The insert benchmark on a small server : Postgres 12.22 through 18.1

This has results for Postgres versions 12.22 through 18.1 with the Insert Benchmark on a small server.

Postgres continues to be boring in a good way. It is hard to find performance regressions.

 tl;dr for a cached workload

• performance has been stable from Postgres 12 through 18

tl;dr for an IO-bound workload

• performance has mostly been stable
• create index has been ~10% faster since Postgres 15
• throughput for the write-only steps has been ~10% less since Postgres 15
• throughput for the point-query steps (qp*) has been ~20% better since Postgres 13

Builds, configuration and hardware

I compiled Postgres from source using -O2 -fno-omit-frame-pointer for versions 12.22, 13.22, 13.23, 14.19, 14.20, 15.14, 15.15, 16.10, 16.11, 17.6, 17.7, 18.0 and 18.1.

The server is an ASUS ExpertCenter PN53 with and AMD Ryzen 7 7735HS CPU, 8 cores, SMT disabled, 32G of RAM. Storage is one NVMe device for the database using ext-4 with discard enabled. The OS is Ubuntu 24.04. More details on it are here.

For versions prior to 18, the config file is named conf.diff.cx10a_c8r32 and they are as similar as possible and here for versions 12, 13, 14, 15, 16 and 17.

For Postgres 18 I used 3 variations, which are here:

• conf.diff.cx10b_c8r32
• uses io_method='sync' to match Postgres 17 behavior
• conf.diff.cx10c_c8r32
• uses io_method='worker' and io_workers=16 to do async IO via a thread pool. I eventually learned that 16 is too large.
• conf.diff.cx10d_c8r32
• uses io_method='io_uring' to do async IO via io_uring

The Benchmark

The benchmark is explained here and is run with 1 client and 1 table. I repeated it with two workloads:

• cached - the values for X, Y, Z are 30M, 40M, 10M
• IO-bound - the values for X, Y, Z are 800M, 4M, 1M

The point query (qp100, qp500, qp1000) and range query (qr100, qr500, qr1000) steps are run for 1800 seconds each.

The benchmark steps are:

• l.i0
• insert X rows per table in PK order. The table has a PK index but no secondary indexes. There is one connection per client.
• l.x
• create 3 secondary indexes per table. There is one connection per client.
• l.i1
• use 2 connections/client. One inserts Y rows per table and the other does deletes at the same rate as the inserts. Each transaction modifies 50 rows (big transactions). This step is run for a fixed number of inserts, so the run time varies depending on the insert rate.
• l.i2
• like l.i1 but each transaction modifies 5 rows (small transactions) and Z rows are inserted and deleted per table.
• Wait for S seconds after the step finishes to reduce variance during the read-write benchmark steps that follow. The value of S is a function of the table size.
• qr100
• use 3 connections/client. One does range queries and performance is reported for this. The second does does 100 inserts/s and the third does 100 deletes/s. The second and third are less busy than the first. The range queries use covering secondary indexes. If the target insert rate is not sustained then that is considered to be an SLA failure. If the target insert rate is sustained then the step does the same number of inserts for all systems tested. This step is frequently not IO-bound for the IO-bound workload.
• qp100
• like qr100 except uses point queries on the PK index
• qr500
• like qr100 but the insert and delete rates are increased from 100/s to 500/s
• qp500
• like qp100 but the insert and delete rates are increased from 100/s to 500/s
• qr1000
• like qr100 but the insert and delete rates are increased from 100/s to 1000/s
• qp1000
• like qp100 but the insert and delete rates are increased from 100/s to 1000/s

Results: overview

The performance reports are here for:

• Cached
• All point releases tested
• Latest point release from each version
• IO-bound
• All point releases tested
• Latest point release from each version

The summary sections from the performances report have 3 tables. The first shows absolute throughput by DBMS tested X benchmark step. The second has throughput relative to the version from the first row of the table. The third shows the background insert rate for benchmark steps with background inserts. The second table makes it easy to see how performance changes over time. The third table makes it easy to see which DBMS+configs failed to meet the SLA.

Below I use relative QPS to explain how performance changes. It is: (QPS for $me / QPS for $base) where $me is the result for some version $base is the result from Postgres 12.22.

When relative QPS is > 1.0 then performance improved over time. When it is < 1.0 then there are regressions. The Q in relative QPS measures: 

• insert/s for l.i0, l.i1, l.i2
• indexed rows/s for l.x
• range queries/s for qr100, qr500, qr1000
• point queries/s for qp100, qp500, qp1000

This statement doesn't apply to this blog post, but I keep it here for copy/paste into future posts. Below I use colors to highlight the relative QPS values with red for <= 0.95, green for >= 1.05 and grey for values between 0.95 and 1.05.

Results: cached

The performance summaries are here for all versions and latest versions.

I focus on the  latest versions. Throughput for 18.1 is within 2% of 12.22, with the exception of the l.i2 benchmark step. This is great news because it means that Postgres has avoided introducing new CPU overhead as they improve the DBMS. There is some noise from the l.i2 benchmark step and that doesn't surprise me because it is likely variance from two issues -- vacuum and get_actual_variable_range. 

Results: IO-bound

The performance summaries are here for all versions and latest versions.

I focus on the latest versions.

• throughput for the load step (l.i0) is 1% less in 18.1 vs 12.22
• throughput for the index step (l.x) is 13% better in 18.1 vs 12.22
• throughput for the write-only steps (l.i1, l.i2) is 11% and 12% less in 18.1 vs 12.22
• throughput for the range-query steps (qr*) is 2%, 3% and 3% less in 18.1 vs 12.22
• throughput for the point-query steps (qp*) is 22%, 23% and 23% better in 18.1 vs 12.22

The improvements for the index step arrived in Postgres 15.

The regressions for the write-only steps arrived in Postgres 15 and are likely from two issues -- vacuum and get_actual_variable_range. 

The improvements for the point-query steps arrived in Postgres 13.