Small Datum

Friday, June 19, 2026

CPU-bound sysbench on a large server: Postgres 12 to 19 beta1

This has results from sysbench on a small server with Postgres versions 12 through 19 beta1. Sysbench is run with high concurrency (40 connections) and a cached database. The purpose is to search for changes in performance.

Postgres remains boring, it is hard to find performance regressions.

tl;dr for Postgres 17 to 19

there are no regressions
throughput on the read-only-count test improves by ~3X in 19 beta1 thanks to a better query plan

tl;dr for Postgres 12 to 19

there are few regressions, throughput might have dropped by up to 5% on a few range query tests
there are a few large improvements for read-only tests
there are many large improvements for write-heavy tests

Builds, configuration and hardware

I compiled Postgres from source for versions 12.22, 13.23, 14.23, 15.18, 16.14, 17.10, 18.4 and 19 beta1.

I used a 48-core server from Hetzner

an ax162s with an AMD EPYC 9454P 48-Core Processor with SMT disabled
2 Intel D7-P5520 NVMe storage devices with RAID 1 (3.8T each) using ext4
128G RAM
Ubuntu 24.04

Configuration files for Postgres:

the config file is named conf.diff.cx10a_c32r128 (x10a_c32r128) and is here for versions 12, 13, 14, 15, 16 and 17.
for Postgres 18 and 19 I used conf.diff.cx10b_c32r128 (x10b_c32r128) which is as close as possible to the Postgres 17 config and uses io_method=sync

Benchmark

I used sysbench and my usage is explained here. I now run 32 of the 42 microbenchmarks listed in that blog post. Most test only one type of SQL statement. Benchmarks are run with the database cached by Postgres.

The read-heavy microbenchmarks are run for 600 seconds and the write-heavy for 1200 seconds. The benchmark is run with 40 clients and 8 tables with 10M rows per table. The database is cached.

The purpose is to search for regressions from new CPU overhead and mutex contention. I use the small server with low concurrency to find regressions from new CPU overheads and then larger servers with high concurrency to find regressions from new CPU overheads and mutex contention.

The tests can be called microbenchmarks. They are very synthetic. But microbenchmarks also make it easy to understand which types of SQL statements have great or lousy performance. Performance testing benefits from a variety of workloads -- both more and less synthetic.

Results

The microbenchmarks are split into 4 groups -- 1 for point queries, 2 for range queries, 1 for writes. For the range query microbenchmarks, part 1 has queries that don't do aggregation while part 2 has queries that do aggregation.

I provide charts below with relative QPS (rQPS). The relative QPS is the following:

(QPS for some version) / (QPS for base version)

When the relative QPS is > 1 then some version is faster than base version. When it is < 1 then there might be a regression. Values from iostat and vmstat divided by QPS are also provided here. These can help to explain why something is faster or slower because it shows how much HW is used per request.

Here, base version is either Postgres 12.23 or 17.10 and some version is a more recent version. I use 12.23 as the base version to identify regressions over a long period of time. And then I use 17.10 as the base version to confirm there aren't recent, large regressions.

I describe performance changes (changes to relative QPS) in terms of basis points. Performance changes by one basis point when the difference in rQPS is 0.01. When rQPS decreases from 0.95 to 0.85 then it changed by 10 basis points.

Results: point queries, version 17 to 19

Summary:

there are no regressins

Relative to: PG 17.10

col-1 : PG 18.4

col-2 : PG 19 beta1

col-1 col-2

1.00 0.99 hot-points

1.01 1.01 point-query

1.00 1.00 points-covered-pk

0.98 0.99 points-covered-si

1.01 1.00 points-notcovered-pk

1.00 1.00 points-notcovered-si

1.01 1.01 random-points_range=10

1.02 1.00 random-points_range=100

1.00 1.00 random-points_range=1000

Results: point queries, version 12 to 19

Summary

there are no regressions
throughput for the hot-points test improves by ~2X in versions 17.10, 18.4 and 19beta

Relative to: PG 12.22

col-1 : PG 13.23

col-2 : PG 14.23

col-3 : PG 15.18

col-4 : PG 16.14

col-5 : PG 17.10

col-6 : PG 18.4

col-7 : PG 19 beta1

col-1 col-2 col-3 col-4 col-5 col-6 col-7

1.00 0.90 0.97 1.03 2.34 2.35 2.31 hot-points

1.00 1.01 1.03 1.04 1.03 1.04 1.03 point-query

1.02 1.04 1.04 1.07 1.04 1.04 1.04 points-covered-pk

1.01 1.07 1.04 1.04 1.04 1.03 1.04 points-covered-si

0.98 1.01 1.03 1.02 1.00 1.01 1.00 points-notcovered-pk

0.99 1.03 1.03 1.01 1.02 1.02 1.01 points-notcovered-si

0.99 1.01 1.03 1.03 1.00 1.01 1.01 random-points_range=10

0.99 1.02 1.04 1.04 1.01 1.03 1.01 random-points_range=100

1.00 1.02 1.02 1.03 1.01 1.02 1.01 random-points_range=1000

Results: range queries without aggregation, version 17 to 19

Summary

there are no regressions
while 19 beta1 has a better result on the scan test, that test has more variance with Postgres so I am reluctant to judge this without more results

Relative to: PG 17.10

col-1 : PG 18.4

col-2 : PG 19 beta1

col-1 col-2

0.98 0.99 range-covered-pk

0.97 0.99 range-covered-si

0.99 0.99 range-notcovered-pk

1.02 1.01 range-notcovered-si

0.96 1.07 scan

Results: range queries without aggregation, version 12 to 19

Summary

there are no regressions
scan throughput has improved a lot from version 12 to 19

Relative to: PG 12.22

col-1 : PG 13.23

col-2 : PG 14.23

col-3 : PG 15.18

col-4 : PG 16.14

col-5 : PG 17.10

col-6 : PG 18.4

col-7 : PG 19 beta1

col-1 col-2 col-3 col-4 col-5 col-6 col-7

0.99 1.03 1.04 1.04 1.03 1.00 1.02 range-covered-pk

0.99 1.04 1.04 1.04 1.03 1.00 1.03 range-covered-si

1.00 1.00 1.00 0.99 1.00 0.99 0.99 range-notcovered-pk

1.00 1.01 1.01 0.99 1.00 1.02 1.01 range-notcovered-si

1.09 1.27 1.10 1.21 1.19 1.14 1.28 scan

Results: range queries with aggregation, version 17 to 19

Summary

there are no regressions
throughput on the read-only-count test is ~3X better thanks to a new query plan. This improvement was also visible on my small server

Relative to: PG 17.10

col-1 : PG 18.4

col-2 : PG 19 beta1

col-1 col-2

1.03 3.30 read-only-count

1.02 0.99 read-only-distinct

1.00 0.97 read-only-order

0.99 0.99 read-only_range=10

0.99 0.99 read-only_range=100

1.01 1.00 read-only_range=10000

1.03 1.01 read-only-simple

1.03 1.01 read-only-sum

Results: range queries with aggregation, version 12 to 19

Summary

there might be a few small regressions, but losing 5% throughput from version 12 to 19 isn't a big deal
throughput on the read-only-count test is ~3X better thanks to a new query plan. This improvement was also visible on my small server

Relative to: PG 12.22

col-1 : PG 13.23

col-2 : PG 14.23

col-3 : PG 15.18

col-4 : PG 16.14

col-5 : PG 17.10

col-6 : PG 18.4

col-7 : PG 19 beta1

col-1 col-2 col-3 col-4 col-5 col-6 col-7

1.01 0.95 0.96 0.97 0.93 0.95 3.06 read-only-count

1.00 0.98 0.98 0.98 0.96 0.98 0.95 read-only-distinct

1.00 0.98 0.98 1.00 0.99 0.99 0.97 read-only-order

0.99 1.00 1.01 1.00 1.01 0.99 1.00 read-only_range=10

0.99 1.00 1.00 1.00 1.01 1.00 0.99 read-only_range=100

1.00 0.97 1.02 1.03 1.04 1.05 1.03 read-only_range=10000

1.00 0.97 0.99 0.97 0.95 0.98 0.96 read-only-simple

1.00 0.96 0.97 0.97 0.94 0.97 0.95 read-only-sum

Results: writes, version 17 to 19

Summary

there are no regressions

Relative to: PG 17.10

col-1 : PG 18.4

col-2 : PG 19 beta1

col-1 col-2

0.99 0.99 delete

1.02 1.02 insert

1.00 0.98 read-write_range=10

0.99 0.99 read-write_range=100

1.01 1.03 update-index

1.01 0.98 update-inlist

0.98 1.01 update-nonindex

1.01 1.03 update-one

1.00 1.00 update-zipf

0.97 0.99 write-only

Results: writes, version 12 to 19

Summary

there are no regressions
many large improvements arrived in version 17 and remain in 19 beta1

Relative to: PG 12.22

col-1 : PG 13.23

col-2 : PG 14.23

col-3 : PG 15.18

col-4 : PG 16.14

col-5 : PG 17.10

col-6 : PG 18.4

col-7 : PG 19 beta1

col-1 col-2 col-3 col-4 col-5 col-6 col-7

0.99 1.11 1.13 1.10 1.28 1.27 1.27 delete

1.02 1.17 1.16 1.19 1.23 1.25 1.25 insert

1.00 1.20 1.22 1.20 1.24 1.24 1.22 read-write_range=10

0.99 1.04 1.05 1.04 1.06 1.05 1.04 read-write_range=100

0.98 1.08 1.05 0.94 1.84 1.85 1.90 update-index

1.00 1.07 1.06 1.05 1.12 1.13 1.10 update-inlist

1.01 1.07 1.07 0.86 1.87 1.84 1.88 update-nonindex

1.04 0.96 0.96 1.10 1.39 1.41 1.43 update-one

1.01 1.05 1.07 0.96 1.63 1.62 1.63 update-zipf

0.99 1.11 1.13 1.09 1.41 1.37 1.40 write-only

Tuesday, June 16, 2026

The insert benchmark on a small server, IO-bound workload : Postgres 19 beta1

This has results for Postgres versions 19 beta1, 18.4 and 17.10 with the Insert Benchmark on a small server using a cached and CPU-bound workload. I also used MySQL 8.4.8 to see where performance was different.

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

tl;dr

create index (the l.x step) is faster in Postgres 19beta1. A Postgres expert told me that the sort algorithm was changed to be more CPU efficient
the write heavy steps (l.i1, l.i2) are 15% and 9% faster in 19 beta1 vs Postgres 17.10
the second write heavy step (l.i2) is more than 20X faster in MySQL 8.4.8 vs Postgres thanks to the CPU overhead from get_actual_variable_range. I have written about this before.

Builds, configuration and hardware

I compiled Postgres from source using -O2 -fno-omit-frame-pointer for versions 19 beta1, 18.4 and 17.10.

I compiled MySQL 8.4.8 from source as well.

The server is an Beelink SER7 with a Ryzen 7 7840HS CPU with 8 cores and AMD SMT disabled, 32G of RAM. Storage is one SSD for the OS and an NVMe SSD for the database using ext-4 with discard enabled. The OS is Ubuntu 24.04.

For 17.10 the config file is named conf.diff.cx10a_c8r32 (cx10a) and is here.

For Postgres 18 and 19 the config file is conf.diff.cx10b_c8r32 (cx10b) which is as similar as possible to the config for version 17.

For MySQL 8.4.8 the config file is my.cnf.cz12a_c8r32.

The Benchmark

The benchmark is explained here and is run with 1 client.

The point query (qp100, qp500, qp1000) and range query (qr100, qr500, qr1000) steps are run for 3600 seconds each.

The benchmark steps are:

l.i0

insert 800M rows per table in PK order. The table has a PK index but no secondary indexes. There is one connection per client.

create 3 secondary indexes per table. There is one connection per client.

l.i1

use 2 connections/client. One inserts 4M 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 1M 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

The performance summary with charts is here.

This table lists relative QPS per benchmark step and relative QPS is:
(QPS for my version / QPS for Postgres 17.10)

The background in the table cells is blue for big improvements and yellow for regressions. There are no regressions here.

The improvements here for Postgres 19 beta1 are similar to what I reported for the cached workload.

The index create (l.x) step is much faster in 19.10. I usually ignore results on this step but I am curious if something was done in 19.10 to improve index create. A Postgres expert told me that the sort algorithm for index create was changed in version 19 to be more CPU efficient.

For the write-heavy steps (l.i1, l.i2):

there are large improvements in 19 beta1 (15% and 9%). The CPU overhead is lower in 19 beta1 compared to 17.10 (see cpupq here).
throughput for the l.i2 step is more than 20X larger for MySQL than for Postgres. From vmstat I see that the CPU overhead (cpupq here) is more than 10X larger with Postgres vs MySQL. From flamegraphs the problem is the CPU overhead in get_actual_variable_range. I have written about this before (see here). The Postgres query planner uses too much CPU skipping old versions to figure out selectivity for a query and there are too many old versions because Postgres doesn't collect them ASAP, vacuum takes time. The flamegraphs are in subdirectories here.

For the range query steps (qr100, qr500, qr1000) throughput is ~3% less in 19 beta1 vs 17.10 and ~1% less in 18.4 vs 17.10. For 19 beta1 there is a small increase in CPU overhead (see cpupq here, here and here). I already have flamegraphs for MySQL 8.4.8 and Postgres 19 beta1, soon I will have them for Postgres 17.10 and 18.4 to try and explain this.

dbms	l.i0	l.x	l.i1	l.i2	qr100	qp100	qr500	qp500	qr1000	qp1000
PG 17.10	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
PG 18.4	1.01	1.03	1.00	1.00	0.98	1.00	0.99	0.99	0.99	0.99
PG 19 beta1	1.01	1.15	1.05	1.09	0.97	1.01	0.96	1.01	0.97	1.00
MySQL 8.4.8	0.77	0.89	0.76	21.62	0.61	1.07	0.66	0.93	0.85	0.84

Friday, June 12, 2026

HammerDB tproc-c on a large server, Postgres 14 to 19 beta1

This has results for HammerDB tproc-c on a large server using MySQL and Postgres. I am new to HammerDB and still figuring out how to explain and present results so I will keep this simple and just share graphs without explaining the results.

tl;dr

There are small regressions in versions 16, 17 and 18
NOPM usually improves a small amount in 19 beta1 relative to 18

Builds, configuration and hardware

I compiled Postgres versions from source: 14.22, 14.23, 15.17, 15.18, 16.13, 16.14, 17.9, 17.10, 18.0, 18.1, 18.2, 18.3, 18.4 and 19 beta1.

I used a 48-core server from Hetzner

an ax162s with an AMD EPYC 9454P 48-Core Processor with SMT disabled
2 Intel D7-P5520 NVMe storage devices with RAID 1 (3.8T each) using ext4
128G RAM
Ubuntu 24.04

Postgres configuration files:

prior to version 18 the config file is named conf.diff.cx10a50g_c32r128 (x10a_c32r128) and is here for versions 14, 15, 16 and 17.
for Postgres 18 and 19 I used conf.diff.cx10b_c32r128 (x10b_c32r128) with io_method=sync to be similar to the config used for versions 14 through 17.

Benchmark

The benchmark is tproc-c from HammerDB. The tproc-c benchmark is derived from TPC-C.

The benchmark was run for several workloads:

vu=10, wh=1000 - 10 virtual users, 1000 warehouses
vu=20, wh=1000 - 20 virtual users, 1000 warehouses
vu=40, wh=1000 - 40 virtual users, 1000 warehouses
vu=10, wh=2000 - 10 virtual users, 2000 warehouses
vu=20, wh=2000 - 20 virtual users, 2000 warehouses
vu=40, wh=2000 - 40 virtual users, 2000 warehouses
vu=10, wh=4000 - 10 virtual users, 4000 warehouses
vu=20, wh=4000 - 20 virtual users, 4000 warehouses
vu=40, wh=4000 - 40 virtual users, 4000 warehouses

The wh=1000 workloads are less heavy on IO. The wh=4000 workloads are more heavy on IO.

The benchmark for Postgres is run by a variant of this script which depends on scripts here.

stored procedures are enabled
partitioning is used because the warehouse count is >= 1000
a 5 minute rampup is used
then performance is measured for 60 minutes

Results

My analysis at this point is simple -- I only consider average throughput. Eventually I will examine throughput over time and efficiency (CPU and IO).

On the charts that follow y-axis does not start at 0 to improve readability at the risk of overstating the differences. The y-axis shows relative throughput. There might be a regression when the relative throughput is less than 1.0. There might be an improvement when it is > 1.0. The relative throughput is:

(NOPM for some-version / NOPM for base-version)

The base version is Postgres 14.22.

A spreadsheet with absolute and relative values for NOPM is here.

Results: vu=10, wh=1000

Summary:

There are small regressions in versions 16, 17 and 18 while NOPM improves is 19 beta1

Results: vu=20, wh=1000

Summary:

There are small regressions in versions 16, 17 and 18 while NOPM improves is 19 beta1

Results: vu=40, wh=1000

Summary:

There are small regressions in versions 17 and 18 while NOPM improves is 19 beta1

Results: vu=10, wh=2000

Summary:

There are small regressions in version 18 while NOPM improves is 19 beta1

Results: vu=20, wh=2000

Summary:

There are small regressions in versions 16, 17 and 18 while NOPM improves is 19 beta1

Results: vu=40, wh=2000

Summary:

There are small regressions in versions 16, 17 and 18 while NOPM improves is 19 beta1
There is no result for 18.1 because of a bug in my test scripts

Results: vu=10, wh=4000

Summary:

There are small regressions in versions 16, 17 and 18 while NOPM improves is 19 beta1

Results: vu=20, wh=4000

Summary:

There are small regressions in versions 16, 17 and 18

Results: vu=40, wh=4000

Summary:

There are small regressions in versions 16, 17 and 18 while NOPM improves is 19 beta1

Thursday, June 11, 2026

Write-heavy sysbench tests, a large server, modern Postgres and MySQL

This has results for modern Postgres and MySQL using write-heavy tests from sysbench and a large server. I think there are regressions in Postgres that arrive in some of versions 16, 17, 18 and 19 beta1 but I am far from certain and this blog post is just another step in my journey to figure that out.

tl;dr

Postgres suffers a lot from throughput variation while MySQL+InnoDB does not
InnoDB gets much better average throughput on 6 of 10 tests, similar throughput one one and then Postgres does better on 3 of 10 tests
For tests from which I provided vmstat and iostat results, Postgres does more write IO per operation. In some cases InnoDB uses more CPU, in other cases it does not.

Builds, configuration and hardware

I compiled:

Postgres from source for versions 15.17, 16.13, 17.9 and 18.3.
MySQL from source for version 8.4.7

I used a 48-core server from Hetzner

an ax162s with an AMD EPYC 9454P 48-Core Processor with SMT disabled
2 Intel D7-P5520 NVMe storage devices with RAID 1 (3.8T each) using ext4
128G RAM
Ubuntu 24.04

Configuration files for Postgres:

the config file is named conf.diff.cx10a_c32r128 (x10a_c32r128) and is here for versions 15, 16 and 17.
for Postgres 18 I used conf.diff.cx10b_c32r128 (x10b_c32r128) which is as close as possible to the Postgres 17 config and uses io_method=sync

Benchmark

I used sysbench and my usage is explained here. Normally I run 32 of the 42 microbenchmarks listed in that blog post using tables small enough to be cached by the DBMS. Most test only one type of SQL statement.

But I did things differently here:

I only run the write-heavy tests (to save time)
The tables are larger than memory and cannot be cached
Each test (microbenchmark) is run for 2 hours when I normally run each for 15 minutes
After each test a vacuum is done

The purpose is to search for regressions from new CPU overhead and mutex contention related to MVCC GC (vacuum for Postgres, purge for InnoDB).

Results

I provide charts below with relative QPS. The relative QPS is the following:

(QPS for some version) / (QPS for Postgres 15.17)

When the relative QPS is > 1 then some version is faster than base version. When it is < 1 then there might be a regression. When the relative QPS is 1.2 then some version is about 20% faster than base version.

The per-test results from vmstat and iostat can help to explain why something is faster or slower because it shows how much HW is used per request, including CPU overhead per operation (cpu/o) and context switches per operation (cs/o) which are often a proxy for mutex contention.

Results: writes

The table below has relative QPS for Postgres 16 to 19 and then InnoDB all relative to the throughput for Postgres 15.17. Columns 1 to 4 have results for Postgres and the numbers in yellow highlight the tests where there is a regression in Postgres. For column 5 (MySQL with InnoDB) the numbers in yellow and red indicate tests where InnoDB's throughput is less than Postgres. And then the numbers in green indicate tests where InnoDB's throughput is much larger than Postgres.

Note that when relative QPS (rQPS) is 0.90 then throughput dropped by ~10%.

Summary:

throughput for Postgres drops after version 15.17. I don't know yet whether this is a regression.
throughput for InnoDB is much better than Postgres in 6 of 10 tests, similar in one test, and much worse in 3 of 10 tests.

The sections that follow this one have more detail on results from the update-index, update-zipf tests and insert tests.

Relative to: Postgres 15.17

col-1 : Postgres 16.13

col-2 : Postgres 17.9

col-3 : Postgres 18.3

col-4 : Postgres 19 beta1

col-5 : MySQL 8.4.7

col-1 col-2 col-3 col-4 col-5

0.94 0.97 0.98 1.02 1.88 update-inlist

0.94 0.90 0.88 0.92 1.43 update-index

0.91 0.86 0.87 0.92 1.19 update-nonindex

0.96 0.99 0.98 0.98 0.71 update-one

0.92 0.83 0.81 0.85 0.93 update-zipf

0.95 0.93 0.84 0.81 1.71 write-only

0.94 0.94 0.90 0.92 1.14 read-write_range=10

0.95 0.96 0.95 0.95 1.93 read-write_range=100

0.89 0.82 0.80 0.84 1.01 delete

1.05 1.05 1.01 1.10 0.53 insert

Results: update-index

Summary:

Postgres suffers from too much variance
Average throughput is ~1.55X larger for InnoDB than for Postgres
Per operation, Postgres does ~1.20X more write IO (KB written) to storage than InnoDB
Per operation, InnoDB uses more CPU and does more context switches. While autovacuum was enabled and was likely running during the test, my measurements exclude the manual vacuum done at the end of each test.

iostat, vmstat normalized by operation rate

r/s rMB/s w/s wMB/s r/o rKB/o wKB/o o/s dbms

35503.0 373.7 58795.7 1345.1 1.375 14.824 53.351 25817 PG 19b1

33140.6 517.8 53449.6 1735.3 0.827 13.226 44.326 40090 MySQL 8.4.7

cs/s cpu/s cs/o cpu/o dbms

176167 14.4 6.824 .000557 PG 19b1

661395 41.9 16.498 .001046 MySQL 8.4.7

Results: update-zipf

Summary:

Postgres suffers from too much variance
Average throughput is ~1.09X larger for InnoDB than for Postgres
Per operation, Postgres does ~1.30X more write IO (KB written) to storage than InnoDB
Per operation, InnoDB uses more CPU and does more context switches. While autovacuum was enabled and was likely running during the test, my measurements exclude the manual vacuum done at the end of each test.

iostat, vmstat normalized by operation rate

r/s rMB/s w/s wMB/s r/o rKB/o wKB/o o/s dbms

55595.5 620.7 64264.4 1352.3 0.622 7.110 15.490 89396 PG 19b1

27405.9 428.2 37465.1 1133.6 0.282 4.508 11.933 97270 MySQL 8.4.7

cs/s cpu/s cs/o cpu/o dbms

424392 27.2 4.747 .000304 PG 19b1

1213054 44.5 12.471 .000458 MySQL 8.4.7

Results: insert

Summary:

Postgres suffers from too much variance
Average throughput is ~2.06X larger for Postgres than for InnoDB
Per operation, Postgres does ~1.67X more write IO (KB written) to storage than InnoDB
Per operation, Postgres uses more CPU and does more context switches. This is the opposite of what happens above for update-index and update-zipf.

iostat, vmstat normalized by operation rate

r/s rMB/s w/s wMB/s r/o rKB/o wKB/o o/s dbms

1615.5 56.0 15321.7 1170.9 0.007 0.242 5.059 237009 PG 19b1

3.6 0.1 8275.4 340.7 0.000 0.000 3.029 115155 MySQL 8.4.7

cs/s cpu/s cs/o cpu/o dbms

1214563 46.0 10.547 .000399 PG 19b1

800827 50.5 3.379 .000213 MySQL 8.4.7

The insert benchmark on a small server, cached workload : Postgres 19 beta1

This has results for Postgres versions 19 beta1, 18.4 and 17.10 with the Insert Benchmark on a small server using a cached and CPU-bound workload.

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

tl;dr

I don't see regressions here in 19 beta1
I see some improvements here in 19 beta1

index create (l.x) is faster but the step is short-running so I don't assume much from this
the write-heavy steps (l.i1, l.i2) are faster and CPU overhead is lower in 19 beta1, I hope to explain why the CPU overhead is lower, but that waits for another day.

Builds, configuration and hardware

I compiled Postgres from source using -O2 -fno-omit-frame-pointer for versions 19 beta1, 18.4 and 17.10.

For 17.10 the config file is named conf.diff.cx10a_c8r32 (cx10a) and is here.

For Postgres 18 and 19 the config file is conf.diff.cx10b_c8r32 (cx10b) which is as similar as possible to the config for version 17.

The Benchmark

The benchmark is explained here and is run with 1 client.

The point query (qp100, qp500, qp1000) and range query (qr100, qr500, qr1000) steps are run for 3600 seconds each.

The benchmark steps are:

l.i0

insert 30M rows per table in PK order. The table has a PK index but no secondary indexes. There is one connection per client.

create 3 secondary indexes per table. There is one connection per client.

l.i1

use 2 connections/client. One inserts 40M 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 10M 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

The performance summary with charts is here.

The index create (l.x) step is much faster in 19.10. I usually ignore results on this step but I am curious if something was done in 19.10 to improve index create. But this step takes between 1 and 2 minutes and I am reluctant to assume too much from a short running step.

For the write-heavy steps (l.i1, l.i2)

there are small improvements in 18.4
there are large improvements in 19 beta1. The CPU overhead is lower in 19 beta1 compared to 17.10, ~15% lower for l.i1 and ~10% lower for l.i2. Hopefully I can explain why. But the lower CPU overhead might explain the improved performance in 19 beta1. Some of the metrics from iostat and vmstat are here.

dbms	l.i0	l.x	l.i1	l.i2	qr100	qp100	qr500	qp500	qr1000	qp1000
17.10	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
18.4	1.00	1.03	1.02	1.07	0.99	1.00	1.00	1.00	1.01	1.00
19 beta1	1.01	1.16	1.23	1.22	0.99	1.00	0.99	0.99	1.00	1.00

Tuesday, June 9, 2026

Postgres 19 beta1 vs sysbench on a small server

This has results from sysbench on a small server with Postgres 19 beta1, 18.4 and 17.10. Sysbench is run with low concurrency (1 thread) and a cached database. The purpose is to search for changes in performance, often from new CPU overheads.

tl;dr

19beta1, 18.4 and 17.10 have mostly similar performance
There might be small regressions (about 2%) from 17.10 to 19beta1 but my tests are not good at spotting that.
19beta1 is much faster on one test (read-only-count) thanks to a new query plan

Builds, configuration and hardware

I compiled Postgres from source.

The server is a Beelink SER7 7840HS with an AMD Ryzen 7 7840HS CPU and 32G RAM. Storage uses an NVMe device with ext-4 and discard enabled. The OS is Ubuntu 24.04.

The config files are here for 17.10, 18.4 and 19 beta1.

Benchmark

I used sysbench and my usage is explained here. To save time I only run 32 of the 42 microbenchmarks

and most test only 1 type of SQL statement. Benchmarks are run with the database cached by Postgres.

The read-heavy microbenchmarks run for 600 seconds and the write-heavy for 900 seconds.

The benchmark is run with 1 client, 1 table and 50M rows. The purpose is to search for CPU regressions.

Results

I provide charts below with relative QPS (rQPS). The relative QPS is the following:

(QPS for some version) / (QPS for base version)

Here, base version is Postgres 17.10 and some version is either 18.4 or 19 beta1.

I describe performance changes (changes to relative QPS) in terms of basis points. Performance changes by one basis point when the difference in rQPS is 0.01. When rQPS decreases from 0.95 to 0.85 then it changed by 10 basis points.

Results: point queries

Summary:

19beta1 is better than 17.10 by ~3 basis points for most tests
19beta1 is slightly better than 18.4

Relative to Postgres 17.10

col-1 : Postgres 18.4

col-2 : Postgres 19 beta1

col-1 col-2

1.01 1.00 hot-points

0.98 0.97 point-query

1.01 1.03 points-covered-pk

1.00 1.04 points-covered-si

1.00 1.02 points-notcovered-pk

1.00 1.03 points-notcovered-si

0.99 0.99 random-points_range=10

1.00 1.03 random-points_range=100

1.01 1.03 random-points_range=1000

Results: range queries without aggregation

Summary:

19beta1 is worse than 17.10 by ~3 basis points in 4 of 5 tests
19beta1 is better than 17.10 by 5 basis points in the scan test
19beta1 and 18.4 are similar except for the scan test where 19beta1 did better

Relative to Postgres 17.10

col-1 : Postgres 18.4

col-2 : Postgres 19 beta1

col-1 col-2

0.98 0.97 range-covered-pk

0.96 0.96 range-covered-si

0.98 0.98 range-notcovered-pk

0.99 0.99 range-notcovered-si

0.95 1.05 scan

Results: range queries with aggregation

Summary:

19beta1 is worse than than 17.10 on two tests
19beta1 is better than 17.10 on five tests
19beta1 and 17.10 are the same on one test
19beta1 is ~2.5X better than 17.10 on the read-only-count test
19beta1 and 18.4 have similar results except for the read-only-count test

The query for the read-only-count test appears to have a different plan in 19beta1 and that might explain the ~2.5X speedup. In 17.10 and 18.4 it gets Index Scan while in 19beta1 it gets Index Only Scan.

Query plans for the read-only-count test ...

For 17.10

explain SELECT count(c) FROM sbtest1 WHERE id BETWEEN 17704460 AND 17705459

Aggregate (cost=1424.42..1424.43 rows=1 width=8)

-> Index Scan using sbtest1_pkey on sbtest1 (cost=0.56..1421.93 rows=996 width=121)

Index Cond: ((id >= 17704460) AND (id <= 17705459))

For 18.4

explain SELECT count(c) FROM sbtest1 WHERE id BETWEEN 11575278 AND 11576277

Aggregate (cost=1310.09..1310.10 rows=1 width=8)

-> Index Scan using sbtest1_pkey on sbtest1 (cost=0.56..1307.89 rows=882 width=121)

Index Cond: ((id >= 11575278) AND (id <= 11576277))

For 19beta1

explain SELECT count(c) FROM sbtest1 WHERE id BETWEEN 11686801 AND 11687800

Aggregate (cost=32.32..32.33 rows=1 width=8)

-> Index Only Scan using sbtest1_pkey on sbtest1 (cost=0.56..30.13 rows=878 width=0)

Index Cond: ((id >= 11686801) AND (id <= 11687800))

Relative to Postgres 17.10

col-1 : Postgres 18.4

col-2 : Postgres 19 beta1

col-1 col-2

1.04 2.47 read-only-count

1.00 0.99 read-only-distinct

1.02 1.01 read-only-order

0.98 0.97 read-only_range=10

1.00 1.00 read-only_range=100

1.02 1.03 read-only_range=10000

1.09 1.09 read-only-simple

1.01 1.01 read-only-sum

Results: writes

Summary:

19beta1 is worse than 17.10 by 2 to 5 basis points
18.4 is worse than 17.10 by 2 to 3 basis points

Relative to Postgres 17.10

col-1 : Postgres 18.4

col-2 : Postgres 19 beta1

col-1 col-2

0.97 0.97 delete

0.99 0.96 insert

0.98 0.97 read-write_range=10

0.98 0.98 read-write_range=100

0.96 0.95 update-index

0.99 0.97 update-inlist

0.97 0.96 update-nonindex

0.97 0.95 update-one

0.97 0.95 update-zipf

0.98 0.97 write-only

Friday, April 10, 2026

MySQL 9.7.0 vs sysbench on a small server

This has results from sysbench on a small server with MySQL 9.7.0 and 8.4.8. Sysbench is run with low concurrency (1 thread) and a cached database. The purpose is to search for changes in performance, often from new CPU overheads.

I tested MySQL 9.7.0 with and without the hypergraph optimizer enabled. I don't expect it to help much because the queries run here are simple. I hope to learn it doesn't hurt performance in that case.

tl;dr

Throughput improves on two tests with the Hypergraph optimizer in 9.7.0 because they get better query plans.
One read-only test and several write-heavy tests have small regressions from 8.4.8 to 9.7.0. This might be from new CPU overheads but I don't see obvious problems in the flamegraphs.

Builds, configuration and hardware

I compiled MySQL from source for versions \8.4.8 and 9.7.0.

The server is an ASUS ExpertCenter PN53 with AMD Ryzen 7 7735HS, 32G RAM and an m.2 device for the database. More details on it are here. The OS is Ubuntu 24.04 and the database filesystem is ext4 with discard enabled.

The my.cnf files os here for 8.4. I call this the z12a configs and variants of it are used for MySQL 5.6 through 8.4.

For 9.7 I use two configs:

z13a

This is as close as possible to z12a and adds two options to undo changes to the default values for two gtid-related options that arrived in 9.6.

z13b

This is like z13a but then enables the hypergraph optimizer

All DBMS versions use the latin1 character set as explained here.

Benchmark

I used sysbench and my usage is explained here. To save time I only run 32 of the 42 microbenchmarks and most test only 1 type of SQL statement. Benchmarks are run with the database cached by InnoDB.

The tests are run using 1 table with 50M rows. The read-heavy microbenchmarks run for 600 seconds and the write-heavy for 1800 seconds.

Results

I provide tables below with relative QPS. When the relative QPS is > 1 then some version is faster than the base version. When it is < 1 then there might be a regression. The relative QPS (rQPS) is:

(QPS for some version) / (QPS for MySQL 8.4.8)

Results: point queries

I describe performance changes (changes to relative QPS, rQPS) in terms of basis points. Performance changes by one basis point when the difference in rQPS is 0.01. When rQPS decreases from 0.95 to 0.85 then it changed by 10 basis points.

This shows the rQPS for MySQL 9.7.0 using both the z13a and z13b configs. It is relative to the throughput from MySQL 8.4.8.

Throughput with MySQL 9.7.0 is similar to 8.4.8 except for point-query where there are regressions as rQPS drops by 5 and 7 basis points. The point-query test uses simple queries that fetch one column from one row by PK. From vmstat metrics the CPU overhead per query for 9.7.0 is ~8% larger than for 8.4.8, with and without the hypergraph optimizer. I don't see anything obvious in the flamegraphs.

z13a z13b

0.99 1.01 hot-points

0.95 0.93 point-query

0.99 1.01 points-covered-pk

1.00 1.01 points-covered-si

0.98 1.00 points-notcovered-pk

0.99 1.01 points-notcovered-si

1.00 1.02 random-points_range=1000

0.99 1.01 random-points_range=100

0.96 1.00 random-points_range=10

Results: range queries without aggregation

I describe performance changes (changes to relative QPS, rQPS) in terms of basis points. When rQPS decreases from 0.95 to 0.85 then it changed by 10 basis points.

This shows the rQPS for MySQL 9.7.0 using both the z13a and z13b configs. It is relative to the throughput from MySQL 8.4.8.

Throughput with MySQL 9.7.0 is similar to 8.4.8. I am skeptical there is a regression for the scan test with the z13b config. I suspect that is noise.

z13a z13b

0.99 0.99 range-covered-pk

0.99 0.99 range-covered-si

0.99 0.99 range-notcovered-pk

0.98 0.98 range-notcovered-si

1.00 0.96 scan

Results: range queries with aggregation

I describe performance changes (changes to relative QPS, rQPS) in terms of basis points. When rQPS decreases from 0.95 to 0.85 then it changed by 10 basis points.

This shows the rQPS for MySQL 9.7.0 using both the z13a and z13b configs. It is relative to the throughput from MySQL 8.4.8.

There might be small regressions in several tests with rQPS dropping by a few points but I will ignore that for now.
There is a large improvement for the read-only-distinct test with the z13b config. The query for this test is select distinct c from sbtest where id between ? and ? order by c. The reason for the performance improvment is that the hypergraph optimizer chooses a better plan, see here.
There is a large improvement for the read-only test with range=10000. This test uses the read-only version of the classic sysbench transaction (see here). One of the queries it runs is the query used by read-only-distinct. So it benefits from the better plan for that query.

z13a z13b

0.97 0.97 read-only-count

0.98 1.26 read-only-distinct

0.96 0.95 read-only-order

0.99 1.15 read-only_range=10000

0.97 1.00 read-only_range=100

0.96 0.97 read-only_range=10

0.99 0.99 read-only-simple

0.97 0.96 read-only-sum

Results: writes

I describe performance changes (changes to relative QPS, rQPS) in terms of basis points. When rQPS decreases from 0.95 to 0.85 then it changed by 10 basis points.

This shows the rQPS for MySQL 9.7.0 using both the z13a and z13b configs. It is relative to the throughput from MySQL 8.4.8.

There might be several small regressions here. I don't see obvious problems in the flamegraphs.

z13a z13b

0.95 0.92 delete

1.00 1.01 insert

0.97 0.98 read-write_range=100

0.96 0.95 read-write_range=10

0.97 0.96 update-index

0.97 0.92 update-inlist

0.95 0.93 update-nonindex

0.95 0.92 update-one

0.95 0.93 update-zipf

0.97 0.95 write-only