on the decompression during WAL replay. [...] Rahila Syed and Michael Paquier, reviewed in various versions by myself, Andres Freund, Robert Haas, Abhijit Menon-Sen and many others.
As described in this message, a new GUC parameter, called wal_compression by default disabled to not impact existing users, can be used for this purpose. The compression of full-write pages is done using PGLZ, that has been moved to libpgcommon a couple of weeks back as the idea is to make it available particularly for frontend utilities of the type [pg_xlogdump] (https://www.postgresql.org/docs/devel/static/pgxlogdump.html) that decode WAL. Be careful though that compression has a CPU cost, in exchange of reducing the I/O caused by WAL written to disks, so this feature is really for I/O bounded environment or for people who want to reduce their amount of WAL on disk and have some CPU to spare on it. There are also a couple of benefits that can show up when using this feature:
- WAL replay can speed up, meaning that a node in recovery can recover faster (after a crash, after creating a fresh standby node or whatever)
- As synchronous replication is very sensitive to WAL length particularly in presence of multiple backends that need to wait for WAL flush confirmation from a standby, the write/flush position that a standby reports can be sent faster because the standby recovers faster. Meaning that synchronous replication response gets faster as well.
Note as well that this parameter can be changed without restarting the server just with a reload, or SIGHUP, and that it can be updated within a session, so for example if a given application knows that a given query is going to generate a bunch of full-page writes in WAL, wal_compression can be disabled temporarily on a Postgres instance that has it set as enabled. The contrary is true as well.
Now let’s have a look at what this feature can do with for example the two following tables having close to 480MB of data, on a server with 1GB of shared_buffers, the first table contains very repetitive data, and the second uses uuid data (see [pgcrypto] (https://www.postgresql.org/docs/devel/static/pgcrypto.html) for more details):
=# CREATE TABLE int_tab (id int); CREATE TABLE =# ALTER TABLE int_tab SET (FILLFACTOR = 50); ALTER TABLE -- 484MB of repetitive int data =# INSERT INTO int_tab SELECT 1 FROM generate_series(1,7000000); INSERT 0 7000000 =# SELECT pg_size_pretty(pg_relation_size('int_tab')); pg_size_pretty ---------------- 484 MB (1 row) =# CREATE TABLE uuid_tab (id uuid); CREATE TABLE =# ALTER TABLE uuid_tab SET (FILLFACTOR = 50); ALTER TABLE -- 484MB of UUID data =# INSERT INTO uuid_tab SELECT gen_random_uuid() FROM generate_series(1, 5700000); INSERT 0 5700000 =# SELECT pg_size_pretty(pg_relation_size('uuid_tab')); pg_size_pretty ---------------- 484 MB (1 row)
The fillfactor is set to 50%, and each table will be updated, generated completely full page writes with a minimum hole size to maximize the effects of compression.
Now that the data has been loaded, let’s be sure that it is loaded in the database buffers (not mandatory here, but being maniac costs nothing), and the number of shared buffers of those relations can be fetched at the same time (not exactly the same but it does not really matter to have such few diffence of pages at this scale):
=# SELECT pg_prewarm('uuid_tab'); pg_prewarm ------------ 61957 (1 row) =# SELECT pg_prewarm('int_tab'); pg_prewarm ------------ 61947 (1 row)
After issuing a checkpoint, let’s see how this behaves with the following UPDATE commands:
UPDATE uuid_tab SET id = gen_random_uuid(); UPDATE int_tab SET id = 2;
Before and after each command pg_current_xlog_location() is used to get the XLOG position to evaluate the amount of WAL generated. So, after running that with wal_compression enabled and disabled, combined with a [trick] (/postgresql-2/postgres-calculate-cpu-usage-process/) to calculate CPU for a single backend, I am getting the following results:
|Case||WAL generated||User CPU||System CPU|
|UUID tab, compressed||633 MB||30.64||1.89|
|UUID tab, not compressed||727 MB||17.05||0.51|
|int tab, compressed||545 MB||20.90||0.68|
|int tab, not compressed||727 MB||14.54||0.84|
In short, WAL compression saves 27% for this integer table, and 13% with the data largely incompressible!
Note as well that PGLZ is a CPU-eater, so one of the areas of improvements would be to plug in another compression algorithm of the type lz4, or add a hook in backend code to be able to compress full-page writes with something that has a license not necessarily compatible with PostgreSQL preventing its integration into core code. Another area would be to make this parameter settable at relation-level, as it depends on how a schema is compressible. In any case, that’s great stuff.