![]() ![]() PostgreSQL, like many other database systems systems, records changes in a write-ahead log or WAL. In contrast, the true asynchronous I/O proposal being worked on for the next generation will work everywhere, via various kernel APIs.īackground: How write-ahead logging (WAL) is used for recovery It doesn't yet work on other operating systems. If trying this experiment at home, note also that the current advice-based scheme is only known to work on Linux with ext4 and xfs (it might also work on other systems, and a future release of zfs is expected to work). 10 is not quite enough to replay this contrived test workload at full speed, but 16 is. At 10 you see the replication lag begin to creep up again. Then I tried a few lower values: 24, and then 16, and then 10 again. It's still not quite enough though, so I tried turning up maintenance_io_concurrency to 32. This caused the rate of increase in replication lag to slow down, as a result of the concurrent I/O requests shown in blue. The graph shows the moment that maintenance_io_concurrency, shown as a green dotted line, steps up to 10 (which is the default value), because I ran:ĪLTER SYSTEM SET maintenance_io_concurrency = 10 At the 30 second mark, once the maintenance_io_concurrency setting was set to 10-and then higher-you can see the benefits of recovery prefetching and I/O concurrency: replication lag decreased. The first 30 seconds emulate the behaviour of PostgreSQL 14, where there was no I/O concurrency and the replica could not keep up with the primary server, so the replication lag increased. ![]() (Replication lag, or replay lag, is a measure of how far behind the primary server the replica has fallen, in seconds close to zero and stable is good.)įigure 1: This chart depicts I/O concurrency (shown in blue) impacting replication lag (shown in purple, also called replay lag). On this particular system, the replication lag-shown in the chart below in purple-begins to climb because the replica can't keep up with all the random I/O required to replay the WAL. set full_page_writes to off, to make the effect visible immediately ( otherwise the speed-up potential would vary over time in a saw-tooth pattern due to checkpoints).run a streaming replica, with maintenance_io_concurrency set to 0 to disable recovery prefetching, that is, to behave like PostgreSQL 14.set up a scale 1000 pgbench TCP-B test with 32 clients on a fairly basic developer system.In later releases, we aim to build on this infrastructure to improve even well-cached workloads. systems running with full_page_writes set to off (usually not recommended).very large busy databases with a working set that exceeds memory, and. ![]() The main beneficiaries of recovery prefetching will be: Why? Because the random data blocks involved are unlikely to be in PostgreSQL's buffer pool or in the kernel's page cache.Įffects on real world data will range from dramatic acceleration as seen in this test through to no change at all if they are already well cached in memory. ![]() You can see a perfect demonstration of this new recovery prefetching feature by running pgbench at a scale that doesn't fit in RAM. Let's start with a quick example of the best case improvement possible with recovery prefetching. Trying out recovery prefetching in PostgreSQL 15 PostgreSQL 15: Adding more concurrency, but not yet truly asynchronous.In this blog post, you'll learn how recovery prefetching minimises I/O stalls and reduces replication lag in PostgreSQL 15-as well as how this work fits into a larger set of proposals to move PostgreSQL from traditional UNIX style I/O to efficient modern I/O APIs in the future. With big and busy databases, when I/O concurrency increases, replication lag can be reduced. How? The change in PostgreSQL15 is that recovery now uses the maintenance_io_concurrency setting (default is 10, but you can increase it) to decide how many concurrent I/Os to try to initiate, rather than doing random read I/Os one at a time. A standby server might now have an easier time keeping up with the primary. LEAD and LAG function along with PARTITION BY gets the next and previous rows of the group within the table.PostgreSQL 15 improves crash recovery and physical replication performance of some large and very busy databases by trying to minimise I/O stalls. These functions allow you to access the data from a subsequent row without using any SELF JOIN. LEAD() and LAG() Function returns the values from next row and previous row in the table respectively. ![]()
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