Postgres 18.0 vs sysbench on a small server

This has benchmark results for Postgres 18.0 using sysbench on a small server. Previous results for 18 rc1 are here.

tl;dr

• From 12.22 to 18.0
• there are no regressions larger than 2% but many improvements larger than 5%. Postgres continues to do a great job at avoiding regressions over time.
• From 17.6 to 18.0
• I continue to see small CPU regressions (1% or 2%) in Postgres 18 for short range queries on low-concurrency workloads. I see it for shorter but not for longer range queries so my guess is that this is new overhead in query execution setup or optimization. I hope to explain this.

Builds, configuration and hardware

I compiled Postgres from source for versions 12.22, 13.22, 14.19, 15.14, 16.10, 17.6, and 18.0.

The HW is an ASUS ExpertCenter PN53 with AMD Ryzen 7735HS CPU, 32G of RAM, 8 cores with AMD SMT disabled, Ubuntu 24.04 and an NVMe device with ext4 and discard enabled.

Prior to 18.0, the configuration file was named conf.diff.cx10a_c8r32 and is here for 12.22, 13.22, 14.19, 15.14, 16.10 and 17.6.

For 18.0 I tried 3 configuration files:

• conf.diff.cx10b_c8r32 (x10b) - uses io_method=sync
• conf.diff.cx10c_c8r32 (x10c) - uses io_method=worker
• conf.diff.cx10d_c8r32 (x10d) - uses io_method=io_uring

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

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 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.

I present results for:

• versions 12 through 18 using 12.22 as the base version
• versions 17.6 and 18.0 using 17.6 as the base version

Results: Postgres 17.6 and 18.0

For the read-only_range=X benchmarks there might be small regressions (1% or 2%) when X is 10 or 100 but not 10000. The value of X is the length of the range scan. I have seen similar regressions in the beta and RC releases. Given that this occurs when the range scan is shorter, the problem might be new overhead in query execution setup or optimization. But I have yet to explain this.

Relative to: 17.6 with x10a

col-1 : 18.0 with x10b and io_method=sync

col-2 : 18.0 with x10c and io_method=worker

col-3 : 18.0 with x10d and io_method=io_uring

col-1   col-2   col-3  point queries

1.01    1.01    0.97    hot-points_range=100

1.01    1.00    0.99    point-query_range=100

1.01    1.01    1.00    points-covered-pk_range=100

1.01    1.02    1.01    points-covered-si_range=100

1.01    1.01    1.00    points-notcovered-pk_range=100

1.01    0.99    1.00    points-notcovered-si_range=100

1.02    1.02    1.03    random-points_range=1000

1.01    1.00    0.99    random-points_range=100

1.00    1.00    0.99    random-points_range=10

col-1   col-2   col-3  range queries without aggregation

0.99    0.99    0.98    range-covered-pk_range=100

1.00    0.99    1.00    range-covered-si_range=100

1.00    0.99    0.98    range-notcovered-pk_range=100

0.99    0.99    0.99    range-notcovered-si_range=100

1.04    1.04    1.04    scan_range=100

col-1   col-2   col-3  range queries with aggregation

1.01    1.00    1.01    read-only-count_range=1000

1.01    1.00    1.00    read-only-distinct_range=1000

0.99    1.00    0.98    read-only-order_range=1000

1.01    1.00    1.00    read-only_range=10000

0.99    0.99    0.98    read-only_range=100

0.98    0.99    0.98    read-only_range=10

1.01    1.00    0.99    read-only-simple_range=1000

1.00    1.00    0.99    read-only-sum_range=1000

col-1   col-2   col-3  writes

1.00    1.00    0.99    delete_range=100

0.99    0.99    0.98    insert_range=100

0.99    0.99    0.98    read-write_range=100

0.98    0.99    0.98    read-write_range=10

0.99    1.00    0.99    update-index_range=100

0.99    1.00    1.00    update-inlist_range=100

0.99    1.00    0.98    update-nonindex_range=100

0.99    0.99    0.98    update-one_range=100

0.99    1.00    0.99    update-zipf_range=100

1.00    1.00    0.99    write-only_range=10000

Results: Postgres 12 to 18

From 12.22 to 18.0 there are no regressions larger than 2% but many improvements larger than 5% (highlighted in greeen). Postgres continues to do a great job at avoiding regressions over time.

Relative to: 12.22

col-1 : 13.22

col-2 : 14.19

col-3 : 15.14

col-4 : 16.10

col-5 : 17.6

col-6 : 18.0 with the x10b config

col-1   col-2   col-3   col-4   col-5   col-6   point queries

1.06    1.05    1.05    1.09    2.04    2.05    hot-points_range=100

1.01    1.03    1.03    1.02    1.04    1.04    point-query_range=100

1.00    0.99    0.99    1.03    0.99    1.01    points-covered-pk_range=100

1.04    1.03    1.02    1.05    1.01    1.03    points-covered-si_range=100

1.01    1.00    1.01    1.04    1.01    1.02    points-notcovered-pk_range=100

1.01    1.02    1.03    1.05    1.02    1.04    points-notcovered-si_range=100

1.02    1.00    1.02    1.05    1.00    1.02    random-points_range=1000

1.01    1.01    1.01    1.03    1.01    1.02    random-points_range=100

1.01    1.01    1.01    1.02    1.01    1.01    random-points_range=10

col-1   col-2   col-3   col-4   col-5   col-6   range queries with aggregation

0.99    1.00    1.00    1.00    0.99    0.98    range-covered-pk_range=100

1.01    1.01    1.00    1.00    0.99    0.99    range-covered-si_range=100

1.00    1.00    1.01    1.01    1.00    1.00    range-notcovered-pk_range=100

1.00    1.00    1.00    1.01    1.02    1.01    range-notcovered-si_range=100

1.00    1.30    1.19    1.18    1.16    1.20    scan_range=100

col-1   col-2   col-3   col-4   col-5   col-6   range queries without aggregation

1.04    1.02    1.00    1.05    1.02    1.03    read-only-count_range=1000

1.00    1.00    1.03    1.04    1.03    1.04    read-only-distinct_range=1000

1.00    1.00    1.04    1.04    1.06    1.06    read-only-order_range=1000

1.01    1.01    1.04    1.07    1.06    1.07    read-only_range=10000

1.00    1.00    1.01    1.01    1.02    1.01    read-only_range=100

1.00    1.00    1.00    0.99    1.01    0.99    read-only_range=10

1.01    1.01    1.02    1.02    1.03    1.03    read-only-simple_range=1000

1.01    1.00    1.00    1.03    1.02    1.02    read-only-sum_range=1000

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

1.01    1.02    1.01    1.03    1.13    1.12    delete_range=100

0.99    0.98    0.97    0.98    1.06    1.05    insert_range=100

0.99    1.00    1.00    1.01    1.02    1.02    read-write_range=100

0.99    1.01    1.01    1.01    1.03    1.01    read-write_range=10

1.00    1.00    1.01    1.00    1.09    1.08    update-index_range=100

1.00    1.10    1.09    1.09    1.10    1.09    update-inlist_range=100

1.03    1.05    1.06    1.05    1.15    1.14    update-nonindex_range=100

0.99    0.98    0.99    0.98    1.07    1.06    update-one_range=100

1.01    1.04    1.06    1.05    1.18    1.17    update-zipf_range=100

0.98    1.01    1.01    0.99    1.07    1.07    write-only_range=10000

Postgres 18rc1 vs sysbench

This post has results for Postgres 18rc1 vs sysbench on small and large servers. Results for Postgres 18beta3 are here for a small and large server.

tl;dr

• Postgres 18 looks great
• I continue to see small CPU regressions in Postgres 18 for range queries that don't do aggregation on low-concurrency workloads. I have yet to explain that. 
• The throughput for the scan microbenchmark has more variance with Postgres 18. I assume this is related to more or less work getting done by vacuum but I have yet to debug the root cause.

Builds, configuration and hardware

I compiled Postgres from source for versions 17.6, 18 beta3 and 18 rc1.

The servers are:

• small
• an ASUS ExpertCenter PN53 with AMD Ryzen 7735HS CPU, 32G of RAM, 8 cores with AMD SMT disabled, Ubuntu 24.04 and an NVMe device with ext4 and discard enabled.
• large32
• Dell Precision 7865 Tower Workstation with 1 socket, 128G RAM, AMD Ryzen Threadripper PRO 5975WX with 32 Cores and AMD SMT disabled, Ubuntu 24.04 and and NVMe device with ext4 and discard.
• large48
• an ax162s from Hetzner 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)

All configurations use synchronous IO which is the the only option prior to Postgres 18 and for Postgres 18 the config file sets io_method=sync.

Configuration files:

• small server
• for versions 17.6 (x10a_c8r32) and 18 (x10b_c8r32)
• large servers
• for versions 17.6 (x10a_c32r128) and 18 (x10b_c32r128)

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.

For all servers the read-heavy microbenchmarks run for 600 seconds and the write-heavy for 900 seconds.

The number of tables and rows per table was:

• small server - 1 table, 50M rows
• large servers - 8 tables, 10M rows per table

The number of clients (amount of concurrency) was:

• small server - 1
• large32 server - 24
• large48 servcer- 40

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 Postgres 17.6)

When the relative QPS is > 1 then some version is faster than PG 17.6.  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.

The numbers highlighted in yellow below might be from a small regression for range queries that don't do aggregation. But note that this does reproduce for the full table scan microbenchmark (scan). I am not certain it is a regression as this might be from non-deterministic CPU overheads for read-heavy workloads that are run after vacuum. I hope to look at CPU flamegraphs soon.

Results: small server

I continue to see small (~3%) regressions in throughput for range queries without aggregation across Postgres 18 beta1, beta2, beta3 and rc1. But I have yet to debug this and am not certain it is a regression. I am also skeptical about the great results for scan. I suspect that I have more work to do to make the benchmark less subject to variance from MVCC GC (vacuum here). I also struggle with that on RocksDB (compaction), but not on InnoDB (purge).

Relative to: Postgres 17.6

col-1 : 18beta3

col-2 : 18rc1

col-1   col-2   point queries

1.01    0.98    hot-points_range=100

1.01    1.00    point-query_range=100

1.02    1.02    points-covered-pk_range=100

0.99    1.01    points-covered-si_range=100

1.00    0.99    points-notcovered-pk_range=100

1.00    0.99    points-notcovered-si_range=100

1.01    1.00    random-points_range=1000

1.01    0.99    random-points_range=100

1.01    1.00    random-points_range=10

col-1   col-2   range queries without aggregation

0.97    0.96    range-covered-pk_range=100

0.97    0.97    range-covered-si_range=100

0.99    0.99    range-notcovered-pk_range=100

0.99    0.99    range-notcovered-si_range=100

1.35    1.36    scan_range=100

col-1   col-2   range queries with aggregation

1.02    1.03    read-only-count_range=1000

1.00    1.00    read-only-distinct_range=1000

0.99    0.99    read-only-order_range=1000

1.00    1.00    read-only_range=10000

1.00    0.99    read-only_range=100

0.99    0.98    read-only_range=10

1.01    1.01    read-only-simple_range=1000

1.02    1.00    read-only-sum_range=1000

col-1   col-2   writes

0.99    0.99    delete_range=100

0.99    1.01    insert_range=100

0.99    0.99    read-write_range=100

0.99    0.99    read-write_range=10

0.98    0.98    update-index_range=100

1.00    0.99    update-inlist_range=100

0.98    0.98    update-nonindex_range=100

0.98    0.97    update-one_range=100

0.98    0.97    update-zipf_range=100

0.99    0.98    write-only_range=10000

Results: large32 server

I don't see small regressions in throughput for range queries without aggregation across Postgres 18 beta1, beta2, beta3 and rc1. I have only seen that on the low concurrency (small server) results.

The improvements on the scan microbenchmark come from using less CPU. But I am skeptical about the improvements. I might have more work to do to make the benchmark less subject to variance from MVCC GC (vacuum here). I also struggle with that on RocksDB (compaction), but not on InnoDB (purge).

Relative to: Postgres 17.6

col-1 : Postgres 18rc1

col-1   point queries

1.01    hot-points_range=100

1.01    point-query_range=100

1.01    points-covered-pk_range=100

1.01    points-covered-si_range=100

1.00    points-notcovered-pk_range=100

1.00    points-notcovered-si_range=100

1.01    random-points_range=1000

1.00    random-points_range=100

1.01    random-points_range=10

col-1   range queries without aggregation

0.99    range-covered-pk_range=100

0.99    range-covered-si_range=100

0.99    range-notcovered-pk_range=100

0.99    range-notcovered-si_range=100

1.12    scan_range=100

col-1   range queries with aggregation

1.00    read-only-count_range=1000

1.02    read-only-distinct_range=1000

1.01    read-only-order_range=1000

1.03    read-only_range=10000

1.00    read-only_range=100

1.00    read-only_range=10

1.00    read-only-simple_range=1000

1.00    read-only-sum_range=1000

col-1   writes

1.01    delete_range=100

1.00    insert_range=100

1.00    read-write_range=100

1.00    read-write_range=10

1.00    update-index_range=100

1.00    update-inlist_range=100

1.00    update-nonindex_range=100

0.99    update-one_range=100

1.00    update-zipf_range=100

1.00    write-only_range=10000

Results: large48 server

I don't see small regressions in throughput for range queries without aggregation across Postgres 18 beta1, beta2, beta3 and rc1. I have only seen that on the low concurrency (small server) results.

The improvements on the scan microbenchmark come from using less CPU. But I am skeptical about the improvements. I might have more work to do to make the benchmark less subject to variance from MVCC GC (vacuum here). I also struggle with that on RocksDB (compaction), but not on InnoDB (purge).

I am skeptical about the regression I see here for scan. That comes from using ~10% more CPU per query. I might have more work to do to make the benchmark less subject to variance from MVCC GC (vacuum here). I also struggle with that on RocksDB (compaction), but not on InnoDB (purge).

I have not see the large improvements for the insert and delete microbenchmarks on previous tests on that large server. I assume this is another case where I need to figure out how to reduce variance when I run the benchmark.

Relative to: Postgres 17.6

col-1 : Postgres 18beta3

col-2 : Postgres 18rc1

col-1   col-2   point queries

0.99    0.99    hot-points_range=100

0.99    0.99    point-query_range=100

1.00    0.99    points-covered-pk_range=100

0.99    1.02    points-covered-si_range=100

1.00    0.99    points-notcovered-pk_range=100

0.99    1.01    points-notcovered-si_range=100

1.00    0.99    random-points_range=1000

1.00    0.99    random-points_range=100

1.00    1.00    random-points_range=10

col-1   col-2   range queries without aggregation

0.99    0.99    range-covered-pk_range=100

0.98    0.99    range-covered-si_range=100

0.99    0.99    range-notcovered-pk_range=100

1.01    1.01    range-notcovered-si_range=100

0.91    0.91    scan_range=100

col-1   col-2   range queries with aggregation

1.04    1.03    read-only-count_range=1000

1.02    1.01    read-only-distinct_range=1000

1.01    1.00    read-only-order_range=1000

1.06    1.06    read-only_range=10000

0.98    0.97    read-only_range=100

0.99    0.99    read-only_range=10

1.02    1.02    read-only-simple_range=1000

1.03    1.03    read-only-sum_range=1000

col-1   col-2   writes

1.46    1.49    delete_range=100

1.32    1.32    insert_range=100

0.99    1.00    read-write_range=100

0.98    1.00    read-write_range=10

0.99    1.00    update-index_range=100

0.95    1.03    update-inlist_range=100

1.00    1.02    update-nonindex_range=100

0.96    1.04    update-one_range=100

1.00    1.01    update-zipf_range=100

1.00    1.00    write-only_range=10000

Postgres 18 beta3, large server, sysbench

This has performance results for Postgres 18 beta3, beta2, beta1, 17.5 and 17.4 using the sysbench benchmark and a large server. The working set is cached and the benchmark is run with high concurrency (40 connections). The goal is to search for CPU and mutex regressions. This work was done by Small Datum LLC and not sponsored

tl;dr

• There might be small regressions (~2%) for several range queries that don't do aggregation. This is similar to what I reported for 18 beta3 on a small server, but here it only occurs for 3 of the 4 microbenchmarks and on the small server it occurs on all 4. I am still uncertain about whether this really is a regression.

Builds, configuration and hardware

I compiled Postgres versions 17.4, 17.5, 18 beta1, 18 beta2 and 18 beta3 from source.

The server is an ax162-s from Hetzner with an AMD EPYC 9454P processor, 48 cores, AMD SMT disabled and 128G RAM. The OS is Ubuntu 22.04. Storage is 2 NVMe devices with SW RAID 1 and 

ext4. More details on it are here.

The config file for Postgres 17.4 and 17.5 is x10a_c32r128.

The config files for Postgres 18 are:

• x10b_c32r128 is functionally the same as x10a_c32r128 but adds io_method=sync
• x10d_c32r128 starts with x10a_c2r128 and adds io_method=io_uring

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 tests are run using 8 tables with 10M rows per table. The read-heavy microbenchmarks run for 600 seconds and the write-heavy for 900 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 charts below with relative QPS. The relative QPS is the following:

(QPS for some version) / (QPS for Postgres 17.5)

When the relative QPS is > 1 then some version is faster than PG 17.5.  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.

Relative to: pg174_o2nofp.x10a_c32r128

col-1 : pg175_o2nofp.x10a_c32r128

col-2 : pg18beta1_o2nofp.x10b_c32r128

col-3 : pg18beta1_o2nofp.x10d_c32r128

col-4 : pg18beta2_o2nofp.x10d_c32r128

col-5 : pg18beta3_o2nofp.x10d_c32r128

col-1   col-2   col-3   col-4   col-5

0.98    0.99    0.99    1.00    0.99    hot-points_range=100

1.01    1.01    1.00    1.01    1.01    point-query_range=100

1.00    1.00    0.99    1.00    1.00    points-covered-pk

1.00    1.01    1.00    1.02    1.00    points-covered-si

1.00    1.01    1.00    1.00    1.00    points-notcovered-pk

1.00    1.00    1.01    1.02    1.00    points-notcovered-si

1.00    1.00    1.00    1.00    1.00    random-points_range=1000

1.00    1.01    1.00    1.00    1.00    random-points_range=100

1.00    1.00    1.00    1.00    1.00    random-points_range=10

1.00    0.97    0.96    0.98    0.97    range-covered-pk

1.00    0.97    0.97    0.98    0.97    range-covered-si

0.99    0.99    0.99    0.99    0.98    range-notcovered-pk

1.00    1.01    1.01    1.00    1.01    range-notcovered-si

1.00    1.02    1.03    1.03    1.02    read-only-count

1.00    1.00    1.00    1.01    1.01    read-only-distinct

1.00    1.00    1.00    1.00    1.00    read-only-order

1.01    1.01    1.02    1.02    1.01    read-only_range=10000

1.00    0.99    0.99    0.99    1.00    read-only_range=100

1.01    0.99    0.99    1.00    0.99    read-only_range=10

1.00    1.01    1.01    1.01    1.01    read-only-simple

1.00    1.02    1.03    1.03    1.02    read-only-sum

1.00    1.13    1.14    1.02    0.91    scan_range=100

1.00    1.13    1.13    1.02    0.90    scan.warm_range=100

1.00    0.99    0.99    0.99    0.99    delete_range=100

0.99    1.00    1.02    0.99    1.00    insert_range=100

1.01    1.00    1.00    1.00    0.99    read-write_range=100

1.00    0.98    1.00    1.01    0.99    read-write_range=10

0.99    0.99    1.02    0.98    0.96    update-index

1.00    1.01    1.00    1.00    1.01    update-inlist

0.98    0.98    0.99    0.98    0.97    update-nonindex

0.95    0.95    0.94    0.93    0.95    update-one_range=100

0.97    0.98    0.98    0.97    0.95    update-zipf_range=100

0.98    0.99    0.99    0.98    0.98    write-only_range=10000

Postgres 18 beta3, small server, sysbench

This has performance results for Postgres 18 beta3, beta2, beta1 and 17.6 using the sysbench benchmark and a small server. The working set is cached and the benchmark is run with low concurrency (1 connection). The goal is to search for CPU regressions. This work was done by Small Datum LLC and not sponsored

tl;dr

• There might be small regressions (~2%) for several range queries that don't do aggregation. This is similar to what I reported for 18 beta1.
• Vacuum continues to be a problem for me and I had to repeat the benchmark a few times to get a stable result. It appears to be a big source of non-deterministic behavior leading to false alarms for CPU regressions in read-heavy tests that run after vacuum. In some ways, RocksDB compaction causes similar problems. Fortunately, InnoDB MVCC GC (purge) does not cause such problems.

Builds, configuration and hardware

I compiled Postgres versions 17.6, 18 beta1, 18 beta2 and 18 beta3 from source.

The server is a Beelink SER7 with a Ryzen 7 7840HS CPU, 32G of RAM, 8 cores with AMD SMT disabled, Ubuntu 24.04 and an NVMe devices with discard enabled and ext4 for the database.

The config file for Postgres 17.6 is x10a_c8r32.

The config files for Postgres 18 are:

• x10b_c8r32 is functionally the same as x10a_c8r32 but adds io_method=sync
• x10b1_c8r32 starts with x10b_c8r32 and adds vacuum_max_eager_freeze_failure_rate =0
• x10b2_c8r32 starts with x10b_c8r32 and adds vacuum_max_eager_freeze_failure_rate =0.99

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 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

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 Postgres 17.6)

When the relative QPS is > 1 then some version is faster than PG 17.6.  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.

The numbers highlighted in yellow below might be from a small regression for range queries that don't do aggregation. But note that this does reproduce for the full table scan microbenchmark (scan). I am not certain it is a regression as this might be from non-deterministic CPU overheads for read-heavy workloads that are run after vacuum. I hope to look at CPU flamegraphs soon.

• the mapping from microbenchmark name to Lua script is here
• the range query without aggregation microbenchmarks use oltp_range_covered.lua with various flags set and the SQL statements it uses are here. All of these return 100 rows.
• the scan microbenchmark uses oltp_scan.lua which is a SELECT with a WHERE clause that filters all rows (empty result set)

Relative to: x.pg176_o2nofp.x10a_c8r32.pk1

col-1 : x.pg18beta1_o2nofp.x10b_c8r32.pk1

col-2 : x.pg18beta2_o2nofp.x10b_c8r32.pk1

col-3 : x.pg18beta3_o2nofp.x10b_c8r32.pk1

col-4 : x.pg18beta3_o2nofp.x10b1_c8r32.pk1

col-5 : x.pg18beta3_o2nofp.x10b2_c8r32.pk1

col-1   col-2   col-3   col-4   col-5 -> point queries

1.00    1.00    0.98    0.99    0.99    hot-points_range=100

1.00    1.01    1.00    1.00    0.99    point-query_range=100

1.00    1.02    1.01    1.01    1.01    points-covered-pk

1.00    1.00    1.00    1.00    1.00    points-covered-si

1.01    1.01    1.00    1.00    1.00    points-notcovered-pk

1.01    1.00    1.00    1.00    1.00    points-notcovered-si

0.99    1.00    0.99    1.00    1.00    random-points_range=1000

1.01    1.00    1.00    1.00    1.00    random-points_range=100

1.01    1.01    1.00    1.00    0.99    random-points_range=10

col-1   col-2   col-3   col-4   col-5 -> range queries w/o agg

0.98    0.99    0.97    0.98    0.96    range-covered-pk_range=100

0.98    0.99    0.96    0.98    0.97    range-covered-si_range=100

0.98    0.98    0.98    0.97    0.98    range-notcovered-pk

0.99    0.99    0.98    0.98    0.98    range-notcovered-si

1.01    1.02    1.00    1.00    1.00    scan

col-1   col-2   col-3   col-4   col-5 -> range queries with agg

1.02    1.01    1.02    1.01    0.98    read-only-count_range=1000

0.98    1.01    1.01    1.00    1.03    read-only-distinct

0.99    0.99    0.99    0.99    0.99    read-only-order_range=1000

1.00    1.00    1.01    1.00    1.01    read-only_range=10000

0.99    0.99    0.99    0.99    0.99    read-only_range=100

0.99    0.99    0.99    0.98    0.99    read-only_range=10

1.01    1.00    1.00    1.00    1.01    read-only-simple

1.01    1.00    1.01    1.00    1.00    read-only-sum_range=1000

col-1   col-2   col-3   col-4   col-5 -> writes

0.99    1.00    0.98    0.98    0.98    delete_range=100

0.99    0.98    0.98    1.00    0.98    insert_range=100

0.99    0.99    0.99    0.98    0.99    read-write_range=100

0.98    0.99    0.99    0.98    0.99    read-write_range=10

1.00    0.99    0.98    0.97    0.99    update-index_range=100

1.01    1.00    0.99    1.01    1.00    update-inlist_range=100

1.00    1.00    0.99    0.96    0.99    update-nonindex_range=100

1.01    1.01    0.99    0.97    0.99    update-one_range=100

1.00    1.00    0.99    0.98    0.99    update-zipf_range=100

1.00    0.99    0.98    0.98    1.00    write-only_range=10000

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.

Postgres 18 beta2: large server, Insert Benchmark, part 2

I repeated the benchmark for one of the workloads used in a recent blog post on Postgres 18 beta2 performance. The workload used 1 client and 1 table with 50M rows that fits in the Postgres buffer pool. In the result I explain here, one of the benchmark steps was run for ~10X more time. Figuring out how long to run the steps in the Insert Benchmark is always a work in progress -- I want to test more things, so I don't want to run steps for too long, but there will be odd results if the run times are too short.

tl;dr

• up to 2% less throughput on range queries in the qr100 benchmark step. This is similar to what I saw in my previous report.
• up to 12% more throughput on the l.i2 benchmark step in PG beta1 and beta2. This is much better than what I saw in my previous report.

Details

Details on the benchmark are in my previous post.

The benchmark is explained here and was run for one workloads -- 1 client, cached.

• run with 1 client, 1 table and a cached database
• load 50M rows in step l.i0, do 160M writes in step l.i1 and 40M in l.i2. Note that here the l.i1 and l.i2 steps run for ~10X longer than in my previous post.

The benchmark steps are:

• l.i0
• insert X 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 Y 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 Z 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.

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 Postgres 17.4.

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 red for <= 0.97, green for >= 1.03 and grey for values between 0.98 and 1.02.

Results: 1 client, cached

Normally I summarize the summary but I don't do that here to save space.

But the tl;dr is:

• up to 2% less throughput on range queries in the qr100 benchmark step. This is similar to what I saw in my previous report.
• up to 12% more throughput on the l.i2 benchmark step in PG beta1 and beta2. This is much better than what I saw in my previous report.