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引言

HiBench是intel为评估各大数据框架而设计的测试集，它可以用来测试hadoop集群对于常见计算任务的性能。从普通的排序，字符串统计到机器学习，数据库操作，图像处理和搜索引擎，都能够涵盖。本文是HiBench中hadoopbench的快速配置指南。更加具体的使用说明可以参考官方wiki。

软件依赖

HiBench需要java环境，以及Maven管理。

安装java运行环境

安装JDK&JRE

sudo apt-get install openjdk-8-jre openjdk-8-jdk

建议装version 8不要装9。安装完成后，默认路径是/usr/lib/jvm/java-8-openjdk-amd64，如果不一致请搜索到正确路径。

添加环境变量

cd
vim .bashrc

添加如下jave的PATH：

# JAVA PATH
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64

更新环境变量

source .bashrc

测试java环境

java -version

看到相应版本信息输出即表明配置正确：

hadoop@hadoop-master:~$ java -version
openjdk version "1.8.0_121"
OpenJDK Runtime Environment (build 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13)
OpenJDK 64-Bit Server VM (build 25.121-b13, mixed mode)

安装Maven

下载Maven包

wget http://apache.fayea.com/maven/maven-3/3.5.0/binaries/apache-maven-3.5.0-bin.zip

解压缩

unzip apache-maven-3.5.0-bin.zip -d /YOUR/PATH/TO/RESTORE

笔者解压位置为/usr/local/。

添加环境变量

cd
vim .bashrc

添加如下Maven的PATH：

# set maven environment
export M3_HOME=/usr/local/apache-maven-3.5.0
export PATH=$M3_HOME/bin:$PATH

更新环境变量

source .bashrc

测试Maven环境

mvn -v

看到相应版本信息输出即表明配置正确：

hadoop@hadoop-slave1:~$ mvn -v
Apache Maven 3.5.0 (ff8f5e7444045639af65f6095c62210b5713f426; 2017-04-04T03:39:06+08:00)
Maven home: /usr/local/apache-maven-3.5.0
Java version: 1.8.0_121, vendor: Oracle Corporation
Java home: /usr/lib/jvm/java-8-openjdk-amd64/jre
Default locale: en_US, platform encoding: ANSI_X3.4-1968
OS name: "linux", version: "4.4.0-53-generic", arch: "amd64", family: "unix"

下载HiBench

git clone https://github.com/intel-hadoop/HiBench.git比较慢，建议直接网页下载zip包，然后解压到理想的目录下。

安装Hibench

切到HiBench下，执行对应的安装操作，可以选择自己想要安装的模块。以安装hadoop框架下用于测试sql的模块为例：

mvn -Phadoopbench -Dmodules -Psql -Dscala=2.11 clean package 

更多安装命令可见https://github.com/intel-hadoop/HiBench/blob/master/docs/build-hibench.md。
鉴于网络因素，建议一个模块一个模块的安装，某些模块的安装可能会非常耗时。

配置HiBench

主要配置conf/hadoop.conf及conf/hibench.conf两个文件。

hadoop.conf

笔者的hadoop是安装在/usr/local/hadoop-2.8.0目录下的，并且以单机localhost:9000(真分布式时使用真实的IP:port)为例：

# Hadoop home
hibench.hadoop.home             /usr/local/hadoop-2.8.0

# The path of hadoop executable
hibench.hadoop.executable       /usr/local/hadoop-2.8.0/bin/hadoop

# Hadoop configraution directory
hibench.hadoop.configure.dir    /usr/local/hadoop-2.8.0/etc/hadoop

# The root HDFS path to store HiBench data
hibench.hdfs.master             hdfs://localhost:9000/user/hadoop/HiBench


# Hadoop release provider. Supported value: apache, cdh5, hdp
hibench.hadoop.release          apache

hibench.conf

 The definition of these profiles can be found in the workload's conf file i.e. conf/workloads/micro/wordcount.conf
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

主要设置测试集运行时的数据量和并发度。

运行HiBench

安装完成后，可以运行其中的测试集。首先要启动hadoop：

start-dfs.sh;
start-yarn.sh;

关于hadoop的快速配置教程请见Hadoop真分布式集群最速搭建攻略
。

以运行Hadoop框架下micro集的sort为例：

 bin/workloads/micro/sort/prepare/prepare.sh
 bin/workloads/micro/sort/hadoop/run.sh

等待读条MapReduce完毕，可以在report/sort/hadoop/bench.log处查看具体的运行结果和日志。运行结果大致是这样的：

17/05/11 15:16:41 INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:8032
Running on 1 nodes to sort from hdfs://localhost:9000/user/hadoop/HiBench/HiBench/Sort/Input into hdfs://localhost:9000/user/hadoop/HiBench/HiBench/Sort/Output with 8 reduces.
Job started: Thu May 11 15:16:43 CST 2017
17/05/11 15:16:43 INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:8032
17/05/11 15:16:56 INFO input.FileInputFormat: Total input files to process : 8
17/05/11 15:17:11 INFO mapreduce.JobSubmitter: number of splits:24
17/05/11 15:17:15 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494484569605_0006
...
17/05/11 15:17:27 INFO mapreduce.Job:  map 0% reduce 0%
17/05/11 15:17:43 INFO mapreduce.Job:  map 25% reduce 0%
...
17/05/11 15:22:40 INFO mapreduce.Job:  map 100% reduce 98%
17/05/11 15:22:48 INFO mapreduce.Job:  map 100% reduce 100%
7/05/11 15:23:02 INFO mapreduce.Job: Job job_1494484569605_0006 completed successfully
17/05/11 15:23:02 INFO mapreduce.Job: Counters: 51
        File System Counters
        ...
        Job Counters
        ...
        Map-Reduce Framework
        ...
        Shuffle Errors
        ...
        File Input Format Counters
        ...
        File Output Format Counters
        ...
Job ended: Thu May 11 15:23:02 CST 2017
The job took 378 seconds.

总结

本文是hadoop常用测试集HiBench快速配置指南，希望对大家有所帮助。任何问题欢迎在下方留言，笔者会及时回答。