Sysbench for MySQL 5.6 through 9.5 on a 2-socket, 24-core server

This has results for the sysbench benchmark on a 2-socket, 24-core server. A post with results from 8-core and 32-core servers is here.

tl;dr

• old bad news - there were many large regressions from 5.6 to 5.7 to 8.0
• new bad news - there are some new regressions after MySQL 8.0

Normally I claim that there are few regressions after MySQL 8.0 but that isn't the case here. I also see regressions after MySQL 8.0 on the other larger servers that I use, but that topic will explained in another post.

Builds, configuration and hardware

I compiled MySQL from source for versions 5.6.51, 5.7.44, 8.0.43, 8.0.44, 8.4.6, 8.4.7, 9.4.0 and 9.5.0.

The server is a SuperMicro SuperWorkstation 7049A-T with 2 sockets, 12 cores/socket, 64G RAM, one m.2 SSD (2TB,  ext4 with discard enabled). The OS is Ubuntu 24.04. The CPUs are Intel Xeon Silver 4214R CPU @ 2.40GHz.

The config files are here for 5.6, 5.7, 8.0, 8.4 and 9.x.

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 InnoDB.

The read-heavy microbenchmarks are run for 600 seconds and the write-heavy for 900 seconds. The benchmark is run with 16 clients and 8 tables with 10M rows per table. 

The purpose is to search for regressions from new CPU overhead and mutex contention. The workload is cached -- there should be no read IO but will be some write IO.

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. 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 the 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 the base version.

I present two sets of charts. One set uses MySQL 5.6.51 as the base version which is my standard practice. The other uses MySQL 8.0.44 as the base version to show 

Values from iostat and vmstat divided by QPS are here. These 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 and charts are here and in some cases are easier to read than the charts below. Converting the Google Sheets charts to PNG files does the wrong thing for some of the test names listed at the bottom of the charts below.

Results: point queries

Summary

• from 5.6 to 5.7 there are big improvements for 5 tests, no changes for 2 tests and small  regressions for 2 tests
• from 5.7 to 8.0 there are big regressions for all tests
• from 8.0 to 9.5 performance is stable
• for 9.5 the common result is ~20% less throughput vs 5.6

Using vmstat from the hot-points test to understand the performance changes (see here)

• context switch rate (cs/o) is stable, mutex contention hasn't changed
• CPU per query (cpu/o) drops by 35% from 5.6 to 5.7
• CPU per query (cpu/o) grows by 23% from 5.7 to 8.0
• CPU per query (cpu/o) is stable from 8.0 through 9.5

Results: range queries without aggregation

Summary

• from 5.6 to 5.7 throughput drops by 10% to 15%
• from 5.7 to 8.0 throughput drops by about 15%
• from 8.0 to 9.5 throughput is stable
• for 9.5 the common result is ~30% less throughput vs 5.6

Using vmstat from the scan test to understand the performance changes (see here)

• context switch rates are low and can be ignored
• CPU per query (cpu/o) grows by 11% from 5.6 to 5.7
• CPU per query (cpu/o) grows by 15% from 5.7 to 8.0
• CPU per query (cpu/o) is stable from 8.0 through 9.5

Results: range queries with aggregation

Summary

• from 5.6 to 5.7 there are big improvements for 2 tests, no changes for 1 tests and regressions for 5 tests
• from 5.7 to 8.0 there are regressions for all tests
• from 8.0 through 9.5 performance is stable
• for 9.5 the common result is ~25% less throughput vs 5.6

Using vmstat from the read-only-count test to understand the performance changes (see here)

• context switch rates are similar
• CPU per query (cpu/o) grows by 16% from 5.6 to 5.7
• CPU per query (cpu/o) grows by 15% from 5.7 to 8.0
• CPU per query (cpu/o) is stable from 8.0 through 9.5

Results: writes

Summary

• from 5.6 to 5.7 there are big improvements for 9 tests and no changes for 1 test
• from 5.7 to 8.0 there are regressions for all tests
• from 8.4 to 9.x there are regressions for 8 tests and no change for 2 tests
• for 9.5 vs 5.6: 5 are slower in 9.5, 3 are similar and 2 are faster in 9.5

Using vmstat from the insert test to understand the performance changes (see here)

• in 5.7, CPU per insert drops by 30% while context switch rates are stable vs 5.6
• in 8.0, CPU per insert grows by 36% while context switch rates are stable vs 5.7
• in 9.5, CPU per insert grows by 3% while context switch rates grow by 23% vs 8.4

The first chart doesn't truncate the y-axis to show the big improvement for update-index but that makes it hard to see the smaller changes on the other tests.

This chart truncates the y-axis to make it easier to see changes on tests other than update-index.

The insert benchmark on a small server : MySQL 5.6 through 9.5

This has results for MySQL versions 5.6 through 9.5 with the Insert Benchmark on a small server. Results for Postgres on the same hardware are here.

tl;dr

• good news - there are no large regressions after MySQL 8.0
• bad news - there are many large regressions from 5.6 to 5.7 to 8.0

Builds, configuration and hardware

I compiled MySQL from source for versions 5.6.51, 5.7.44, 8.0.43, 8.0.44, 8.4.6, 8.4.7, 9.4.0 and 9.5.0.

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.

The config files are here: 5.6.51, 5.7.44, 8.0.4x, 8.4.x, 9.x.0.

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
• Latest point release from each version
• IO-bound
• All point releases
• 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 MySQL 5.6.51.

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

Below I use colors to highlight the relative QPS values with yellow for regressions and blue for improvements.

Results: cached

Performance summaries are here for all versions and latest versions. I focus on the latest versions.

Below I use colors to highlight the relative QPS values with yellow for regressions and blue for improvements. There are large regressions from new CPU overheads.

• the load step (l.i0) is almost 2X faster for 5.6.51 vs 8.4.7 (relative QPS is 0.59)
• the create index step (l.x) is more than 2X faster for 8.4.7 vs 5.6.51
• the first write-only steps (l.i1) has similar throughput for 5.6.51 and 8.4.7
• the second write-only step (l.i2) is 14% slower in 8.4.7 vs 8.4.7
• the range-query steps (qr*) are ~30% slower in 8.4.7 vs 5.6.51
• the point-query steps (qp*) are 38% slower in 8.4.7 vs 5.6.51

dbms	l.i0	l.x	l.i1	l.i2	qr100	qp100	qr500	qp500	qr1000	qp1000
5.6.51	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
5.7.44	0.91	1.53	1.16	1.09	0.83	0.83	0.83	0.84	0.83	0.83
8.0.44	0.60	2.42	1.05	0.87	0.69	0.62	0.70	0.62	0.70	0.62
8.4.7	0.59	2.54	1.04	0.86	0.68	0.61	0.68	0.61	0.67	0.60
9.4.0	0.59	2.57	1.03	0.86	0.69	0.62	0.69	0.62	0.70	0.61
9.5.0	0.59	2.61	1.05	0.85	0.69	0.62	0.69	0.62	0.69	0.62

Results: IO-bound

Performance summaries are here for all versions and latest versions. I focus on the latest versions.

Below I use colors to highlight the relative QPS values with yellow for regressions and blue for improvements. There are large regressions from new CPU overheads.

• the load step (l.i0) is almost 2X faster for 5.6.51 vs 8.4.7 (relative QPS is 0.60)
• the create index step (l.x) is more than 2X faster for 8.4.7 vs 5.6.51
• the first write-only steps (l.i1) is 1.54X faster for 8.4.7 vs 5.6.51
• the second write-only step (l.i2) is  1.82X faster for 8.4.7 vs 5.6.51
• the range-query steps (qr*) are ~20% slower in 8.4.7 vs 5.6.51
• the point-query steps (qp*) are 13% slower, 3% slower and 17% faster in 8.4.7 vs 5.6.51

dbms	l.i0	l.x	l.i1	l.i2	qr100	qp100	qr500	qp500	qr1000	qp1000
5.6.51	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
5.7.44	0.91	1.42	1.52	1.78	0.84	0.92	0.87	0.97	0.93	1.17
8.0.44	0.62	2.58	1.56	1.81	0.76	0.88	0.79	0.99	0.85	1.18
8.4.7	0.60	2.65	1.54	1.82	0.74	0.87	0.77	0.98	0.82	1.17
9.4.0	0.61	2.68	1.52	1.76	0.75	0.86	0.80	0.97	0.85	1.16
9.5.0	0.60	2.75	1.53	1.73	0.75	0.87	0.79	0.97	0.84	1.17

Challenges compiling old C++ code on modern Linux

I often compile old versions of MySQL, MariaDB, Postgres and RocksDB in my search for performance regressions. Compiling is easy with Postgres as they do a great job at avoiding compilation warnings and I never encounter broken builds. Certainly the community gets the credit for this, but I suspect their task is easier because they use C.  This started as a LinkedIn post.

I expect people to disagree, and I am far from a C++ expert, but here goes ...

tl;dr - if you maintain widely used header files (widely used by C++ projects) consider not removing that include that you don't really need (like <cstdint>) because such removal is likely to break builds for older releases of projects that use your include.

I have more trouble compiling older releases of C++ projects. For MySQL I have a directory in github that includes patches that must be applied. And for MySQL I have to patch all 5.6 versions, 5.7 versions up to 5.7.33 and 8.0 versions up to 8.0.23. The most common reason for the patch is missing C++ includes (like <cstdint>).

For RocksDB with gcc I don't have to patch files but I need to use gcc-11 for RocksDB 6.x and gcc-12 for RocksDB 7.x.

For RocksDB with clang I don't have to patch files for RocksDB 8.x, 9.x and 10.x while I do have to patch 6.x and 7.x. For RocksDB 7.10 I need to edit two files to add <cstdint>. The files are:

• table/block_based/data_block_hash_index.h
• util/string_util.h

All of this is true for Ubuntu 24.04 with clang 18.1.3 and gcc 13.3.0.

One more detail, for my future self, the command line I use to compile RocksDB with clang is one of the following:

• Rather than remember which of V= and VERBOSE= that I need, I just use both
• I get errors if I don't define AR and RANLIB when using clang
• While clang-18 installs clang and clang++ binaries, to get the llvm variants of ar and ranlib I need to use llvm-ar-18 and llvm-ranlib-18 rather than llvm-ar and llvm-ranlib

# without link-time optimization
AR=llvm-ar-18 RANLIB=llvm-ranlib-18 \
CC=clang CXX=clang++ \
make \
DISABLE_WARNING_AS_ERROR=1 DEBUG_LEVEL=0 V=1 VERBOSE=1 -j... \

static_lib db_bench

# with link-time optimization

AR=llvm-ar-18 RANLIB=llvm-ranlib-18 \
CC=clang CXX=clang++ \
make USE_LTO=1 \
DISABLE_WARNING_AS_ERROR=1 DEBUG_LEVEL=0 V=1 VERBOSE=1 -j... \
static_lib db_bench

MySQL 5.6 thru 9.4: small server, Insert Benchmark

This has results for the Insert Benchmark on a small server with InnoDB from MySQL 5.6 through 9.4. The workload here uses low concurrency (1 client), a small server and a cached database. I run it this way to look for CPU regressions before moving on to IO-bound workloads with high concurrency.

tl;dr

• good news - there are no large regressions after MySQL 8.0
• bad news - there are large regressions from MySQL 5.6 to 5.7 to 8.0
• load in 8.0, 8.4 and 9.4 gets about 60% of the throughput vs 5.6
• queries in 8.0, 8.4 and 9.4 get between 60% and 70% of the throughput vs 5.6

Builds, configuration and hardware

I compiled MySQL 5.6.51, 5.7.44, 8.0.43, 8.4.6 and 9.4.0 from source.

The server is an ASUS PN53 with 8 cores, AMD SMT disabled and 32G of RAM. The OS is Ubuntu 24.04. Storage is 1 NVMe device with ext4. More details on it are here.

I used the cz12a_c8r32 config file (my.cnf) which is here for 5.6.51, 5.7.44, 8.0.43, 8.4.6 and 9.4.0.

The Benchmark

The benchmark is explained here. I recently updated the benchmark client to connect via socket rather than TCP so that I can get non-SSL connections for all versions tested. AFAIK, with TCP I can only get SSL connections for MySQL 8.4 and 9.4.

The workload uses 1 client, 1 table with 30M rows and a cached database.

The benchmark steps are:

• l.i0
• insert 30 million 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 40 million 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 10 million rows are inserted and deleted per table.
• Wait for N seconds after the step finishes to reduce variance during the read-write benchmark steps that follow. The value of N 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. This step is run for 1800 seconds. 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.
• 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 report is here.

The summary section has 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. The summary section is here.

Below I use relative QPS (rQPS) 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 MySQL 5.6.51.

When rQPS is > 1.0 then performance improved over time. When it is < 1.0 then there are regressions. When it is 0.90 then I claim there is a 10% regression. 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

Below I use colors to highlight the relative QPS values with yellow for regressions and blue for improvements.

Results: details

This table is a copy of the second table in the summary section. It lists the relative QPS (rQPS) for each benchmark step where rQPS is explained above.

The benchmark steps are explained above, they are:

• l.i0 - initial load in PK order
• l.x - create 3 secondary indexes per table
• l.i1, l.i2 - random inserts and random deletes
• qr100, qr500, qr1000 - short range queries with background writes
• qp100, qp500, qp1000 - point queries with background writes

dbms	l.i0	l.x	l.i1	l.i2	qr100	qp100	qr500	qp500	qr1000	qp1000
5.6.51	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
5.7.44	0.89	1.52	1.14	1.08	0.83	0.84	0.83	0.84	0.84	0.84
8.0.43	0.60	2.50	1.04	0.86	0.69	0.62	0.69	0.63	0.70	0.62
8.4.6	0.60	2.53	1.03	0.86	0.68	0.61	0.67	0.61	0.68	0.61
9.4.0	0.60	2.53	1.03	0.87	0.70	0.63	0.70	0.63	0.70	0.62

The summary is:

• l.i0
• there are large regressions starting in 8.0 and modern MySQL only gets ~60% of the throughput relative to 5.6 because modern MySQL has more CPU overhead
• l.x
• I ignore this but there have been improvements
• l.i1, l.i2
• there was a large improvement in 5.7 but new CPU overhead since 8.0 reduces that
• qr100, qr500, qr1000
• there are large regressions from 5.6 to 5.7 and then again from 5.7 to 8.0
• throughput in modern MySQL is ~60% to 70% of what it was in 5.6

Sysbench for MySQL 5.6 thru 9.4 on a small server

This has performance results for InnoDB from MySQL 5.6.51, 5.7.44, 8.0.43, 8.4.6 and 9.4.0 on a small server with sysbench microbenchmarks. The workload here is cached by InnoDB and my focus is on regressions from new CPU overheads. This work was done by Small Datum LLC and not sponsored. 

tl;dr

• Low concurrency (1 client) is the worst case for regressions in modern MySQL
• MySQL 8.0, 8.4 and 9.4 are much slower than 5.6.51 in all but 2 of the 32 microbenchmarks
• The bad news - performance regressions aren't getting fixed
• The good news - regressions after MySQL 8.0 are small

Builds, configuration and hardware

I compiled MySQL from source for versions 5.6.51, 5.7.44, 8.0.43, 8.4.6 and 9.4.0.

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 are 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 900 seconds.

Results

All files I saved from the benchmark are here and the spreadsheet is here.

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. The relative QPS is the following:

(QPS for some version) / (QPS for MySQL 5.6.51)

When the relative QPS is > 1 then some version is faster than 5.6.51.  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.

Results: point queries

Based on results from vmstat the regressions are from new CPU overheads.

Results: range queries without aggregation

Based on results from vmstat the regressions are from new CPU overheads.

Results; range queries with aggregation

Based on results from vmstat the regressions are from new CPU overheads.

Results: writes

Based on results from vmstat the regressions are from new CPU overheads.

The impact of innodb_doublewrite_pages in MySQL 8.0.41

After reading a blog post from JFG on changes to innodb_doublewrite_pages and bug 111353, I wanted to understand the impact from that on the Insert Benchmark using a large server.

I test the impact from:

• using a larger (non-default) value for innodb_doublewrite_pages
• disabling the doublewrite buffer

tl;dr

• Using a larger value for innodb_doublewrite_pages improves QPS by up to 10%
• Disabling the InnoDB doublewrite buffer is great for performance, but bad for durability. I don't suggest you do this in production.

Builds, configuration and hardware

I compiled upstream MySQL 8.0.41 from source.

The server is an ax162-s from Hetzner with 48 cores, AMD 128G RAM and AMD SMT disabled. It uses Ubuntu 22.04 and storage is ext4 using SW RAID 1 over 2 locally attached NVMe devices. More details on it are here. At list prices a similar server from Google Cloud costs 10X more than from Hetzner.

The MySQL configuration files are:

• cz11a_c32r128 - the base configuration file that does not set innodb_doublewrite_pages and gets innodb_doublewrite_pages=8
• cz11e_c32r128 - adds innodb_doublewrite_pages=128 to the base config
• cz11f_c32r128 - adds innodb_doublewrite=0 to the base config (disables doublewrite)

The Benchmark

The benchmark is explained here and is run with 20 clients and a table per client with an IO-bound workload. The database is larger than memory with 200M rows per table and 20 tables.

The benchmark steps are:

• l.i0
• insert 200 million 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 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 X seconds after the step finishes to reduce variance during the read-write benchmark steps that follow. The value of X 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. This step is run for 1800 seconds. 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.
• 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 report is here.

The summary section in the performance report has 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 and all systems sustained the target rates. 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 with the cz11e_c32r128 or cz11f_c32r128 configs and $base is the result from the cz11a_c32r128 config. The configs are explained above, cz11e_c32r128 increases innodb_doublewrite_pages and cz11f_c32r128 disabled the doublewrite buffer.

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

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: more IO-bound

The performance summary is here.

From the cz11e_c32r128 config that increases innodb_doublewrite_pages to 128:

• the impact on write-heavy steps is mixed: create index was ~7% slower and l.i2 was ~10% faster
• the impact on range query + write steps is positive but small. The improvements were 0%, 0% and 4%. Note that these steps are not as IO-bound as point query + write steps and the range queries do ~0.3 reads per query (see here).
• the impact on point query + write steps is positive and larger. The improvements were 3%, 8% and 9%. These benchmark steps are much more IO-bound than the steps that do range queries.

From the cz11f_c32r128 config that disables the InnoDB doublewrite buffer:

• the impact on write-heavy steps is large -- from 1% to 36% faster.
• the impact on range query + write steps is positive but small. The improvements were 0%, 2% and 15%. Note that these steps are not as IO-bound as point query + write steps and the range queries do ~0.3 reads per query (see here).
• the impact on point query + write steps is positive and larger. The improvements were 14%, 41% and 42%.

At what level of concurrency do MySQL 5.7 and 8.0 become faster than 5.6?

Are MySQL 5.7 and 8.0 faster than 5.6? That depends a lot on the workload -- both types of SQL and amount of concurrency. Here I summarize results from sysbench on a larger server (48 cores) using 1, 4, 6, 8, 10, 20 and 40 clients to show how things change.

tl;dr

• the workload here is microbenchmarks with a database cached by InnoDB
• 5.7.44 is faster than 8.0.x at all concurrency levels on most microbenchmarks
• for 5.6.51 vs 8.0.x
• for point queries, 5.6.51 is faster at <= 8 clients
• for range queries without aggregation 5.6.51 is always faster
• for range queries with aggregation 5.6.51 is faster except at 40 clients
• for writes, 5.6.51 is almost always faster at 10 or fewer clients (excluding update-index)

Performance summaries

For point queries:

• 5.7.44 is always faster than 8.0
• 8.0.28 suffers from bug 102037 - found by me with sysbench, fixed in 8.0.31
• at what level of concurrency do most things get faster in 5.7 & 8.0 vs 5.6?
• 5.7.44 becomes faster than 5.6.51 at 6+ clients
• 8.0.x becomes faster than 5.6.51 at between 10 and 20 clients
• Two of the microbenchmarks are always faster in 5.6.51 - both do non-covering queries on a secondary index

For range queries without aggregation

• 5.7.44 is always faster than 8.0x
• 5.6.51 is always faster than 5.7.44 and 8.0.x

For range queries with aggregation

• 5.7.44 is almost always faster than 8.0.x
• 5.7.44 becomes faster than 5.6.51 at 6+ clients
• 8.0.x becomes faster than 5.6.51 at 40 clients

For writes

• For update-index
• 5.7.44 and 8.0.x are always faster than 5.6.51 at 4+ clients
• There is an odd drop from ~6X to ~3X for 8.0.32 and 8.0.39 at 20 clients
• 5.7.44 is mostly faster than 8.0.x for 1 to 20 clients and they have similar results at 40 clients
• 5.7.44 & 8.0.x are always faster than 5.6.51 at 20+ clients

Builds, configuration and hardware

I compiled MySQL from source for versions 5.6.51, 5.7.44, 8.0.28, 8.0.32, 8.0.39 and 8.0.41.

The server is an ax162-s from Hetzner with 48 cores (AMD EPYC 9454P), 128G RAM and AMD SMT disabled. It uses Ubuntu 22.04 and storage is ext4 with SW RAID 1 over 2 locally attached NVMe devices. More details on it are here. At list prices a similar server from Google Cloud costs 10X more than from Hetzner.

The configuration files are named my.cnf.cz11a_c32r128 and here for 5.6.51, 5.7.44, 8.0.28, 8.0.32, 8.0.39 and 8.0.41.

Benchmark

I used sysbench and my usage is explained here. To save time I only run 27 of the 42 microbenchmarks and most test only 1 type of SQL statement. Benchmarks are run with the database cached by InnoDB.

The tests run with 8 tables and 10M rows/table. There are 40 client threads, read-heavy microbenchmarks run for 180 seconds and write-heavy run for 300 seconds.

The command lines to run all tests are:

bash r.sh 8 10000000 180 300 md2 1 1 1

bash r.sh 8 10000000 180 300 md2 1 1 4

bash r.sh 8 10000000 180 300 md2 1 1 6

bash r.sh 8 10000000 180 300 md2 1 1 8

bash r.sh 8 10000000 180 300 md2 1 1 10

bash r.sh 8 10000000 180 300 md2 1 1 20

bash r.sh 8 10000000 180 300 md2 1 1 40

Results

For the results below I split the microbenchmarks into 4 groups: point queries, range queries without aggregation, range queries with queries, writes. The spreadsheet with all data is here. Files with performance summaries for relative and absolute QPS are here. Values from iostat and vmstat per microbenchmark are here for 1 client, 4 clients, 6 clients, 8 clients, 10 clients, 20 clients and 40 clients. These help to explain why something is faster or slower because it shows how much HW is used per query.

The relative QPS is the following where $version is >= 5.7.44.

(QPS for $version) / (QPS for MySQL 5.6.51)

The numbers in the spreadsheets are the relative QPS. When the relative QPS is > 1 then $version is faster than MySQL 5.6.51. When it is 3.0 then $version is 3X faster than the base case.

Results: charts 

Notes on the charts

• the y-axis shows the relative QPS
• the y-axis starts at 0.80 to make it easier to see differences
• in some cases the y-axis truncates the good outliers, cases where the relative QPS is greater than 1.5. I do this to improve readability for values near 1.0. Regardless, the improvements are nice.

Results: point queries

Summary

• 5.7.44 is always faster than 8.0
• 8.0.28 suffers from bug 102037 - found by me with sysbench, fixed in 8.0.31
• at what level of concurrency do most things get faster in 5.7 & 8.0 vs 5.6?
• 5.7.44 becomes faster than 5.6.51 at 6+ clients
• 8.0.x becomes faster than 5.6.51 at between 10 and 20 clients
• Two of the microbenchmarks are always faster in 5.6.51 - both do non-covering queries on a secondary index

Results: range queries without aggregation

Summary

• 5.7.44 is always faster than 8.0x
• 5.6.51 is always faster than 5.7.44 and 8.0.x

Results: range queries with aggregation

Summary

• 5.7.44 is almost always faster than 8.0.x
• 5.7.44 becomes faster than 5.6.51 at 6+ clients
• 8.0.x becomes faster than 5.6.51 at 40 clients

Results: writes

The relative speedup for the update-index microbenchmark is frequently so large that it obscures the smaller changes on other microbenchmarks. So here I truncate the y-axis for some of the charts (for 6+ clients) and the section that follows has the charts without truncation.

Summary

• For update-index
• 5.7.44 and 8.0.x are always faster than 5.6.51 at 4+ clients
• There is an odd drop from ~6X to ~3X for 8.0.32 and 8.0.39 at 20 clients but you can't see that on the charts in this section because of the truncation. It is visible in the next section. From vmstat I see an increase in CPU/operation (cpu/o) and context switches /operation (cs/o) at 20 clients but not at 40 clients.
• 5.7.44 is mostly faster than 8.0.x for 1 to 20 clients and they have similar results at 40 clients
• 5.7.44 & 8.0.x are always faster than 5.6.51 at 20+ clients

Results: charts for writes without truncation

The y-axis is truncated the the charts for writes in the previous section for 6+ clients. This section has those charts without truncation.