概述
项目地址: Intel-hadoop/HiBench
Hibench 是 Intel 开源的大数据基准测试工具,可以评估不同大数据框架的速度,吞吐量和系统资源利用率。包括 Sort, WordCount, TeraSort, Sleep, SQL, PageRank, Nutch indexing, Bayes, Kmeans, NWeight 和 enhanced DFSIO等,还支持流任务的基准测试,例如 Spark Streaming, Flink, Storm 和 Gearpump。
环境准备
安装
//获取源码
wget https://github.com/intel-hadoop/HiBench/archive/HiBench-7.0.zip
//编译spark相关模块
mvn -Phadoopbench -Psparkbench -Dspark=2.2 -Dscala=2.11 clean package
// 安装 bc 用于生成 report 信息
yum install bc
配置
conf/spark.conf
根据文件提示配置相关的路径和参数即可
conf/hadoop.conf
# Hadoop home
hibench.hadoop.home /opt/cloudera/parcels/CDH/lib/hadoop
# The path of hadoop executable
hibench.hadoop.executable /opt/cloudera/parcels/CDH/bin/hadoop
# Hadoop configraution directory
hibench.hadoop.configure.dir /etc/hadoop/conf
# hdfs namenode 地址或者 nameservice
hibench.hdfs.master hdfs://localhost:8020
# Hadoop release provider. Supported value: apache, cdh5, hdp
hibench.hadoop.release cdh5
conf/hibench.conf
# 生成测试数据的规模,默认有 tiny, small, large, huge, gigantic and bigdata,具体可以在对应benchmark的conf里修改
hibench.scale.profile tiny
# Mapper number in hadoop, partition number in Spark
hibench.default.map.parallelism 8
# Reducer nubmer in hadoop, shuffle partition number in Spark
hibench.default.shuffle.parallelism 8
运行
进行terasort 测试
配置
可以自定义数据规模
conf/workloads/micro/terasort.conf
#datagen
hibench.terasort.tiny.datasize 32000
hibench.terasort.small.datasize 3200000
hibench.terasort.large.datasize 32000000
hibench.terasort.huge.datasize 320000000
hibench.terasort.gigantic.datasize 3200000000
hibench.terasort.bigdata.datasize 6000000000
# 增加自定义的数据量
hibench.terasort.myscale.datasize 5242880
hibench.workload.datasize ${hibench.terasort.${hibench.scale.profile}.datasize}
# export for shell script
hibench.workload.input ${hibench.hdfs.data.dir}/Terasort/Input
hibench.workload.output ${hibench.hdfs.data.dir}/Terasort/Output%
在 hibench.conf 中 设置 hibench.scale.profile 为 myscale。
bin/workloads/micro/terasort/prepare/prepare.sh
bin/workloads/micro/terasort/spark/run.sh
查看报告
report/hibench.report
68DDBA74-3397-467E-8C50-8CA65ABEC4DE.png
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