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. The LAG() function is valuable when you need to compare or calculate values from the previous row, especially for understanding trends, changes, or patterns within ordered data.PostgreSQL 15 improves crash recovery and physical replication performance of some large and very busy databases by trying to minimise I/O stalls. In this output, the “previous_day_revenue” column contains the revenue from the sale that occurred on the day immediately preceding each current sale. The result might look like this: sale_date | revenue | previous_day_revenue -+-+- | 100 | NULL | 150 | 100 | 90 | 150 | 200 | 90 In this example, the LAG() function retrieves the revenue from the previous day’s sale for each row based on the order of sale dates. Suppose you have a table named “daily_sales” with columns “sale_date” and “revenue”, and you want to calculate the difference in revenue between the current day’s sales and the previous day’s sales: SELECT sale_date, revenue, LAG(revenue, 1) OVER (ORDER BY sale_date) AS previous_day_revenue FROM daily_sales Here’s a simple example of how to use the LAG() function in PostgreSQL: ORDER BY: Specifies the column(s) by which you want to order the result set for accessing preceding row values.If omitted, the function operates on the entire result set. The function operates separately within each partition. PARTITION BY: An optional clause that divides the result set into partitions or groups.default_value: An optional value to be returned when the LAG() function is invoked on the first row of a partition.The default is 1 (i.e., the previous row). offset: The number of rows before the current row from which you want to access the value.expression: The column or expression whose value you want to access from the preceding row.The syntax of the LAG() function is as follows: LAG(expression, offset, default_value) OVER (PARTITION BY partition_expression ORDER BY sort_expression)

This function allows you to compare values between the current row and the previous row or to retrieve values from “lagging” rows relative to the current row based on a specified order. The LAG() function is used to access the value of a column in a preceding row within the same result set.

Postgresql lead lag