References

https://github.com/intel-hadoop/HiBench/wiki/Getting-Started

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Introduction

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HiBench is an open sourced and Apache licensed big data benchmark suite that helps evaluate different big data frameworks in terms of speed, throughput and system resource utilizations.

It contains a set of Hadoop, Spark and streaming workloads, including Sort, WordCount, TeraSort, PageRank, Bayes, Kmeans, enhanced DFSIO, etc. It also contains several streaming workloads for Spark Streaming, Storm and Samza. 

Build

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NOTE: Following steps are tested on Ubuntu-16.04.

Prerequisites

apt install -y maven

Build

$ git clone https://github.com/intel-hadoop/HiBench.git
$        # get source
cd src
$ mvnHiBench
mvn -Dspark=2.2 -Dscala=2.11 clean package -D spark1.6.1 -D MR2 # Changed the spark version from default
$ cd conf
$ cp 99-user_defined_properties.conf.template 99-user_defined_properties.conf

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               # build all modules in HiBench
# if you just want to build for hadoop and spark
mvn -Phadoopbench -Psparkbench -Dspark=2.2 -Dscala=2.11 clean package

Run Benchmark

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Prerequisites

apt install -y bc python2.7 python-setuptools openssh-server
service start ssh

Hadoop

Setup

• A working hadoop cluster with HDFS, and YARN
  
  • To setup pseudo-distributed cluster, pls refer this link (Hadoop 2.x) or this link (Hadoop 3.0).
  • To setup multi-node cluster, please refer this link (Hadoop 2.x) or this link (Hadoop 3.0)
• Start up SSH service

You may encounter two problems:

'''Solution:''' Started spark (start-all and history server)

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1. Passphraseless ssh
   Hadoop requires a certain account to login to nodes without passphrase. This account should be setup in each node. To setup this account, please refer following cmds.

   Code Block
   language bash

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hibench.hadoop.home <Hadoop installation location>
hibench.spark.home <Spark installation location>
hibench.hdfs.master hdfs://<host>:8020
hibench.spark.master spark://<host>:7077
hibench.hadoop.version hadoop2 - # Change this in addition to the above configuration as hibench was not able to detect the hadoop version

Errors and Workarounds

Running WordCount Workload

$ workloads/wordcount/prepare/prepare.sh

_{'''Error 1:''' certain environment variables not found
workloads/wordcount/spark/scala/bin/run.sh}

Traceback (most recent call last):
File "/home/nbhoyar/HiBench/bin/functions/load-config.py", line 556, in <module>
load_config(conf_root, workload_root, workload_folder, patching_config)
File "/home/nbhoyar/HiBench/bin/functions/load-config.py", line 161, in load_config
generate_optional_value()
File "/home/nbhoyar/HiBench/bin/functions/load-config.py", line 374, in generate_optional_value
HibenchConf["hibench.hadoop.examples.test.jar"] = OneAndOnlyOneFile(HibenchConf['hibench.hadoop.mapreduce.home'] + "/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient*-tests.jar")
File "/home/nbhoyar/HiBench/bin/functions/load-config.py", line 114, in OneAndOnlyOneFile
raise Exception("Need to match one and only one file!")
Exception: Need to match one and only one file!
/home/nbhoyar/HiBench/bin/functions/workload-functions.sh: line 39: .: filename argument required
.: usage: . filename [arguments]

'''Solution:''' Modified line 358 and 374 in bin/functions/load-config.py to reflect the correct path of ODPi Hadoop's example jar file

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1. mkdir -p ~/.ssh rm -f ~/.ssh/id_rsa* # scan and save target fingerprints ssh-keyscan -t ecdsa-sha2-nistp256 -H ${HOSTNAME} > ~/.ssh/known_hosts ssh-keyscan -t ecdsa-sha2-nistp256 -H localhost >> ~/.ssh/known_hosts ssh-keyscan -t ecdsa-sha2-nistp256 -H 0.0.0.0 >> ~/.ssh/known_hosts # generate key ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys chmod 0600 ~/.ssh/authorized_keys

   
   

2. Hadoop user privilege
   It is recommended to run hadoop services as a non-root user. Usually a user, hdfs, is created to run HDFS and YARN services. If "root" is a must option, following cmds are required

   Code Block
   language bash theme Confluence
   USER=$(whoami) export HDFS_NAMENODE_USER=${USER} export HDFS_DATANODE_USER=${USER} export HDFS_SECONDARYNAMENODE_USER=${USER} export YARN_RESOURCEMANAGER_USER=${USER} export YARN_NODEMANAGER_USER=${USER}

   
   

Configure HiBench

HiBench requires Hadoop configuration info to setup and run test workloads. The default configuration is <HIBENCH_ROOT_DIR>/conf/hadoop.conf. A template configuration file can be used as start point.

cp conf/hadoop.conf.template conf/hadoop.conf

Usually these two fields should be modified to match Hadoop settings:

hibench.hadoop.home: point to hadoop root directory

hibench.hdfs.master: point to hdfs service uri. This uri can be found at <HADOOP_ROOT_DIR>/etc/hadoop/core-site.xml:fs.defaultFS.

A detail description for fields in hadoop.conf are listed as following:

Run Workload

HiBench's workload usually have two parts: prepare and run. For example, to run "wordcount",

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

The prepare.sh launches a Hadoop job to generate the input data on HDFS. The run.sh submits a Hadoop job to the cluster.

View Report

When benchmark is done, the report is outputed to <HIBENCH_ROOT_DIR>/report/hibench.report. It is a summarized workload report, including workload name, execution duration, data size, throughput per cluster, throughput per node.

The report directory also includes further information for debugging and tuning.

• <workload>/hadoop/bench.log: Raw logs on client side.
• <workload>/hadoop/monitor.html: System utilization monitor results.
• <workload>/hadoop/conf/<workload>.conf: Generated environment variable configurations for this workload.

Tuning Benchmark

• change input data size:
  • set hibench.scale.profile in conf/hibench.conf. Available values are tiny, small, large, huge, gigantic and bigdata.
• change parallelism

  • Change the below properties in conf/hibench.conf to control the parallelism.

    Property	Meaning
    hibench.default.map.parallelism	Mapper number in hadoop
    hibench.default.shuffle.parallelism	Reducer number in hadoop

    
    

Spark

Setup

• A working HDFS service
• A working YARN service, if Spark is tested in YARN mode
• Working Spark: Spark can be configured to work in either "standalone mode" or "YARN mode". ("Mesos mode" is not counted in as Mesos is not deployed when we run HiBench)
  
  • Standalone mode: it is the easiest to set up and will provide almost all the same features as the "YARN mode" if only Spark is running.
  • YARN mode:

• Start SSH service

Configure HiBench

Configure Hadoop

Hadoop is used to generate the input data of the workloads. Create and edit conf/hadoop.conf：

cp conf/hadoop.conf.template conf/hadoop.conf

Configure Spark

Create and edit conf/spark.conf：

cp conf/spark.conf.template conf/spark.conf

Set the below properties properly:

hibench.spark.home            The Spark installation location
hibench.spark.master          The Spark master, i.e. `spark://xxx:7077`, `yarn-client`

Run Workload

HiBench's workload usually have two parts: prepare and run. For example, to run "wordcount",

bin/workloads/micro/wordcount/prepare/prepare.sh
bin/workloads/micro/wordcount/spark/run.sh

The prepare.sh launches a Hadoop job to generate the input data on HDFS. The run.sh submits a Spark job to the cluster.

View Report

Same as "Hadoop benchmark", the report is outputed to <HIBENCH_ROOT_DIR>/report/hibench.report.

Meanwhile, detail information is generated for debugging and tuning.

• <workload>/spark/bench.log: Raw logs on client side.
• <workload>/spark/monitor.html: System utilization monitor results.
• <workload>/spark/conf/<workload>.conf: Generated environment variable configurations for this workload.
• <workload>/spark/conf/sparkbench/<workload>/sparkbench.conf: Generated configuration for this workloads, which is used for mapping to environment variable.
• <workload>/spark/conf/sparkbench/<workload>/spark.conf: Generated configuration for spark.

Tuning Benchmark

• change input data size:
  • set hibench.scale.profile in conf/hibench.conf. Available values are tiny, small, large, huge, gigantic and bigdata.

• change parallelism

  Property	Meaning
  hibench.default.map.parallelism	Partition number in Spark
  hibench.default.shuffle.parallelism	Shuffle partition number in Spark

  
  

• change Spark job properties

  Property	Meaning
  hibench.yarn.executor.num	Spark executor number in Yarn mode
  hibench.yarn.executor.cores	Spark executor cores in Yarn mode
  spark.executor.memory	Spark executor memory
  spark.driver.memory	Spark driver memory