![]() ![]() Firstly, the database will try not to exceed it but is allowed to, so keep enough free space on the partition and monitor it. Note: The max_wal_size is a soft limit for total WAL size, which has two consequences. With these (default) values, PostgreSQL will trigger a CHECKPOINT every 5 minutes, or after the WAL grows to about 1GB on disk. max_wal_size = 1GB (before PostgreSQL 9.5 this was checkpoint_segments).Those time/size limits are set using two configuration options: This blog post is about how to configure the two other events, affecting regular periodic checkpoints. The first two points are rather irrelevant here – those are rare, manually triggered, events. generating a configured amount of WAL since the previous checkpoint (aka “running out of WAL” or “filling WAL”).reaching a configured amount of time since the last checkpoint.pg_start_backup, CREATE DATABASE, or pg_ctl stop|restart and a few others) executing a command that requires a checkpoint (e.g.executing the CHECKPOINT command directly.There are about three or four reasons why a checkpoint can be triggered: So in practice you want the checkpoints to happen infrequently not to affect the users, but frequently enough to reasonably limit time for recovery and disk space requirements. increasing COMMIT latency, reducing throughput). But it would also turn the asynchronous writes to data files into synchronous ones, seriously impacting the users (e.g. That would allow keeping only tiny amount of WAL and the recovery would be very fast (having to replay only the tiny WAL amount). Let’s also discuss the other extreme – doing very frequent checkpoints (say, every second or so). Let’s see how to achieve that in PostgreSQL □ Note: If you happen to be a gamer, you are probably familiar with the concept of checkpoints – your character passes a certain point in the game, and if you fail to beat the next boss or fall into a lake hot lava, you start from that last point instead of starting from the very beginning. That is exactly what checkpoints are for – guarantee that WAL before some point in time are no longer needed for recovery, reducing the disk space requirements and recovery time. And it could also remove WAL before that “known good” position. Then it could determine this position during recovery, and replay only the remaining part of the WAL, significantly reducing the recovery time. So imagine how much disk space you would need to keep all WAL when running the database for a year, and how much time it would take to replay during recovery.īut what if the database could guarantee that all changes for a given WAL position (offset in the log), all data file changes up to that position are flushed to disk. We often deal with databases that are not extremely big (say, few hundred GBs), but produce several TBs of WAL per day. The obvious disadvantage is of course the need to keep and replay the whole WAL. In the end we should get a complete (and correct) database. The simplest approach would be to take start from scratch and replay the whole WAL from the very beginning. ![]() ![]() Let’s say the system crashed and the database needs to perform recovery. That is is good, because while WAL writes are sequential in nature, while writes to data files are often random. Users only have to wait for the WAL (to be flushed to disk), while the data files are modified only in memory and then flushed later in the background. While this may double the amount of writes, it may actually improve performance. That provides durability, because in case of a crash the database may use WAL to perform recovery – read the changes from WAL and re-apply them on data files. PostgreSQL is one of the databases relying on write-ahead log (WAL) – all changes are written to a log (a stream of changes) first, and only then to the data files. (The other one being autovacuum, discussed a few days ago by Joe Nelson from Citus.) So let me walk you through the checkpoints – what they do and how to tune them in PostgreSQL. Yet checkpoints are one of the areas where we often identify confusion and configuration issues, both on the community mailing lists and during performance tuning reviews for our customers. On systems doing non-trivial number of writes, tuning checkpoints is crucial for getting good performance. PostgreSQL 9 Cookbook – Chinese Edition.PostgreSQL Server Programming Cookbook – 2nd Edition.PostgreSQL 9 Administration Cookbook – 3rd Edition.PostgreSQL High Availability Cookbook – 2nd Edition. ![]()
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