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DM 1.0-GA Benchmark Report
Learn about the DM 1.0-GA benchmark report.
benchmark

DM 1.0-GA Benchmark Report

This benchmark report describes the test purpose, environment, scenario, and result for DM 1.0-GA.

Test purpose

The purpose of this test is to test the performance of DM full import and incremental replication.

Test environment

Machine information

System information:

Machine IP Operation system Kernel version File system type
172.16.4.39 CentOS Linux release 7.6.1810 3.10.0-957.1.3.el7.x86_64 ext4
172.16.4.40 CentOS Linux release 7.6.1810 3.10.0-957.1.3.el7.x86_64 ext4
172.16.4.41 CentOS Linux release 7.6.1810 3.10.0-957.1.3.el7.x86_64 ext4
172.16.4.42 CentOS Linux release 7.6.1810 3.10.0-957.1.3.el7.x86_64 ext4
172.16.4.43 CentOS Linux release 7.6.1810 3.10.0-957.1.3.el7.x86_64 ext4
172.16.4.44 CentOS Linux release 7.6.1810 3.10.0-957.1.3.el7.x86_64 ext4

Hardware information:

Type Specification
CPU 40 CPUs, Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz
Memory 192GB, 12 * 16GB DIMM DDR4 2133 MHz
Disk Intel DC P4510 4TB NVMe PCIe 3.0
Network card 10 Gigabit Ethernet

Others:

  • Network rtt between servers: rtt min/avg/max/mdev = 0.074/0.088/0.121/0.019 ms

Cluster topology

Machine IP Deployment instance
172.16.4.39 PD1, DM-worker1, DM-master
172.16.4.40 PD2, MySQL1
172.16.4.41 PD3, TiDB
172.16.4.42 TiKV1
172.16.4.43 TiKV2
172.16.4.44 TiKV3

Version information

  • MySQL version: 5.7.27-log
  • TiDB version: v4.0.0-alpha-198-gbde7f440e
  • DM version: v1.0.1
  • Sysbench version: 1.0.17

Test scenario

Data flow

MySQL1 (172.16.4.40) -> DM-worker1 (172.16.4.39) -> TiDB (172.16.4.41)

Public configuration or data

Database table structure used for the test

{{< copyable "sql" >}}

CREATE TABLE `sbtest` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `k` int(11) NOT NULL DEFAULT '0',
  `c` char(120) CHARSET utf8mb4 COLLATE utf8mb4_bin NOT NULL DEFAULT '',
  `pad` char(60) CHARSET utf8mb4 COLLATE utf8mb4_bin NOT NULL DEFAULT '',
  PRIMARY KEY (`id`),
  KEY `k_1` (`k`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin

Database configuration

We use TiDB Ansible to deploy the TiDB cluster, and use default configuration provided in TiDB Ansible.

Full import benchmark case

Test procedure

  • Set up environment
  • Use sysbench to create the table and generate the initial data in upstream MySQL
  • Start DM-task in the full mode

Sysbench test script used for preparing initial data:

{{< copyable "shell-regular" >}}

sysbench --test=oltp_insert --tables=4 --mysql-host=172.16.4.40 --mysql-port=3306 --mysql-user=root --mysql-db=dm_benchmark --db-driver=mysql --table-size=50000000 prepare

Full import benchmark result

item dump thread mydumper extra-args dump speed (MB/s)
enable single table concurrent 32 "-r 320000 --regex '^sbtest.*'" 191.03
disable single table concurrent 32 "--regex '^sbtest.*'" 72.22
item latency of execute transaction (s) statement per transaction data size (GB) time (s) import speed (MB/s)
load data 1.737 4878 38.14 2346.9 16.64

Benchmark result with different pool size in load unit

Full import data size in benchmark case is 3.78 GB, which is generated from sysbench by the following script:

{{< copyable "shell-regular" >}}

sysbench --test=oltp_insert --tables=4 --mysql-host=172.16.4.40 --mysql-port=3306 --mysql-user=root --mysql-db=dm_benchmark --db-driver=mysql --table-size=5000000 prepare
load pool size latency of execution txn (s) import time (s) import speed (MB/s) TiDB 99 duration (s)
2 0.250 425.9 9.1 0.23
4 0.523 360.1 10.7 0.41
8 0.986 267.0 14.5 0.93
16 2.022 265.9 14.5 2.68
32 3.778 262.3 14.7 6.39
64 7.452 281.9 13.7 8.00

Benchmark result with different row count in per statement

Full import data size in this benchmark case is 3.78 GB, load unit pool size uses 32. The statement count is controlled by mydumper parameters.

row count in per statement mydumper extra-args latency of execution txn (s) import time (s) import speed (MB/s) TiDB 99 duration (s)
7426 -s 1500000 -r 320000 6.982 258.3 15.0 10.34
4903 -s 1000000 -r 320000 3.778 262.3 14.7 6.39
2470 -s 500000 -r 320000 1.962 271.36 14.3 2.00
1236 -s 250000 -r 320000 1.911 283.3 13.7 1.50
618 -s 125000 -r 320000 0.683 299.9 12.9 0.73
310 -s 62500 -r 320000 0.413 322.6 12.0 0.49

Increase replication benchmark case

Test procedure

  • Set up environment
  • Use sysbench to create the table and generate the initial data in upstream MySQL
  • Start DM-task in the all mode, and wait until the task enters sync unit
  • Use sysbench to generate incremental data in upstream MySQL, use query-status to watch the DM replication status, and observe the monitoring metrics of DM and TiDB on Grafana

Benchmark result for incremental replication

Upstream sysbench test script:

{{< copyable "shell-regular" >}}

sysbench --test=oltp_insert --tables=4 --num-threads=32 --mysql-host=172.17.4.40 --mysql-port=3306 --mysql-user=root --mysql-db=dm_benchmark --db-driver=mysql --report-interval=10 --time=1800 run

DM sync unit worker-count is 32, and batch size is 100 in this benchmark case.

items qps tps 95% Latency
MySQL 42.79k 42.79k 1.18ms
DM relay log unit - 11.3MB/s 45us (read duration)
DM binlog replication unit 22.97k (binlog event received qps, not including skipped events) - 20ms (txn execution latency)
TiDB 31.30k (Begin/Commit 3.93k Insert 22.76k) 4.16k 95%: 6.4ms 99%: 9ms

Benchmark result with different sync unit concurrency

sync unit worker-count DM tps DM execution latency (ms) TiDB qps TiDB 99 duration (ms)
4 7074 63 7.1k 3
8 14684 64 14.9k 4
16 23486 56 24.9k 6
32 23345 28 29.2k 10
64 23302 30 31.2k 16
1024 22225 70 56.9k 70

Benchmark result with different SQL distribution

sysbench type relay log flush speed (MB/s) DM tps DM execution latency (ms) TiDB qps TiDB 99 duration (ms)
insert_only 11.3 23345 28 29.2k 10
write_only 18.7 33470 129 34.6k 11

Recommended parameters

dump unit

We recommend that the statement size be 200 KB1 MB, and row count in each statement be approximately 10005000, which is based on the actual row size in your scenario.

load unit

We recommend that you set pool-size to 16.

sync unit

We recommend that you set batch size to 100 and worker-count to 16~32.