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