一、测试结果
测试结论1:2台64核256g阿里云服务器组成的2节点集群,hadoop3.2.2 作为分布式存储,Cloudwave4.0在 SSB30g 国际标准测试集下,整体性能优于Starrocks3.0近0.4倍。
数据库 | 数据集 | 响应时间(ms) | CPU 最大占用率 | 存储压缩比 | 数据导入时间 |
Cloudwave4.0 | ssb30 | 748 | 10.8%(696%/6400%) | 56.7%(10.2g/18g) | 82秒 |
Starrocks3.0 | ssb30 | 1057 | 33.3%(2131%/6400%) | 46.1%(8.3g/18g) | 95秒 |
测试结论2:2台64核256g阿里云服务器组成的2节点集群,hadoop3.2.2 作为分布式存储,Cloudwave4.0在 SSB100g 国际标准测试集下,整体性能优于Starrocks3.0近0.9倍。
数据库 | 数据集 | 响应时间(ms) | CPU 最大占用率 | 存储压缩比 | 数据导入时间 |
Cloudwave4.0 | ssb100 | 1128 | 44.3%(2834%/6400%) | 58.6%(34.6g/59g) | 9分钟24秒 |
Starrocks3.0 | ssb100 | 2191 | 53.7%(3437%/6400%) | 49.15%(29g/59g) | 6分钟 |
二、评测环境
- 硬件环境:2台 64核256g 云服务器(组成2节点的集群),essd pl1 高效云盘
- 软件环境:jdk19(Cloudwave4.0官方推荐版本,官方基于jdk19版本里头的的vector api,实现全面向量化引擎)、jdk8(starrocks安装推荐jdk版本,主要用于fe,亦可少踩坑)、mysql8(作为starrocks的客户端)、hadoop 3.2.2(作为cloudwave 和 starrocks 共同的分布式存储,副本数=2)
- 软件版本:Cloudwave 4.0(最新版在2023年5月份发版),Starrocks 3.0(最新版在2023年4月份发版)
- 评测数据集:ssb30,ssb100
硬件环境
jdk版本
mysql版本
hadoop版本
Cloudwave版本
Starrocks版本
三、评测方法
- 执行19轮测试脚本,每轮执行13条标准测试sql,去除第1轮的测试数据(由于IO原因,第1次查询两边的性能均受IO影响,本测试主要测数据库引擎的算法在同等计算资源的条件下的优劣,因此去除第一轮测试数据),将余下的18轮测试数据做平均,获得每条sql的平均耗时
- 观察最大CPU占用
- 观察存储压缩比
- 观察数据加载时间
四、开始测试
- 启动hadoop
两节点的hadoop启动完毕
hdfs namenode -format start-dfs.sh jps
[cloudwave]启动 Cloudwave4.0
./start-all-server.sh jps
[cloudwave]上传 ssb30 数据
hdfs dfs -put ssb30 /cloudwave/uploads/
[cloudwave]加载数据
- 执行数据导入命令
loaddata ssb30
./cplus_go.bin -s 'loaddata ssb30'
- [cloudwave]查看数据导入情况
- 可以看到30g的数据,82s就导入完成了
- 通过 hdfs 命令,可以看到cloudwave做了数据压缩,ssb30数据的原始大小是18G,导入cloudwave数据库之后,压缩到了10.2g(图中的20.3G 表示hdfs两个数据副本的总大小)
- [cloudwave]开始测试
- 执行测试脚本
./test_ssb.sh
,七镜观察到cloudwave 的2节点集群测ssb30 CPU最大占用是696%/6400%
- 执行分析脚本
./analysis.sh cloudwave "$(ls n*txt)" +
- [cloudwave]按上述步骤测试ssb100
- 上传数据到hdfs
- 查看上传的数据
- 执行导入数据命令
- 9分钟导入完成
- ssb100数据的原始大小是59G,导入cloudwave数据库之后,压缩到了34.6g
- cloudwave 的2节点集群测ssb100 CPU最大占用是2834%/6400%
- 分析测试结果
- [starrocks] 启动 starrocks3.0 fe
./start_fe.sh --daemon
- [starrocks] 添加starrocks3.0 be
mysql -uroot -h127.0.0.1 -P9030 ALTER SYSTEM ADD BACKEND "172.17.161.33:9050"; ALTER SYSTEM ADD BACKEND "172.17.161.30:9050";
- [starrocks] 启动 starrocks 3.0 be
./start_be.sh --daemon ps -ef | grep starrocks
- [starrocks] 验证集群状态,两个节点的 Alive=true 即可。
- [starrocks] 创建表
- [starrocks] 开始导入数据,ssb30导入时间是95s
date && ./bin/stream_load.sh data_dir/ssb30 && date
- [starrocks] 查看ssb30 压缩比,ssb30数据的原始大小是18G,导入starrocks数据库之后,压缩到了8.3g
- [starrocks] 开始测试
- 执行测试脚本./test_ssb.sh,七镜观察到 starrocks 的2节点集群测ssb30 CPU最大占用是2131%/6400%
- 执行分析脚本./analysis.sh starrocks "$(ls n*txt)" +
执行分析脚本./analysis.sh starrocks "$(ls n*txt)" +
- [starrocks]按上述步骤测试ssb100
- 创建表
导数据,6分钟导入完成ssb100g数据
- 查看压缩比,ssb100数据的原始大小是59G,导入starrocks数据库之后,压缩到了29g
- 执行测试,starrocks 的2节点集群测ssb100 CPU最大占用是3437%/6400%
- 分析测试结果
五、附加
- Cloudwave 测试脚本
#!/bin/bash # Program: # test ssb # History: # 2023/03/17 junfenghe.cloud@qq.com version:0.0.1 rm -rf ./n*txt for ((i=1; i<20; i++)) do cat sql_ssb.sql |./cplus.sh > n${i}.txt done
- Starrocks 测试脚本
#!/bin/bash # Program: # test ssb # History: # 2023/03/17 junfenghe.cloud@qq.com version:0.0.1 rm -rf ./n*txt for ((i=1; i<20; i++)) do cat sql_ssb.sql | mysql -uroot -P 9030 -h 127.0.0.1 -v -vv -vvv >n${i}.txt done
分析脚本
#!/bin/bash #Program: # analysis cloudwave/starrocks logs of base compute #History: #2023/02/20 junfenghe.cloud@qq.com version:0.0.1 path=/bin:/sbin:/usr/bin:/usr/sbin:/usr/local/sbin:/usr/local/bin:~/bin export path suff="(s)#####" if [ -z "${1}" ] then echo "Please input database'name" exit -1 fi if [ -z "$2" ] then echo "Please input times of scanner" exit -f fi if [ -n "${3}" ] then suff=${3} fi for current in ${2} do result_time="" if [ "${1}" == "starrocks" ] then for time in $( cat ${current} | grep sec | awk -F '(' '{print $2}' | awk -F ' ' '{print $1}' ) do result_time="${result_time}${time}${suff}" done elif [ "${1}" == "cloudwave" ] then for time in $( cat ${current} | grep Elapsed | awk '{print $2}'| sed 's/:/*60+/g'| sed 's/+00\*60//g ; s/+0\*60//g ; s/^0\*60+//g' ) do result_time="${result_time}${time}${suff}" done fi echo ${result_time%${suff}*} done exit 0
sql_ssb.sql
use ssb100; select sum(lo_revenue) as revenue from lineorder,dates where lo_orderdate = d_datekey and d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25; select sum(lo_revenue) as revenue from lineorder,dates where lo_orderdate = d_datekey and d_yearmonthnum = 199401 and lo_discount between 4 and 6 and lo_quantity between 26 and 35; select sum(lo_revenue) as revenue from lineorder,dates where lo_orderdate = d_datekey and d_weeknuminyear = 6 and d_year = 1994 and lo_discount between 5 and 7 and lo_quantity between 26 and 35; select sum(lo_revenue) as lo_revenue, d_year, p_brand from lineorder ,dates,part,supplier where lo_orderdate = d_datekey and lo_partkey = p_partkey and lo_suppkey = s_suppkey and p_category = 'MFGR#12' and s_region = 'AMERICA' group by d_year, p_brand order by d_year, p_brand; select sum(lo_revenue) as lo_revenue, d_year, p_brand from lineorder,dates,part,supplier where lo_orderdate = d_datekey and lo_partkey = p_partkey and lo_suppkey = s_suppkey and p_brand between 'MFGR#2221' and 'MFGR#2228' and s_region = 'ASIA' group by d_year, p_brand order by d_year, p_brand; select sum(lo_revenue) as lo_revenue, d_year, p_brand from lineorder,dates,part,supplier where lo_orderdate = d_datekey and lo_partkey = p_partkey and lo_suppkey = s_suppkey and p_brand = 'MFGR#2239' and s_region = 'EUROPE' group by d_year, p_brand order by d_year, p_brand; select c_nation, s_nation, d_year, sum(lo_revenue) as lo_revenue from lineorder,dates,customer,supplier where lo_orderdate = d_datekey and lo_custkey = c_custkey and lo_suppkey = s_suppkey and 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; select c_city, s_city, d_year, sum(lo_revenue) as lo_revenue from lineorder,dates,customer,supplier where lo_orderdate = d_datekey and lo_custkey = c_custkey and lo_suppkey = s_suppkey and 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; select c_city, s_city, d_year, sum(lo_revenue) as lo_revenue from lineorder,dates,customer,supplier where lo_orderdate = d_datekey and lo_custkey = c_custkey and lo_suppkey = s_suppkey and (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; select c_city, s_city, d_year, sum(lo_revenue) as lo_revenue from lineorder,dates,customer,supplier where lo_orderdate = d_datekey and lo_custkey = c_custkey and lo_suppkey = s_suppkey and (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; select d_year, c_nation, sum(lo_revenue) - sum(lo_supplycost) as profit from lineorder,dates,customer,supplier,part where lo_orderdate = d_datekey and lo_custkey = c_custkey and lo_suppkey = s_suppkey and lo_partkey = p_partkey and 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; select d_year, s_nation, p_category, sum(lo_revenue) - sum(lo_supplycost) as profit from lineorder,dates,customer,supplier,part where lo_orderdate = d_datekey and lo_custkey = c_custkey and lo_suppkey = s_suppkey and lo_partkey = p_partkey and 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; select d_year, s_city, p_brand, sum(lo_revenue) - sum(lo_supplycost) as profit from lineorder,dates,customer,supplier,part where lo_orderdate = d_datekey and lo_custkey = c_custkey and lo_suppkey = s_suppkey and lo_partkey = p_partkey and 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;
七镜还将带来Cloudwave 4.0 集群版 VS Starrocks 3.0 集群版 在 1T SSB数据集上的评测。