clickhouse单机安装和性能测试(ssb数据集)
目录
1、单机安装
2、clickhouse修改数据目录
3、clickhouse性能测试
4、查看数据库和表的容量大小
1、单机安装
官网安装教程：https://clickhouse.com/docs/zh/getting-started/install
这里使用官方预编译的tgz软件包进行安装。在 https://packages.clickhouse.com/tgz/stable/ 下载最新的安装包。
然后按照 common-static, common-static-dbg, server, client的顺序解压，执行对应解压的doinst.sh即可。
tar -xzvf clickhouse-common-static-21.1.9.41.tgz clickhouse-common-static-21.1.9.41/install/doinst.sh tar -xzvf clickhouse-common-static-dbg-21.1.9.41.tgz clickhouse-common-static-dbg-21.1.9.41/install/doinst.sh tar -xzvf clickhouse-server-21.1.9.41.tgz clickhouse-server-21.1.9.41/install/doinst.sh
// 提示输入defalut用户的密码，如果选择不需密码直接回车即可
tar -xzvf clickhouse-client-21.1.9.41.tgz clickhouse-client-21.1.9.41/install/doinst.sh
使用 clickhouse --help 查看指令。clickhouse start/stop/restart 启动或停止服务，clickhouse-client 使用客户端连接。
默认，配置文件位于 /etc/clickhouse-server、/etc/clickhouse-client， 表数据位于 /var/lib/clickhouse/data、/var/lib/clickhouse/store、/var/lib/clickhouse/metadata，运行日志位于 /var/log/clickhouse-server
开启允许远程连接：修改/etc/clickhouse-server/config.xml，打开 <listen_host>::</listen_host> 的注释。然后关闭防火墙。开启远程连接后，可以通过 http://$clickhouse_server_ip:8123 进行HTTP访问。
本地打开cmd，通过mysql 客户端连接
mysql -udefault -P9004 -h192.168.xxx.xxx -p // 回车，输入密码。没有密码，直接回车
2、clickhouse修改数据目录
clickhouse 默认数据目录在 /var/lib/clickhouse，一般分区空间有限，需要修改。只要停止数据库之后移走该目录再软连接回原地址，即可不修改 config.xml 实现对数据目录的修改。
#先停库 sudo clickhouse stop sudo mkdir -p /sdb1/clickhousedata01 #权限一定要修改否则没权限就启动不了 sudo chown -R clickhouse:clickhouse /sdb1/clickhousedata01 sudo mv /var/lib/clickhouse /sdb1/clickhousedata01 #建立软连接 sudo ln -s /sdb1/clickhousedata01 /var/lib/clickhouse sudo chown -R clickhouse:clickhouse /var/lib/clickhouse sudo ls -l /var/lib/clickhouse #启动 sudo clickhouse start
3、clickhouse性能测试
使用 ssb 数据集进行测试：https://clickhouse.com/docs/zh/getting-started/example-datasets/star-schema
ClickHouse 性能测试：https://developer.aliyun.com/article/940057
导入数据时，官网上默认是导入到 default 库，如果是导入自己创建的数据库，比如 db1
clickhouse-client --query "INSERT INTO db1.customer FORMAT CSV" < customer.tbl
官方ssb测试数据集只提供了4张表的创建，以及单表查询的测试。需要跟starrocks做对比，因此自己参考starrocks的多表查询测试sql（https://www.starrocks.com/zh-CN/blog/1.8），自己写了一下dates表的建表语句、以及多表查询的sql。
CREATE TABLE dates ( D_DATEKEY UInt32, D_DATE String, D_DAYOFWEEK LowCardinality(String), D_MONTH LowCardinality(String), D_YEAR UInt32, D_YEARMONTHNUM UInt32, D_YEARMONTH String, D_DAYNUMINWEEK UInt8, D_DAYNUMINMONTH UInt8, D_DAYNUMINYEAR UInt8, D_MONTHNUMINYEAR UInt8, D_WEEKNUMINYEAR UInt8, D_SELLINGSEASON LowCardinality(String), D_LASTDAYINWEEKFL LowCardinality(String), D_LASTDAYINMONTHFL LowCardinality(String), D_HOLIDAYFL LowCardinality(String), D_WEEKDAYFL LowCardinality(String) ) ENGINE = MergeTree ORDER BY D_DATEKEY;
多表查询sql
--Q1.1 select sum(LO_REVENUE) as revenue from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY where D_YEAR = 1993 and LO_DISCOUNT between 1 and 3 and LO_QUANTITY < 25; -- Q1.2 select sum(LO_REVENUE) as revenue from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY where D_YEARMONTHNUM = 199401 and LO_DISCOUNT between 4 and 6 and LO_QUANTITY between 26 and 35; -- Q1.3 select sum(LO_REVENUE) as revenue from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY where D_WEEKNUMINYEAR = 6 and D_YEAR = 1994 and LO_DISCOUNT between 5 and 7 and LO_QUANTITY between 26 and 35; --Q2.1 select sum(LO_REVENUE) as lo_revenue, D_YEAR, P_BRAND from lineorder inner join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join part on LO_PARTKEY = P_PARTKEY join supplier on LO_SUPPKEY = S_SUPPKEY where P_CATEGORY = 'MFGR#12' and S_REGION = 'AMERICA' group by D_YEAR, P_BRAND order by D_YEAR, P_BRAND; --Q2.2 select sum(LO_REVENUE) as lo_revenue, D_YEAR, P_BRAND from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join part on LO_PARTKEY = P_PARTKEY join supplier on LO_SUPPKEY = S_SUPPKEY where P_BRAND between 'MFGR#2221' and 'MFGR#2228' and S_REGION = 'ASIA' group by D_YEAR, P_BRAND order by D_YEAR, P_BRAND; --Q2.3 select sum(LO_REVENUE) as lo_revenue, D_YEAR, P_BRAND from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join part on LO_PARTKEY = P_PARTKEY join supplier on LO_SUPPKEY = S_SUPPKEY where P_BRAND = 'MFGR#2239' and S_REGION = 'EUROPE' group by D_YEAR, P_BRAND order by D_YEAR, P_BRAND; --Q3.1 select C_NATION, S_NATION, D_YEAR, sum(LO_REVENUE) as lo_revenue from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join customer on LO_CUSTKEY = C_CUSTKEY join supplier on LO_SUPPKEY = S_SUPPKEY where C_REGION = 'ASIA' and S_REGION = 'ASIA'and D_YEAR >= 1992 and D_YEAR <= 1997 group by C_NATION, S_NATION, D_YEAR order by D_YEAR asc, lo_revenue desc; --Q3.2 select C_CITY, S_CITY, D_YEAR, sum(LO_REVENUE) as lo_revenue from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join customer on LO_CUSTKEY = C_CUSTKEY join supplier on LO_SUPPKEY = S_SUPPKEY where C_NATION = 'UNITED STATES' and S_NATION = 'UNITED STATES' and D_YEAR >= 1992 and D_YEAR <= 1997 group by C_CITY, S_CITY, D_YEAR order by D_YEAR asc, lo_revenue desc; --Q3.3 select C_CITY, S_CITY, D_YEAR, sum(LO_REVENUE) as lo_revenue from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join customer on LO_CUSTKEY = C_CUSTKEY join supplier on LO_SUPPKEY = S_SUPPKEY where (C_CITY='UNITED KI1' or C_CITY='UNITED KI5') and (S_CITY='UNITED KI1' or S_CITY='UNITED KI5') and D_YEAR >= 1992 and D_YEAR <= 1997 group by C_CITY, S_CITY, D_YEAR order by D_YEAR asc, lo_revenue desc; --Q3.4 select C_CITY, S_CITY, D_YEAR, sum(LO_REVENUE) as lo_revenue from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join customer on LO_CUSTKEY = C_CUSTKEY join supplier on LO_SUPPKEY = S_SUPPKEY where (C_CITY='UNITED KI1' or C_CITY='UNITED KI5') and (S_CITY='UNITED KI1' or S_CITY='UNITED KI5') and D_YEARMONTH = 'Dec1997' group by C_CITY, S_CITY, D_YEAR order by D_YEAR asc, lo_revenue desc; --Q4.1 select D_YEAR, C_NATION, sum(LO_REVENUE) - sum(LO_SUPPLYCOST) as profit from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join customer on LO_CUSTKEY = C_CUSTKEY join supplier on LO_SUPPKEY = S_SUPPKEY join part on LO_PARTKEY = P_PARTKEY where C_REGION = 'AMERICA' and S_REGION = 'AMERICA' and (P_MFGR = 'MFGR#1' or P_MFGR = 'MFGR#2') group by D_YEAR, C_NATION order by D_YEAR, C_NATION; --Q4.2 select D_YEAR, S_NATION, P_CATEGORY, sum(LO_REVENUE) - sum(LO_SUPPLYCOST) as profit from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join customer on LO_CUSTKEY = C_CUSTKEY join supplier on LO_SUPPKEY = S_SUPPKEY join part on LO_PARTKEY = P_PARTKEY where C_REGION = 'AMERICA' and S_REGION = 'AMERICA' and (D_YEAR = 1997 or D_YEAR = 1998) and (P_MFGR = 'MFGR#1' or P_MFGR = 'MFGR#2') group by D_YEAR, S_NATION, P_CATEGORY order by D_YEAR, S_NATION, P_CATEGORY; --Q4.3 select D_YEAR, S_CITY, P_BRAND, sum(LO_REVENUE) - sum(LO_SUPPLYCOST) as profit from lineorder join dates on toYYYYMMDD(LO_ORDERDATE) = D_DATEKEY join customer on LO_CUSTKEY = C_CUSTKEY join supplier on LO_SUPPKEY = S_SUPPKEY join part on LO_PARTKEY = P_PARTKEY where C_REGION = 'AMERICA'and S_NATION = 'UNITED STATES' and (D_YEAR = 1997 or D_YEAR = 1998) and P_CATEGORY = 'MFGR#14' group by D_YEAR, S_CITY, P_BRAND order by D_YEAR, S_CITY, P_BRAND;
4、查看数据库和表的容量大小
查看数据库容量、行数、压缩率
SELECT sum(rows) AS `总行数`, formatReadableSize(sum(data_uncompressed_bytes)) AS `原始大小`, formatReadableSize(sum(data_compressed_bytes)) AS `压缩大小`, round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100, 0) AS `压缩率` FROM system.parts ┌────总行数─┬─原始大小──┬─压缩大小─┬─压缩率─┐ │ 326819026 │ 77.15 GiB │ 5.75 GiB │ 7 │ └───────────┴───────────┴──────────┴────────┘ 1 rows in set. Elapsed: 0.047 sec. Processed 1.04 thousand rows, 520.93 KB (21.95 thousand rows/s., 11.02 MB/s.)
查看数据表容量、行数、压缩率，如果查询的table在多个库存在，where 条件加上 【AND database = '指定database'】
--在此查询一张临时表的信息 SELECT table AS `表名`, sum(rows) AS `总行数`, formatReadableSize(sum(data_uncompressed_bytes)) AS `原始大小`, formatReadableSize(sum(data_compressed_bytes)) AS `压缩大小`, round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100, 0) AS `压缩率` FROM system.parts WHERE table IN ('temp_1') 【AND database = '指定database'】 GROUP BY table ┌─表名───┬──总行数─┬─原始大小───┬─压缩大小──┬─压缩率─┐ │ temp_1 │ 3127523 │ 838.21 MiB │ 60.04 MiB │ 7 │ └────────┴─────────┴────────────┴───────────┴────────┘ 1 rows in set. Elapsed: 0.008 sec.
查看数据表分区信息
--查看测试表在19年12月的分区信息 SELECT partition AS `分区`, sum(rows) AS `总行数`, formatReadableSize(sum(data_uncompressed_bytes)) AS `原始大小`, formatReadableSize(sum(data_compressed_bytes)) AS `压缩大小`, round((sum(data_compressed_bytes) / sum(data_uncompressed_bytes)) * 100, 0) AS `压缩率` FROM system.parts WHERE (database IN ('default')) AND (table IN ('temp_1')) AND (partition LIKE '2019-12-%') GROUP BY partition ORDER BY partition ASC ┌─分区───────┬─总行数─┬─原始大小──┬─压缩大小───┬─压缩率─┐ │ 2019-12-01 │ 24 │ 6.17 KiB │ 2.51 KiB │ 41 │ │ 2019-12-02 │ 9215 │ 2.45 MiB │ 209.74 KiB │ 8 │ │ 2019-12-03 │ 17265 │ 4.46 MiB │ 453.78 KiB │ 10 │ │ 2019-12-04 │ 27741 │ 7.34 MiB │ 677.25 KiB │ 9 │ │ 2019-12-05 │ 31500 │ 8.98 MiB │ 469.30 KiB │ 5 │ │ 2019-12-06 │ 157 │ 37.50 KiB │ 4.95 KiB │ 13 │ │ 2019-12-07 │ 110 │ 32.75 KiB │ 3.86 KiB │ 12 │ └────────────┴────────┴───────────┴────────────┴────────┘ 7 rows in set. Elapsed: 0.005 sec.
查看数据表字段的信息
SELECT column AS `字段名`, any(type) AS `类型`, formatReadableSize(sum(column_data_uncompressed_bytes)) AS `原始大小`, formatReadableSize(sum(column_data_compressed_bytes)) AS `压缩大小`, sum(rows) AS `行数` FROM system.parts_columns WHERE (database = 'default') AND (table = 'temp_1') GROUP BY column ORDER BY column ASC ┌─字段名───────────┬─类型─────┬─原始大小───┬─压缩大小───┬────行数─┐ │ a │ String │ 23.83 MiB │ 134.13 KiB │ 3127523 │ │ b │ String │ 19.02 MiB │ 127.72 KiB │ 3127523 │ │ c │ String │ 5.97 MiB │ 49.09 KiB │ 3127523 │ │ d │ String │ 3.95 MiB │ 532.86 KiB │ 3127523 │ │ e │ String │ 5.17 MiB │ 49.47 KiB │ 3127523 │ │ totalDate │ DateTime │ 11.93 MiB │ 1.26 MiB │ 3127523 │ └──────────────────┴──────────┴────────────┴────────────┴─────────┘
posted on 2022-05-31 10:52 wenbin_ouyang 阅读(1101) 评论(0) 编辑 收藏 举报
