面试官：单表6亿数据怎么做性能测试_clickhouse单表存储多少数据量-优快云博客

本文链接：https://blog.youkuaiyun.com/qq_19007169/article/details/124793078

最近由于项目需求使用到了 clickhouse 做分析数据库，于是用测试环境做了一个单表 6 亿数据量的性能测试，记录一下测试结果，有做超大数据量分析技术选型需求的朋友可以参考下。

服务器信息

- CPU：Intel Xeon Gold 6240 @ 8x 2.594GHz
- 内存：32G
- 系统：CentOS 7.6
- Linux内核版本：3.10.0
- 磁盘类型：机械硬盘
- 文件系统：ext4

Clickhouse信息

- 部署方式：单机部署
- 版本：20.8.11.17

测试情况

测试数据和测试方法来自 clickshouse 官方的 Star Schema Benchmark，URL：https://clickhouse.com/docs/en/getting-started/example-datasets/star-schema/

按照官方指导造出了测试数据之后，先看一下数据量和空间占用情况。

数据量和空间占用

可以看到 clickhouse 的压缩率很高，压缩率都在 50 以上，基本可以达到 70 左右。数据体积的减小可以非常有效的减少磁盘空间占用、提高 I/O 性能，这对整体查询性能的提升非常有效。

supplier、customer、part、lineorder 为一个简单的「供应商-客户-订单-地区」的星型模型，lineorder_flat 为根据这个星型模型数据关系合并的大宽表，所有分析都直接在这张大宽表中执行，减少不必要的表关联，符合我们实际工作中的分析建表逻辑。

以下性能测试的所有分析 SQL 都在这张大宽表中运行，未进行表关联查询。

查询性能测试详情

Query 1.1

SELECT sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenueFROM lineorder_flatWHERE (toYear(LO_ORDERDATE) = 1993) AND ((LO_DISCOUNT >= 1) AND (LO_DISCOUNT <= 3)) AND (LO_QUANTITY < 25)
┌────────revenue─┐│ 44652567249651 │└────────────────┘
1 rows in set. Elapsed: 0.242 sec. Processed 91.01 million rows, 728.06 MB (375.91 million rows/s., 3.01 GB/s.)

描行数：91,010,000 大约9100万

耗时(秒)：0.242

查询列数：2

结果行数：1

Query 1.2

SELECT sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenueFROM lineorder_flatWHERE (toYYYYMM(LO_ORDERDATE) = 199401) AND ((LO_DISCOUNT >= 4) AND (LO_DISCOUNT <= 6)) AND ((LO_QUANTITY >= 26) AND (LO_QUANTITY <= 35))
┌───────revenue─┐│ 9624332170119 │└───────────────┘
1 rows in set. Elapsed: 0.040 sec. Processed 7.75 million rows, 61.96 MB (191.44 million rows/s., 1.53 GB/s.)

描行数：7,750,000 775万

耗时(秒)：0.040

查询列数：2

返回行数：1

Query 2.1

SELECT     sum(LO_REVENUE),    toYear(LO_ORDERDATE) AS year,    P_BRANDFROM lineorder_flatWHERE (P_CATEGORY = 'MFGR#12') AND (S_REGION = 'AMERICA')GROUP BY     year,    P_BRANDORDER BY     year ASC,    P_BRAND ASC
┌─sum(LO_REVENUE)─┬─year─┬─P_BRAND───┐│     64420005618 │ 1992 │ MFGR#121  ││     63389346096 │ 1992 │ MFGR#1210 ││     ........... │ .... │ ..........││     39679892915 │ 1998 │ MFGR#128  ││     35300513083 │ 1998 │ MFGR#129  │└─────────────────┴──────┴───────────┘
280 rows in set. Elapsed: 8.558 sec. Processed 600.04 million rows, 6.20 GB (70.11 million rows/s., 725.04 MB/s.)

扫描行数：600,040,000 大约6亿

耗时(秒)：8.558

查询列数：3

结果行数：280

Query 2.2

SELECT     sum(LO_REVENUE),    toYear(LO_ORDERDATE) AS year,    P_BRANDFROM lineorder_flatWHERE ((P_BRAND >= 'MFGR#2221') AND (P_BRAND <= 'MFGR#2228')) AND (S_REGION = 'ASIA')GROUP BY     year,    P_BRANDORDER BY     year ASC,    P_BRAND ASC
┌─sum(LO_REVENUE)─┬─year─┬─P_BRAND───┐│     66450349438 │ 1992 │ MFGR#2221 ││     65423264312 │ 1992 │ MFGR#2222 ││     ........... │ .... │ ......... ││     39907545239 │ 1998 │ MFGR#2227 ││     40654201840 │ 1998 │ MFGR#2228 │└─────────────────┴──────┴───────────┘
56 rows in set. Elapsed: 1.242 sec. Processed 600.04 million rows, 5.60 GB (482.97 million rows/s., 4.51 GB/s.)

扫描行数：600,040,000 大约6亿

耗时(秒)：1.242

查询列数：3

结果行数：56

Query 3.1

SELECT     C_NATION,    S_NATION,    toYear(LO_ORDERDATE) AS year,    sum(LO_REVENUE) AS revenueFROM lineorder_flatWHERE (C_REGION = 'ASIA') AND (S_REGION = 'ASIA') AND (year >= 1992) AND (year <= 1997)GROUP BY     C_NATION,    S_NATION,    yearORDER BY     year ASC,    revenue DESC
┌─C_NATION──┬─S_NATION──┬─year─┬──────revenue─┐│ INDIA     │ INDIA     │ 1992 │ 537778456208 ││ INDONESIA │ INDIA     │ 1992 │ 536684093041 ││ .....     │ .......   │ .... │ ............ ││ CHINA     │ CHINA     │ 1997 │ 525562838002 ││ JAPAN     │ VIETNAM   │ 1997 │ 525495763677 │└───────────┴───────────┴──────┴──────────────┘
150 rows in set. Elapsed: 3.533 sec. Processed 546.67 million rows, 5.48 GB (154.72 million rows/s., 1.55 GB/s.)

扫描行数：546,670,000 大约5亿4千多万

耗时(秒)：3.533

查询列数：4

结果行数：150

Query 3.2

SELECT     C_CITY,    S_CITY,    toYear(LO_ORDERDATE) AS year,    sum(LO_REVENUE) AS revenueFROM lineorder_flatWHERE (C_NATION = 'UNITED STATES') AND (S_NATION = 'UNITED STATES') AND (year >= 1992) AND (year <= 1997)GROUP BY     C_CITY,    S_CITY,    yearORDER BY     year ASC,    revenue DESC
┌─C_CITY─────┬─S_CITY─────┬─year─┬────revenue─┐│ UNITED ST6 │ UNITED ST6 │ 1992 │ 5694246807 ││ UNITED ST0 │ UNITED ST0 │ 1992 │ 5676049026 ││ .......... │ .......... │ .... │ .......... ││ UNITED ST9 │ UNITED ST9 │ 1997 │ 4836163349 ││ UNITED ST9 │ UNITED ST5 │ 1997 │ 4769919410 │└────────────┴────────────┴──────┴────────────┘
600 rows in set. Elapsed: 1.000 sec. Processed 546.67 million rows, 5.56 GB (546.59 million rows/s., 5.56 GB/s.)

查询列数：4

结果行数：600

Query 4.1

SELECT     toYear(LO_ORDERDATE) AS year,    C_NATION,    sum(LO_REVENUE - LO_SUPPLYCOST) AS profitFROM lineorder_flatWHERE (C_REGION = 'AMERICA') AND (S_REGION = 'AMERICA') AND ((P_MFGR = 'MFGR#1') OR (P_MFGR = 'MFGR#2'))GROUP BY     year,    C_NATIONORDER BY     year ASC,    C_NATION ASC
┌─year─┬─C_NATION──────┬────────profit─┐│ 1992 │ ARGENTINA     │ 1041983042066 ││ 1992 │ BRAZIL        │ 1031193572794 ││ .... │ ......        │  ............ ││ 1998 │ PERU          │  603980044827 ││ 1998 │ UNITED STATES │  605069471323 │└──────┴───────────────┴───────────────┘
35 rows in set. Elapsed: 5.066 sec. Processed 600.04 million rows, 8.41 GB (118.43 million rows/s., 1.66 GB/s.)

扫描行数：600,040,000 大约6亿

耗时(秒)：5.066

查询列数：4

结果行数：35

Query 4.2

SELECT     toYear(LO_ORDERDATE) AS year,    S_NATION,    P_CATEGORY,    sum(LO_REVENUE - LO_SUPPLYCOST) AS profitFROM lineorder_flatWHERE (C_REGION = 'AMERICA') AND (S_REGION = 'AMERICA') AND ((year = 1997) OR (year = 1998)) AND ((P_MFGR = 'MFGR#1') OR (P_MFGR = 'MFGR#2'))GROUP BY     year,    S_NATION,    P_CATEGORYORDER BY     year ASC,    S_NATION ASC,    P_CATEGORY ASC
┌─year─┬─S_NATION──────┬─P_CATEGORY─┬───────profit─┐│ 1997 │ ARGENTINA     │ MFGR#11    │ 102369950215 ││ 1997 │ ARGENTINA     │ MFGR#12    │ 103052774082 ││ .... │ .........     │ .......    │ ............ ││ 1998 │ UNITED STATES │ MFGR#24    │  60779388345 ││ 1998 │ UNITED STATES │ MFGR#25    │  60042710566 │└──────┴───────────────┴────────────┴──────────────┘
100 rows in set. Elapsed: 0.826 sec. Processed 144.42 million rows, 2.17 GB (174.78 million rows/s., 2.63 GB/s.)