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
bash
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
bash
./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
sql
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）：
sql
SET max_memory_usage = 20000000000;
CREATE TABLE lineorder_flat
ENGINE = MergeTree ORDER BY (LO_ORDERDATE, LO_ORDERKEY)
AS SELECT
l.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
bash
cd ssb-dbgen-master
clickhouse-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
sql
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
bash
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
bash
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
bash
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