Small Datum

Friday, April 3, 2026

CPU-bound sysbench on a large server: Postgres, MySQL and MariaDB

This post has results for CPU-bound sysbench vs Postgres, MySQL and MariaDB on a large server using older and newer releases.

The goal is to measure:

how performance changes over time from old versions to new versions
performance between modern MySQL, MariaDB and Postgres

The context here is a collection of microbenchmarks using a large server with high concurrency. Results on other workloads might be different. But you might be able to predict performance for a more complex workload using the data I share here.

tl;dr

for point queries

Postgres is faster than MySQL, MySQL is faster than MariaDB
modern MariaDB suffers from huge regressions that arrived in 10.5 and remain in 12.x

for range queries without aggregation

MySQL is about as fast as MariaDB, both are faster than Postgres (often 2X faster)

for range queries with aggregation

MySQL is about as fast as MariaDB, both are faster than Postgres (often 2X faster)

for writes

Postgres is much faster than MariaDB and MySQL (up to 4X faster)
MariaDB is between 1.3X and 1.5X faster than MySQL

on regressions

Postgres tends to be boring with few regressions from old to new versions
MySQL and MariaDB are exciting, with more regressions to debug

Hand-wavy summary

My hand-wavy summary about performance over time has been the following. It needs a revision, but also needs to be concise.

Modern Postgres is about as fast as old Postgres, with some improvements. It has done great at avoiding perf regressions.

Modern MySQL at low concurrency has many performance regressions from new CPU overheads (code bloat). At high concurrency it is faster than old MySQL because the improvements for concurrency are larger than the regressions from code bloat.

Modern MariaDB at low concurrency has similar perf as old MariaDB. But at high concurrency it has large regressions for point queries, small regressions for range queries and some large improvements for writes. Note that many things use point queries internally - range scan on non-covering index, updates, deletes. The regressions arrive in 10.5, 10.6, 10.11 and 11.4.

For results on a small server with a low concurrency workload, I have many posts including:

MySQL and MariaDB from 2024
MySQL-only from 2026
Postgres from 2025

Builds, configuration and hardware

I compiled:

Postgres from source for versions 12.22, 13.23, 14.21, 15.16, 16.12, 17.8 and 18.2.
MySQL from source for versions 5.6.51, 5.7.44, 8.0.44, 8.4.7 and 9.5.0
MariaDB from source for versions 10.2.30, 10.2.44, 10.3.39, 10.4.34, 10.5.29, 10.6.25, 10.11.15, 11.4.10, 11.8.6, 12.2.2 and 12.3.1

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 22.04 running the non-HWE kernel (5.5.0-118-generic). The server has since been updated to Ubuntu 24.04 and I am repeating tests.

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 I used conf.diff.cx10b_c32r128 (x10b_c32r128) which is as close as possible to the Postgres 17 config and uses io_method=sync

The my.cnf files for MySQL are here: 5.6.51, 5.7.44, 8.0.4x, 8.4.x, 9.x.0

The my.cnf files for MariaDB are here: 10.2, 10.3, 10.4, 10.5, 10.6, 10.11, 11.4, 11.8, 12.2, 12.3.

I thought I was using the latin1 charset for all versions of MariaDB and MySQL but I recently learned I was using somehting like utf8mb4 on recent versions (maybe MariaDB 11.4+ and MySQL 8.0+). See here for details. I will soon repeat tests using latin1 for all versions. For some tests, the use of a multi-byte charset increases CPU overhead by up to 5%, which reduces throughput by a similar amount.

With Postgres I have been using a multi-byte charset for all versions.

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 900 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 without aggregation while part 2 has queries with aggregation.

I provide charts below with relative QPS. 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. 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.

The spreadsheet with charts is here and in some cases is easier to read than the charts below. Files with performance summaries are archived here.

The relative QPS numbers are also here for:

Files with HW efficiency numbers, average values from vmstat and iostat normalized by QPS, are here for:

Results: MySQL vs MariaDB vs Postgres

HW efficiency metrics are here. They have metrics from vmstat and iostat normalized by QPS.

Point queries

Postgres is faster than MySQL is faster than MariaDB
MySQL gets about 2X more QPS than MariaDB on 5 of the 9 tests
a table for relative QPS by test is here
from HW efficiency metrics for the random-points.range1000 test:

Postgres is 1.35X faster than MySQL, MySQL is more than 2X faster than MariaDB
MariaDB uses 2.28X more CPU and does 23.41X more context switches than MySQL
Postgres uses less CPU but does ~1.93X more context switches than MySQL

Range queries without aggregation

MySQL is about as fast as MariaDB, both are faster than Postgres (often 2X faster)
MariaDB has lousy results on the range-notcovered-si test because it must do many point lookups to fetch columns not in the index and MariaDB has problems with point queries at high concurrency
a table for relative QPS by test is here
from HW efficiency metrics for the scan:

MySQL is 1.2X faster than Postgres and 1.5X faster than MariaDB
MariaDB uses 1.19X more CPU and does ~1000X more context switches than MySQL
Postgres uses 1.55X more CPU but does few context switches than MySQL

Range queries with aggregation

MySQL is about as fast as MariaDB, both are faster than Postgres (often 2X faster)
a table for relative QPS by test is here
from HW efficiency metrics for read-only-count

MariaDB is 1.22X faster than MySQL, MySQL is 4.2X faster than Postgres
MariaDB uses 1.22X more CPU than MySQL but does ~2X more context switches
Postgres uses 4.11X more CPU than MySQL and does 1.08X more context switches
Query plans are here and MySQL + MariaDB benefit from the InnoDB clustered index

from HW efficiency metrics for read-only.range=10

MariaDB is 1.22X faster than MySQL, MySQL is 4.2X fasterMySQL is 1.2X faster than Postgres and 1.5X faster than MariaDB
MariaDB uses 1.19X more CPU and does ~1000X more context switches than MySQL
Postgres uses 1.55X more CPU but does few context switches than MySQL

Writes

Postgres is much faster than MariaDB and MySQL (up to 4X faster)
MariaDB is between 1.3X and 1.5X faster than MySQL
a table for relative QPS by test is here
from HW efficiency metrics for insert

Postgres is 3.03X faster than MySQL, MariaDB is 1.32X faster than MySQL
MySQL uses ~1.5X more CPU than MariaDB and ~2X more CPU than Postgres
MySQL does ~1.3X more context switches than MariaDB and ~2.9X more than Postgres

Results: MySQL

HW efficiency metrics are here. They have metrics from vmstat and iostat normalized by QPS.

Point queries

For 7 of 9 tests QPS is ~1.8X larger or more in 5.7.44 than in 5.6.51
For 2 tests there are small regressions after 5.6.51 -- points-covered-si & points-notcovered-si
a table for relative QPS by test is here
from HW efficiency metrics for points-covered-si:

the regression is explained by an increase in CPU

Range queries without aggregation

there is a small regression from 5.6 to 5.7 and a larger one from 5.7 to 8.0
a table for relative QPS by test is here
from HW efficiency metrics for range-covered-pk:

CPU overhead grows by up to 1.4X after 5.6.51, this is true for all of the tests

Range queries with aggregation

regressions after 5.6.51 here are smaller than in the other groups, but 5.7 tends to do better than 8.0, 8.4 and 9.5
a table for relative QPS by test is here
HW efficiency metrics are here for read-only_range=100

QPS changes because CPU/query changes

Writes

QPS improves after 5.6 by up to ~7X
a table for relative QPS by test is here
HW efficiency metrics are here insert

QPS improves after 5.6.51 because CPU per statement drops

Results: MariaDB

HW efficiency metrics are here. The have metrics from vmstat and iostat normalized by QPS.

Point queries

QPS for 6 of 9 tests drops in half (or more) from 10.2 to 12.3
a table for relative QPS is here
most of the regressions arrive in 10.5 and the root cause might be remove support for innodb_buffer_pool_intances and only support one buffer pool instance
HW efficiency metrics are here for points-covered-pk

there are large increases in CPU overhead and the context switch rate starting in 10.5

Range queries without aggregation

for range-covered-* and range-notcovered-pk there is a small regression in 10.4
for range-not-covered-si there is a large regression in 10.5 because this query does frequent point lookups on the PK to get missing columns
for scan there is a regression in 10.5 that goes away, but the regressions return in 10.11 and 11.4
a table for relative QPS by test is here
HW efficiency metrics are here

Range queries with aggregation

for most tests there are small regressions in 10.4 and 10.5
a table for relative QPS by test is here
HW efficiency metrics are here

Writes

for most tests modern MariaDB is faster than 10.2
table for relative QPS by test is here
HW efficiency metrics are here

Results: Postgres

HW efficiency metrics are here. They have metrics from vmstat and iostat normalized by QPS.

Point queries

QPS for hot-points increased by ~2.5X starting in Postgres 17.x
otherwise QPS is stable from 12.22 through 18.2
a table for relative QPS by test is here
HW efficiency metrics for the hot-points test are here

CPU drops by more than half starting in 17.x

Range queries without aggregation

QPS is stable for the range-not-covered-* and scan tests
QPS drops almost in half for the range-covered-* tests
a table for relative QPS by test is here
all versions use the same query plan for the range-covered-pk test
HW efficiency metrics are here for range-covered-pk and for range-covered-si

An increase in CPU overhead explains the regressions for range-covered-*
I hope to get flamegraphs and thread stacks for these tests to explain what happens

Range queries with aggregation

QPS is stable from 12.22 through 18.2
a table for relative QPS by test is here
HW efficiency metrics are here

Writes

QPS is stable for 5 of 10 tests
QPS improves by up to 1.7X for the other 5 tests, most of that arrives in 17.x
a table for relative QPS by test is here
HW efficiency metrics are here for update-index

CPU overhead and context switch rates drop almost in half starting in 17.x

Tuesday, March 31, 2026

gcc vs clang for sysbench on a small server with Postgres, MySQL and MariaDB

This has results for sysbench on a small server and compares performanc for Postgres, MySQL and MariaDB compiled using clang vs using gcc.

tl;dr

Throughput with clang and gcc is similar

Builds, configuration and hardware

I compiled Postgres 18.3, MySQL 8.4.8 and MariaDB 11.8.6 from source. The server has 8 AMD cores with SMT disabled and 32G of RAM. The OS is Ubuntu 24.04, gcc is version 13.3.0 and clang is version 18.1.3. Storage is ext-4 with discard enabled and an NVMe SSD.

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 client and 1 table with 50M rows. The read-heavy microbenchmarks run for 630 seconds and the write-heavy for 930 seconds.

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 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 number below are the relative QPS computed as: (QPS with a gcc build / QPS with a clang build)

Legend:
* pg - for Postgres 18.3, (QPS with gcc / QPS with clang)
* my - for MySQL 8.4.8, (QPS with gcc / QPS with clang)
* ma - for MariaDB 11.8.6, (QPS with gcc / QPS with clang)

-- point queries
pg my ma
1.02 1.00 0.99 hot-points
1.02 0.98 1.02 point-query
0.95 1.01 1.02 points-covered-pk
0.96 1.02 1.02 points-covered-si
0.97 1.00 1.02 points-notcovered-pk
0.96 1.03 1.02 points-notcovered-si
0.97 1.01 1.01 random-points_range=1000
0.98 1.01 1.01 random-points_range=100
1.00 0.99 1.00 random-points_range=10

-- range queries without aggregation
pg my ma
1.01 0.98 1.03 range-covered-pk
1.00 0.98 1.05 range-covered-si
0.99 0.98 1.04 range-notcovered-pk
0.99 1.02 0.97 range-notcovered-si
1.02 1.06 1.03 scan

-- range queries with aggregation
pg my ma
1.01 0.96 1.05 read-only-count
0.99 0.99 1.01 read-only-distinct
0.99 1.00 1.00 read-only-order
0.99 1.00 1.01 read-only_range=10000
1.00 0.98 1.00 read-only_range=100
1.01 0.97 1.00 read-only_range=10
0.99 0.97 1.03 read-only-simple
1.02 0.98 1.02 read-only-sum

-- writes
pg my ma
1.03 0.98 1.00 delete
1.01 1.00 1.00 insert
1.00 0.98 1.00 read-write_range=100
1.00 0.98 1.00 read-write_range=10
0.99 1.01 0.97 update-index
0.96 1.01 0.99 update-inlist
0.99 0.99 0.99 update-nonindex
1.02 0.98 0.99 update-one
0.98 0.98 0.99 update-zipf
1.00 0.99 0.99 write-only

Sunday, March 29, 2026

The insert benchmark on a small server : Postgres 12.22 through 18.3

This has results for Postgres versions 12.22 through 18.3 with the Insert Benchmark on a small server. My previous post for the same hardware with results up to Postgres 18.1 is here. This post also has results for:

all 17.x releases from 17.0 through 17.9
18.2 with and without full page writes enabled
both 1 and 4 users

Postgres continues to be boring in a good way. It is hard to find performance regressions. Performance wasn't always stable, but I am reluctant to expect it to show no changes because there are sources of variance beyond the DBMS, especially HW (a too-hot SSD or CPU will run slower). Sometimes perf changes because there are obvious perf bugs, sometimes it changes for other reasons.

tl;dr for a CPU-bound workload

performance is stable from Postgres 12 through 18
performance is stable from Postgres 17.0 through 17.9
disabling full-page writes improves throughput on write-heavy benchmark steps

tl;dr for an IO-bound workload

performance is mostly stable from Postgres 12 through 18
performance is stable from Postgres 17.0 through 17.9
disabling full-page writes improves throughput on write-heavy benchmark steps
in a few cases there are large improvements to point-query throughput on the qp1000 benchmark step. I will try to explain that soon.

Builds, configuration and hardware

I compiled Postgres from source using -O2 -fno-omit-frame-pointer for versions 12.22, 13.23, 14.22, 15.17, 16.13, 17.0 to 17.9, 18.2 and 18.3.

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 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 in most cases I used a config named conf.diff.cx10b_c8r32 (aka cx10b) which is as similar as possible to the configs for versions 17 and earlier. But for tests with full-page writes disabled I used additional configs to compare with results from the cx10b config.

cx10b_fpw0

this adds full_page_writes=off while cx10b has =on

cx10b_wallz4

this adds wal_compression=lz4 while cx10b has =off

cx10b_fpw0_wallz4

this adds full_page_writes=off and wal_compression=lz4

The Benchmark

The benchmark is explained here and is run with 1 client 4 clients. In each case each client uses a separate table. I repeated it with two workloads:

CPU-bound

for 1 user the values for X, Y, Z are 30M, 40M, 10M
for 4 users the values for X, Y, Z are 10M, 16M, 4M

IO-bound

for 1 user the values for X, Y, Z are 800M, 4M, 1M
for 4 users the values for X, Y, Z are 200M, 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.

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:

CPU-bound

Latest point releases: 1 user and 4 users
All 17.x releases: 1 user and 4 users
Full-page writes enabled and disabled: 1 user and 4 users

IO-bound

Latest point releases: 1 user and 4 users
All 17.x releases: 1 user and 4 users
Full-page writes enabled and disabled: 1 user and 4 users

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. The base version is Postgres 12.22 for the latest point releases comparison, 17.0 for the 17.x releases comparison and 18.2 with the cx10b config for the full-page writes comparison.

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: CPU-bound

The performance summaries are here for:

1 user: latest point releases, all 17.x releases and full-page writes
4 users: latest point releases, all 17.x releases and full-page writes

For latest point releases at 1 user

there is either no change or a small improvement for l.i0 (load in PK order), l.x (create indexes) and the read-write tests (qr*, qp*).
for l.i1 and l.i2 (random write-only) throughput drops by 5% to 10% from 12.22 to 13.23 and has been stable since then (throughput in 18.2 is similar to 13.23. The CPU per operation overhead (cpupq here) increases after 12.22 for the l.i2 step but there wasn't an obvious increase for the l.i1 step - but the way I measure this is far from perfect. The results I share here are worse than what I measured in December 2025.

For latest point releases at 4 users

there might be a small (3%) regression for l.i0 (load in PK order) in 18.2 vs 12.22. Perhaps this is noise. From vmstat and iostat metrics there aren't obvious changes.
throughput in 18.2 is better than 12.22 for all other benchmark steps

For all 17.x releases at 1 user

throughput is stable from 17.0 to 17.9 for all benchmark steps except l.i1 and l.i2 (random writes) where there might be a 5% regression late in 17.x. This might be from new CPU overhead - see cpupq here.

For all 17.x releases at 4 users

throughput is stable with small improvements from 17.0 to 17.9

For full-page writes at 1 user

throughput improves by ~5% for l.i1 and l.i2 (random writes) when full-page writes are disabled and KB written to storage per commit drops by ~20% -- see wkbpi here.
enabling wal_compression=lz4 decreases write throughput for all write-heavy steps when full-page writes are enabled. The impact is smaller when full page writes are disabled.

For full-page writes at 4 users

throughput improves by <= 5% for all write-heavy steps when full-page writes are disabled
the impact from wal_compression=lz4 isn't obvious

Results: IO-bound

The performance summaries are here for:

1 user: latest point releases, all 17.x releases and full-page writes
4 users: latest point releases, all 17.x releases and full-page writes

For latest point releases at 1 user

there are small (<= 10%) improvements for l.i0 (load in PK order) and l.x (create index). I don't see anything obvious in vmstat and iostat metrics to explain this.
there are small (<= 10%) regressions for l.i1 and l.i2 (random writes) that might be from a sequence of small regressions from 13.x through 18.x. I don't see anything obvious in vmstat and iostat metrics to explain this.
throughput is unchanged for the range-query read+write tests (qr*)
throughput improves by ~1.4X for the point-query read+write tests (qp*). This improvement arrived in 13.x. This can be explained by large drops in CPU overhead (cpupq) and context switch rates (cspq) -- see here.
the results here are similar to what I measured in December 2025

For latest point releases at 4 users

there are small (~10%) regressions for l.i0 (load in PK order) that arrived in 17.x. The context switch rate (cspq) increases in 17.x.
there are small (<= 20%) improvements for l.x (create index) that arrived in 13.x
there are large regressions for l.i1 and l.i2 (random writes) that arrive in 15.x through 18.x. There are large increases in CPU overhead (cpupq) -- see here.
throughput is unchanged for the range-query read+write tests (qr*)
throughput improves for the point-query read+write tests (qp*) at higher write rates (qp500, qp1000).

For all 17.x releases at 1 user

throughput is stable with a few exceptions
for qp1000 (point-query, read+write) it improves by ~5% in 17.1 and is then stable to 17.9
in 17.9 there are large (~1.4x) improvements for all of the point-query, read+write tests
the changes in throughput for qp1000 might be explained by a small drop in CPU overhead per query (cpupq) that arrived in 17.1 and a large drop that arrived in 17.9 -- see here.

For all 17.x releases at 4 users

throughput for most steps (l.i0, l.x, qr*, qp100, qp500) is stable
throughput for l.i1 and l.i2 (random writes) has more variance
throughput for qp1000 drops by up to 10% from 17.3 through 17.8 and in those cases the CPU overhead increased -- see cpupq here.

For full-page writes at 1 user

throughput improves by 6% for l.i1 (random writes) when full-page writes are disabled
throughput improved for qp* tests when either full-page writes were disabled or lz4 was used for log_compression. That is harder to explain, perhaps it is noise.

For full-page writes at 4 users

throughput improves by 20% for l.i1 (random writes) when full-page writes are disabled
throughput improved for qp* tests when either full-page writes were disabled or lz4 was used for log_compression. That is harder to explain, perhaps it is noise.

n_dead_tup vs n_live_tup

The tables below show the ratio: n_dead_tup / (n_dead_tup + n_live_tup) for the CPU-bound and IO-bound workloads using 1 user (and one table). These were measured at the end of each benchmark step.

CPU-bound

12.22 18.3

l.i0 0.000 0.000

l.x 0.000 0.000

l.i1 0.065 0.035

l.i2 0.045 0.020

qr100 0.006 0.006

qp100 0.012 0.012

qr500 0.040 0.040

qp500 0.021 0.024

qr1000 0.031 0.036

qp1000 0.040 0.003

IO-bound

12.22 18.3

l.i0 0.000 0.000

l.x 0.000 0.000

l.i1 0.005 0.005

l.i2 0.006 0.006

qr100 0.000 0.000

qp100 0.000 0.000

qr500 0.002 0.002

qp500 0.003 0.003

qr1000 0.005 0.005

qp1000 0.007 0.007

Saturday, March 28, 2026

Selecting a character set for MySQL and MariaDB clients

MySQL and MariaDB have many character-set related options, perhaps too many:

character_set_client
character_set_connection
character_set_database
character_set_filesystem
character_set_results
character_set_server
character_set_system

This is a topic that I don't know much about and I am still far from an expert. My focus has been other DBMS topics. But I spent time recently on this topic while explaining what looked like a performance regression, but really was just a new release of MySQL using a charset that is less CPU-efficient than the previous charset that was used.

Debugging

The intial sequence to understand what was going on was:

mysql -e 'SHOW GLOBAL VARIABLES like "character_set_%"
mysql -e 'SHOW SESSION VARIABLES like "character_set_%"
run "SHOW SESSION VARIABLES" from my benchmark client

Note:

the output from steps 1 and 2 was different

with SHOW GLOBAL VARIABLES I got character_set_client =latin1 but with SHOW SESSION VARIABLES I got character_set_client =utf8mb3. This happens. One reason is that some MySQL client binaries autodetect the charset based on the value of LANG or LC_TYPE from your Linux env. Another reason is that if autodetection isn't done then the clients can use the default charset that was set at compile time. That charset is then passed to the server during connection handshake (see thd_init_client_charset). So it is likely that character_set_client as displayed by SHOW GLOBAL VARIABLES isn't what your client will use.

the output from steps 2 and 3 was different

autodetection is only done when mysql_options() is called with a certain flag (see below). And that is not done by the MySQL driver in sysbench, nor is it done by Python's MySQLdb. So my benchmark clients are likely selecting the default charset and don't do autodetection. And that default is determined by the version of the MySQL client library, meaning that default can change over the years. For the source that implements this, search for MYSQL_AUTODETECT_CHARSET_NAME and read sql-common/client.c.

The following enables autodetection and should be called before calling mysql_real_connect():

mysql_options(...,

MYSQL_SET_CHARSET_NAME,

MYSQL_AUTODETECT_CHARSET_NAME);

Note that adding the following into my.cnf isn't a workaround for clients that don't do autodetect.

[client]

default-character-set=...

Notes

These are from my usage of MySQL 5.7.44, 8.0.45 and 8.4.8 along with MariaDB 10.6.25, 10.11.16 and 11.4.10. All were compiled from source as was sysbench. I installed MySQLdb and the MySQL client library via apt for Ubuntu 24.04.

The values for character_set_client, character_set_results and character_set_connection were measured via the MySQL command-line client running SHOW GLOBAL VARIABLES and SHOW SESSION VARIABLES and then the benchmark clients running SHOW SESSION VARIABLES.

The reason for sharing this is to explain the many possible values your session might use for character_set_client, character_set_results and character_set_connection. And using the wrong value might waste CPU.

What per-session values are used for character_set_client|results|connection?

* my.cnf has character_set_server=latin1

* per SHOW GLOBAL VARIABLES each is set to =latin1

* values below measured via SHOW SESSION VARIABLES

Values for character_set_client|results|connection

... with "mysql" command line client

... this is easy to change with --default-character-set command line option or equivalent option in my.cnf

dbms

5.7.44 utf8

8.0.45 utf8mb4

8.4.8 utf8mb4

10.6.25 utf8mb3

10.11.16 utf8mb3

11.4.10 utf8mb3

Values for character_set_client|results|connection

... with sysbench

client library version

dbms 5.7 8.0 8.4 10.6 10.11 11.4

5.7.44 latin1 latin1 latin1 NA NA NA

8.0.45 latin1 utf8mb4 utf8mb4 NA NA NA

8.4.8 latin1 utf8mb4 utf8mb4 NA NA NA

10.6.25 latin1 latin1 latin1 utf8mb4 utf8mb4 utf8mb4

10.11.16 latin1 latin1 latin1 utf8mb4 utf8mb4 utf8mb4

11.4.10 latin1 utf8mb4 utf8mb4 utf8mb4 utf8mb4 utf8mb4

Values for character_set_client|results|connection

... with insert benchmark (Python MySQLdb and /lib/x86_64-linux-gnu/libmysqlclient.so.21

... I am not what version is libmysqlclient.so.21, this is on Ubuntu 24.04

dbms

5.7.44 latin1

8.0.45 utf8mb4

8.4.8 utf8mb4

10.6.25 latin1

10.11.16 latin1

11.4.10 utf8mb4