clickhouse ssb-dbgen数据构造 及 clickhouse-benchmark简单压测

一、 测试数据构造

1. 数据样例

官方文档有给出一批数据样例。优点是比较真实，缺点是太大了，动辄上百G不适合简单小测试

• Anonymized Yandex.Metrica Dataset
• Star Schema Benchmark
• WikiStat
• Terabyte of Click Logs from Criteo
• AMPLab Big Data Benchmark
• New York Taxi Data
• OnTime

相对来说 ssb-dbgen工具 生成的表比较简单，数据量也可以自己控制，更加方便。

2. ssb-dbgen下载安装

• 下载

https://github.com/vadimtk/ssb-dbgen

• 安装依赖包

yum -y install gcc gcc-c++ make cmake

• ssb-dbgen安装：解压，进入目录，执行 make 即可

3. ssb-dbgen生成测试数据

ssb-dbgen工具指定参数可以生成如下表的数据，其中lineorder是最大的

• c–customer.tbl
• d–date.tbl
• p–part.tbl
• s–supplier.tbl
• l–lineorder.tbl
• a-all

./dbgen -s 10 -T a

       -s 100 lineorder表会生成6亿行数据（约67G），-s 1000则会为其生成60亿行数据（约670G），需要大量空间和时间，注意控制。

4. 创建表结构

测试表可以都用，也可以挑一些，官方文档只建了4个

CREATE TABLE customer
(C_CUSTKEY       UInt32,C_NAME          String,C_ADDRESS       String,C_CITY          LowCardinality(String),C_NATION        LowCardinality(String),C_REGION        LowCardinality(String),C_PHONE         String,C_MKTSEGMENT    LowCardinality(String)
)
ENGINE = MergeTree ORDER BY (C_CUSTKEY);CREATE TABLE lineorder
(LO_ORDERKEY             UInt32,LO_LINENUMBER           UInt8,LO_CUSTKEY              UInt32,LO_PARTKEY              UInt32,LO_SUPPKEY              UInt32,LO_ORDERDATE            Date,LO_ORDERPRIORITY        LowCardinality(String),LO_SHIPPRIORITY         UInt8,LO_QUANTITY             UInt8,LO_EXTENDEDPRICE        UInt32,LO_ORDTOTALPRICE        UInt32,LO_DISCOUNT             UInt8,LO_REVENUE              UInt32,LO_SUPPLYCOST           UInt32,LO_TAX                  UInt8,LO_COMMITDATE           Date,LO_SHIPMODE             LowCardinality(String)
)
ENGINE = MergeTree PARTITION BY toYear(LO_ORDERDATE) ORDER BY (LO_ORDERDATE, LO_ORDERKEY);CREATE TABLE part
(P_PARTKEY       UInt32,P_NAME          String,P_MFGR          LowCardinality(String),P_CATEGORY      LowCardinality(String),P_BRAND         LowCardinality(String),P_COLOR         LowCardinality(String),P_TYPE          LowCardinality(String),P_SIZE          UInt8,P_CONTAINER     LowCardinality(String)
)
ENGINE = MergeTree ORDER BY P_PARTKEY;CREATE TABLE supplier
(S_SUPPKEY       UInt32,S_NAME          String,S_ADDRESS       String,S_CITY          LowCardinality(String),S_NATION        LowCardinality(String),S_REGION        LowCardinality(String),S_PHONE         String
)
ENGINE = MergeTree ORDER BY S_SUPPKEY;

将star schema转换为flat schema（表关联转为大宽表）：

SET max_memory_usage = 20000000000;CREATE TABLE lineorder_flat
ENGINE = MergeTree ORDER BY (LO_ORDERDATE, LO_ORDERKEY)
AS SELECTl.LO_ORDERKEY AS LO_ORDERKEY,l.LO_LINENUMBER AS LO_LINENUMBER,l.LO_CUSTKEY AS LO_CUSTKEY,l.LO_PARTKEY AS LO_PARTKEY,l.LO_SUPPKEY AS LO_SUPPKEY,l.LO_ORDERDATE AS LO_ORDERDATE,l.LO_ORDERPRIORITY AS LO_ORDERPRIORITY,l.LO_SHIPPRIORITY AS LO_SHIPPRIORITY,l.LO_QUANTITY AS LO_QUANTITY,l.LO_EXTENDEDPRICE AS LO_EXTENDEDPRICE,l.LO_ORDTOTALPRICE AS LO_ORDTOTALPRICE,l.LO_DISCOUNT AS LO_DISCOUNT,l.LO_REVENUE AS LO_REVENUE,l.LO_SUPPLYCOST AS LO_SUPPLYCOST,l.LO_TAX AS LO_TAX,l.LO_COMMITDATE AS LO_COMMITDATE,l.LO_SHIPMODE AS LO_SHIPMODE,c.C_NAME AS C_NAME,c.C_ADDRESS AS C_ADDRESS,c.C_CITY AS C_CITY,c.C_NATION AS C_NATION,c.C_REGION AS C_REGION,c.C_PHONE AS C_PHONE,c.C_MKTSEGMENT AS C_MKTSEGMENT,s.S_NAME AS S_NAME,s.S_ADDRESS AS S_ADDRESS,s.S_CITY AS S_CITY,s.S_NATION AS S_NATION,s.S_REGION AS S_REGION,s.S_PHONE AS S_PHONE,p.P_NAME AS P_NAME,p.P_MFGR AS P_MFGR,p.P_CATEGORY AS P_CATEGORY,p.P_BRAND AS P_BRAND,p.P_COLOR AS P_COLOR,p.P_TYPE AS P_TYPE,p.P_SIZE AS P_SIZE,p.P_CONTAINER AS P_CONTAINER
FROM lineorder AS l
INNER JOIN customer AS c ON c.C_CUSTKEY = l.LO_CUSTKEY
INNER JOIN supplier AS s ON s.S_SUPPKEY = l.LO_SUPPKEY
INNER JOIN part AS p ON p.P_PARTKEY = l.LO_PARTKEY;

5. 导入数据

cd ssb-dbgen-masterclickhouse-client --password --query "INSERT INTO hydb.customer FORMAT CSV" < customer.tbl
clickhouse-client --password --query "INSERT INTO hydb.part FORMAT CSV" < part.tbl
clickhouse-client --password --query "INSERT INTO hydb.supplier FORMAT CSV" < supplier.tbl
clickhouse-client --password --query "INSERT INTO hydb.lineorder FORMAT CSV" < lineorder.tbl

如果还不够，也可以多次执行以下语句，至满意数据量

insert into hydb.lineorder select * from hydb.lineorder;

二、 clickhouse-benchmark简单压测

clickhouse-benchmark是自带的一个简单压测工具，可以控制执行SQL的次数、并发度等。

1. 常用参数

• -c 并发度，例如10个并发同时执行指定SQL
• -d 间隔几秒执行SQL，默认为1，0表示禁用
• -h 指定连接的db ip，可以同时指定多个-h 连接多个库进行对比
• -i SQL执行总次数
• -r 有多个SQL时，以随机顺序执行
• -t 指定压测时间，到达指定时间后停止发送压测SQL。默认为0，表示无限制

2. 两种用法

• 直接执行，适合简单SQL

echo "SELECT toYear(LO_ORDERDATE),count(*) FROM hydb.lineorder group by toYear(LO_ORDERDATE) order by 2 desc" | clickhouse-benchmark --password='xxxx' -i 10

• 执行SQL文件，适合复杂、批量语句

vi queries_file#查询语句
SELECT sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue FROM hydb.lineorder WHERE toYear(LO_ORDERDATE) = 1993 AND LO_DISCOUNT BETWEEN 1 AND 3 AND LO_QUANTITY < 25;

执行压测

clickhouse-benchmark --password='xxxx' -i 10 < queries_file

压测的语句可以简单写点，也可以参考：Star Schema Benchmark | ClickHouse Docs

3. 结果分析

Queries executed: 10.

localhost:9000, queries 10, QPS: 6.772, RPS: 67904487.440, MiB/s: 518.070, result RPS: 67721584.984, result MiB/s: 516.675.

0.000% 0.145 sec.
10.000% 0.146 sec.
20.000% 0.146 sec.
30.000% 0.146 sec.
40.000% 0.147 sec.
50.000% 0.148 sec.
60.000% 0.148 sec.
70.000% 0.148 sec.
80.000% 0.149 sec.
90.000% 0.150 sec.
95.000% 0.150 sec.
99.000% 0.150 sec.
99.900% 0.150 sec.
99.990% 0.150 sec.

在结果报告中，您可以找到:

• 查询数量：参见Queries executed:字段。

• 状态码（按顺序给出）:

  • ClickHouse服务器的连接信息。
  • 已处理的查询数。
  • QPS：服务端每秒处理的查询数量
  • RPS：服务器每秒读取多少行
  • MiB/s：服务器每秒读取多少字节的数据
  • 结果RPS：服务端每秒生成多少行的结果集数据
  • 结果MiB/s.服务端每秒生成多少字节的结果集数据

• 查询执行时间的百分比。

参考

https://github.com/vadimtk/ssb-dbgen

Star Schema Benchmark | ClickHouse Docs

性能测试 | ClickHouse Docs

使用 ssb-dbgen 对 ClickHouse 压测_数据库人生的博客-CSDN博客