clickhouse简介
ClickHouse是一个面向列存储的数据库管理系统，可以使用SQL查询实时生成分析数据报告，主要用于OLAP(在线分析处理查询)场景。关于clickhouse原理以及基础知识在以后学习中慢慢总结。
1、Docker安装ClickHouse
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docker run -d --name some-clickhouse-server \-p 8123:8123 -p 9009:9009 -p 9091:9000 \--ulimit nofile=262144:262144 \-v /home/clickhouse:/var/lib/clickhouse \yandex/clickhouse-server |
2、下载SSBM工具
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1、git clone https://github.com/vadimtk/ssb-dbgen.git2、cd ssb-dbgen3、make |
3、生成数据
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./dbgen -s 100 -T c./dbgen -s 100 -T p./dbgen -s 100 -T s./dbgen -s 100 -T l./dbgen -s 100 -T d |
查看下数据
4、建表
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CREATE TABLE default.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); |
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CREATE TABLE default.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); |
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CREATE TABLE default.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; |
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CREATE TABLE default.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; |
5、导入数据
准备工作：
先把ssb-dbgen(lineorder.tbl,customer.tbl,part.tbl,supplier.tbl)考到clickhouse-server容器里面
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clickhouse-client --query "INSERT INTO customer FORMAT CSV" < customer.tblclickhouse-client --query "INSERT INTO part FORMAT CSV" < part.tblclickhouse-client --query "INSERT INTO supplier FORMAT CSV" < supplier.tblclickhouse-client --query "INSERT INTO lineorder FORMAT CSV" < lineorder.tbl |
注意：如果此处报错，检查clickhouse的配置（端口是否占用，是否设置用户和密码）
6、测试
| 编号 | 查询语句SQL | 耗时(ms) |
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| Q1 | SELECT SUM(l.LO_EXTENDEDPRICE * l.LO_DISCOUNT) AS revenue FROM lineorder_flat WHERE toYear(l.LO_ORDERDATE) = 1993 AND l.LO_DISCOUNT BETWEEN 1 AND 3 AND l.LO_QUANTITY < 25; | 36 |
| Q2 | SELECT SUM(l.LO_EXTENDEDPRICE * l.LO_DISCOUNT) AS revenue FROM lineorder_flat WHERE toYYYYMM(l.LO_ORDERDATE) = 199401 AND l.LO_DISCOUNT BETWEEN 4 AND 6 AND l.LO_QUANTITYBETWEEN 26 AND 35; | 12 |
| Q3 | SELECT SUM(l.LO_EXTENDEDPRICE * l.LO_DISCOUNT) AS revenue FROM lineorder_flat WHERE toISOWeek(l.LO_ORDERDATE) = 6 AND toYear(l.LO_ORDERDATE) = 1994 AND l.LO_DISCOUNT BETWEEN 5 AND 7 AND l.LO_QUANTITY BETWEEN 26 AND 35; | 12 |
| Q4 | SELECT SUM(l.LO_REVENUE), toYear(l.LO_ORDERDATE) AS year, p.P_BRAND FROM lineorder_flat WHERE p.P_CATEGORY = ‘MFGR#12' AND s.S_REGION = ‘AMERICA' GROUP BY year, p.P_BRAND ORDER BY year, p.P_BRAND; | 16 |
| Q5 | SELECT SUM(l.LO_REVENUE), toYear(l.LO_ORDERDATE) AS year, p.P_BRAND FROM lineorder_flat WHERE p.P_BRAND BETWEEN ‘MFGR#2221' AND ‘MFGR#2228' AND s.S_REGION = ‘ASIA' GROUP BY year, p.P_BRAND ORDER BY year, p.P_BRAND; | 21 |
| Q6 | SELECT toYear(l.LO_ORDERDATE) AS year, s.S_CITY, p.P_BRAND, SUM(l.LO_REVENUE -l.LO_SUPPLYCOST) AS profit FROM lineorder_flat WHERE s.S_NATION = ‘UNITED STATES' AND (year = 1997 OR year = 1998) AND p.P_CATEGORY = ‘MFGR#14' GROUP BY year, s.S_CITY, p.P_BRAND ORDER BY year, s.S_CITY, p.P_BRAND; | 19 |
官网参考：
https://clickhouse.tech/docs/zh/getting-started/example-datasets/star-schema/#star-schema-benchmark
以上就是Docker创建ClickHouse 并初始化数据测试的详细内容，更多关于Docker的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/Joe192/article/details/118159352
