http://www.talkwithtrend.com/Question/177983-1247453
一些测试的描述如下内容最为详细,供你参考:
测试对于验证系统的正确性、分析系统的性能来说非常重要,但往往容易被我们所忽视。为了能对系统有更全面的了解、能找到系统的瓶颈所在、能对系统性能做更好的改进,打算先从测试入手,学习Hadoop几种主要的测试手段。本文将分成两部分:第一部分记录如何使用Hadoop自带的测试工具进行测试;第二部分记录Intel开放的Hadoop Benchmark Suit: HiBench的安装及使用。
1. Hadoop基准测试
Hadoop自带了几个基准测试,被打包在几个jar包中,如hadoop-test.jar和hadoop-examples.jar,在Hadoop环境中可以很方便地运行测试。本文测试使用的Hadoop版本是cloudera的hadoop-0.20.2-cdh3u3。
在测试前,先设置好环境变量:
$ export $HADOOP_HOME=/home/hadoop/hadoop $ export $PATH=$PATH:$HADOOP_HOME/bin
使用以下命令就可以调用jar包中的类:
$ hadoop jar $HADOOP_HOME/xxx.jar
(1). Hadoop Test
当不带参数调用hadoop-test-0.20.2-cdh3u3.jar时,会列出所有的测试程序:
$ hadoop jar $HADOOP_HOME/hadoop-test-0.20.2-cdh3u3.jar An example program must be given as the first argument. Valid program names are: DFSCIOTest: Distributed i/o benchmark of libhdfs. DistributedFSCheck: Distributed checkup of the file system consistency. MRReliabilityTest: A program that tests the reliability of the MR framework by injecting faults/failures TestDFSIO: Distributed i/o benchmark. dfsthroughput: measure hdfs throughput filebench: Benchmark SequenceFile(Input|Output)Format (block,record compressed and uncompressed), Text(Input|Output)Format (compressed and uncompressed) loadgen: Generic map/reduce load generator mapredtest: A map/reduce test check. minicluster: Single process HDFS and MR cluster. mrbench: A map/reduce benchmark that can create many small jobs nnbench: A benchmark that stresses the namenode. testarrayfile: A test for flat files of binary key/value pairs. testbigmapoutput: A map/reduce program that works on a very big non-splittable file and does identity map/reduce testfilesystem: A test for FileSystem read/write. testipc: A test for ipc. testmapredsort: A map/reduce program that validates the map-reduce framework's sort. testrpc: A test for rpc. testsequencefile: A test for flat files of binary key value pairs. testsequencefileinputformat: A test for sequence file input format. testsetfile: A test for flat files of binary key/value pairs. testtextinputformat: A test for text input format. threadedmapbench: A map/reduce benchmark that compares the performance of maps with multiple spills over maps with 1 spill
这些程序从多个角度对Hadoop进行测试,TestDFSIO、mrbench和nnbench是三个广泛被使用的测试。
TestDFSIO
TestDFSIO用于测试HDFS的IO性能,使用一个MapReduce作业来并发地执行读写操作,每个map任务用于读或写每个文件,map的输出用于收集与处理文件相关的统计信息,reduce用于累积统计信息,并产生summary。TestDFSIO的用法如下:
TestDFSIO.0.0.6 Usage: TestDFSIO [genericOptions] -read | -write | -append | -clean [-nrFiles N] [-fileSize Size[B|KB|MB|GB|TB]] [-resFile resultFileName] [-bufferSize Bytes] [-rootDir]
以下的例子将往HDFS中写入10个1000MB的文件:
$ hadoop jar $HADOOP_HOME/hadoop-test-0.20.2-cdh3u3.jar TestDFSIO -write -nrFiles 10 -fileSize 1000
结果将会写到一个本地文件TestDFSIO_results.log:
----- TestDFSIO ----- : write Date & time: Mon Dec 10 11:11:15 CST 2012 Number of files: 10 Total MBytes processed: 10000.0 Throughput mb/sec: 3.5158047729862436 Average IO rate mb/sec: 3.5290374755859375 IO rate std deviation: 0.22884063705950305 Test exec time sec: 316.615
以下的例子将从HDFS中读取10个1000MB的文件:
$ hadoop jar $HADOOP_HOME/hadoop-test-0.20.2-cdh3u3.jar TestDFSIO -read -nrFiles 10 -fileSize 1000
结果将会写到一个本地文件TestDFSIO_results.log:
----- TestDFSIO ----- : read Date & time: Mon Dec 10 11:21:17 CST 2012 Number of files: 10 Total MBytes processed: 10000.0 Throughput mb/sec: 255.8002711482874 Average IO rate mb/sec: 257.1685791015625 IO rate std deviation: 19.514058659935184 Test exec time sec: 18.459
使用以下命令删除测试数据:
$ hadoop jar $HADOOP_HOME/hadoop-test-0.20.2-cdh3u3.jar TestDFSIO -clean
nnbench
nnbench用于测试NameNode的负载,它会生成很多与HDFS相关的请求,给NameNode施加较大的压力。这个测试能在HDFS上模拟创建、读取、重命名和删除文件等操作。nnbench的用法如下:
NameNode Benchmark 0.4 Usage: nnbench <options> Options: -operation <Available operations are create_write open_read rename delete. This option is mandatory> * NOTE: The open_read, rename and delete operations assume that the files they operate on, are already available. The create_write operation must be run before running the other operations. -maps <number of maps. default is 1. This is not mandatory> -reduces <number of reduces. default is 1. This is not mandatory> -startTime <time to start, given in seconds from the epoch. Make sure this is far enough into the future, so all maps (operations) will start at the same time>. default is launch time + 2 mins. This is not mandatory -blockSize <Block size in bytes. default is 1. This is not mandatory> -bytesToWrite <Bytes to write. default is 0. This is not mandatory> -bytesPerChecksum <Bytes per checksum for the files. default is 1. This is not mandatory> -numberOfFiles <number of files to create. default is 1. This is not mandatory> -replicationFactorPerFile <Replication factor for the files. default is 1. This is not mandatory> -baseDir <base DFS path. default is /becnhmarks/NNBench. This is not mandatory> -readFileAfterOpen <true or false. if true, it reads the file and reports the average time to read. This is valid with the open_read operation. default is false. This is not mandatory> -help: Display the help statement
以下例子使用12个mapper和6个reducer来创建1000个文件:
$ hadoop jar $HADOOP_HOME/hadoop-test-0.20.2-cdh3u3.jar nnbench -operation create_write -maps 12 -reduces 6 -blockSize 1 -bytesToWrite 0 -numberOfFiles 1000 -replicationFactorPerFile 3 -readFileAfterOpen true -baseDir /benchmarks/NNBench-`hostname -s`
mrbench
mrbench会多次重复执行一个小作业,用于检查在机群上小作业的运行是否可重复以及运行是否高效。mrbench的用法如下:
MRBenchmark.0.0.2 Usage: mrbench [-baseDir <base DFS path for output/input, default is /benchmarks/MRBench>] [-jar <local path to job jar file containing Mapper and Reducer implementations, default is current jar file>] [-numRuns <number of times to run the job, default is 1>] [-maps <number of maps for each run, default is 2>] [-reduces <number of reduces for each run, default is 1>] [-inputLines <number of input lines to generate, default is 1>] [-inputType <type of input to generate, one of ascending (default), descending, random>] [-verbose]
以下例子会运行一个小作业50次:
$ hadoop jar $HADOOP_HOME/hadoop-test-0.20.2-cdh3u3.jar mrbench -numRuns 50
运行结果如下所示:
DataLines Maps Reduces AvgTime (milliseconds) 1 2 1 14237
以上结果表示平均作业完成时间是14秒。
(2). Hadoop Examples
除了上文提到的测试,Hadoop还自带了一些例子,比如WordCount和TeraSort,这些例子在hadoop-examples-0.20.2-cdh3u3.jar中。执行以下命令会列出所有的示例程序:
$ hadoop jar $HADOOP_HOME/hadoop-examples-0.20.2-cdh3u3.jar An example program must be given as the first argument. Valid program names are: aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files. aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files. dbcount: An example job that count the pageview counts from a database. grep: A map/reduce program that counts the matches of a regex in the input. join: A job that effects a join over sorted, equally partitioned datasets multifilewc: A job that counts words from several files. pentomino: A map/reduce tile laying program to find solutions to pentomino problems. pi: A map/reduce program that estimates Pi using monte-carlo method. randomtextwriter: A map/reduce program that writes 10GB of random textual data per node. randomwriter: A map/reduce program that writes 10GB of random data per node. secondarysort: An example defining a secondary sort to the reduce. sleep: A job that sleeps at each map and reduce task. sort: A map/reduce program that sorts the data written by the random writer. sudoku: A sudoku solver. teragen: Generate data for the terasort terasort: Run the terasort teravalidate: Checking results of terasort wordcount: A map/reduce program that counts the words in the input files.
WordCount在 Running Hadoop On CentOS (Single-Node Cluster) 一文中已有介绍,这里就不再赘述。
TeraSort
一个完整的TeraSort测试需要按以下三步执行:
用TeraGen生成随机数据对输入数据运行TeraSort用TeraValidate验证排好序的输出数据
并不需要在每次测试时都生成输入数据,生成一次数据之后,每次测试可以跳过第一步。
TeraGen的用法如下:
$ hadoop jar hadoop-*examples*.jar teragen <number of 100-byte rows> <output dir>
以下命令运行TeraGen生成1GB的输入数据,并输出到目录/examples/terasort-input:
$ hadoop jar $HADOOP_HOME/hadoop-examples-0.20.2-cdh3u3.jar teragen 10000000 /examples/terasort-input
TeraGen产生的数据每行的格式如下:
<10 bytes key><10 bytes rowid><78 bytes filler>rn
其中:
key是一些随机字符,每个字符的ASCII码取值范围为[32, 126]rowid是一个整数,右对齐filler由7组字符组成,每组有10个字符(最后一组8个),字符从’A’到’Z’依次取值
以下命令运行TeraSort对数据进行排序,并将结果输出到目录/examples/terasort-output:
$ hadoop jar $HADOOP_HOME/hadoop-examples-0.20.2-cdh3u3.jar terasort /examples/terasort-input /examples/terasort-output
以下命令运行TeraValidate来验证TeraSort输出的数据是否有序,如果检测到问题,将乱序的key输出到目录/examples/terasort-validate
$ hadoop jar $HADOOP_HOME/hadoop-examples-0.20.2-cdh3u3.jar teravalidate /examples/terasort-output /examples/terasort-validate
(3). Hadoop Gridmix2
Gridmix是Hadoop自带的基准测试程序,是对其它几个基准测试程序的进一步封装,包括产生数据、提交作业、统计完成时间等功能模块。Gridmix自带了各种类型的作业,分别为streamSort、javaSort、combiner、monsterQuery、webdataScan和webdataSort。
$ cd $HADOOP_HOME/src/benchmarks/gridmix2 $ ant $ cp build/gridmix.jar .
修改环境变量
修改gridmix-env-2文件:
export HADOOP_INSTALL_HOME=/home/jeoygin export HADOOP_VERSION=hadoop-0.20.2-cdh3u3 export HADOOP_HOME=${HADOOP_INSTALL_HOME}/${HADOOP_VERSION} export HADOOP_CONF_DIR=${HADOOP_HOME}/conf export USE_REAL_DATASET= export APP_JAR=${HADOOP_HOME}/hadoop-test-0.20.2-cdh3u3.jar export EXAMPLE_JAR=${HADOOP_HOME}/hadoop-examples-0.20.2-cdh3u3.jar export STREAMING_JAR=${HADOOP_HOME}/contrib/streaming/hadoop-streaming-0.20.2-cdh3u3.jar
如果USE_REAL_DATASET的值为TRUE的话,将使用500GB压缩数据(等价于2TB非压缩数据),如果留空将使用500MB压缩数据(等价于2GB非压缩数据)。
修改配置信息
配置信息在gridmix_config.xml文件中。gridmix中,每种作业有大中小三种类型:小作业只有3个输入文件(即3个map);中作业的输入文件是与正则表达式{part-0000,part-0001,part-000*2}匹配的文件;大作业会处理处有数据。
产生数据
$ chmod +x generateGridmix2data.sh $ ./generateGridmix2data.sh
generateGridmix2data.sh脚本会运行一个作业,在HDFS的目录/gridmix/data中产生输入数据。
运行
$ chmod +x rungridmix_2 $ ./rungridmix_2
运行后,会创建_start.out文件来记录开始时间,结束后,创建_end.out文件来记录完成时间。
(4). 查看任务统计信息
Hadoop提供非常方便的方式来获取一个任务的统计信息,使用以下命令即可作到:
$ hadoop job -history all <job output directory>
这个命令会分析任务的两个历史文件(这两个文件存储在<job output directory>/_logs/history目录中)并计算任务的统计信息。
2. HiBench
HiBench是Intel开放的一个Hadoop Benchmark Suit,包含9个典型的Hadoop负载(Micro benchmarks、HDFS benchmarks、web search benchmarks、machine learning benchmarks和data analytics benchmarks),主页是: https://github.com/intel-hadoop/hibench 。
HiBench为大多数负载提供是否启用压缩的选项,默认的compression codec是zlib。
Micro Benchmarks:
Sort (sort):使用Hadoop RandomTextWriter生成数据,并对数据进行排序WordCount (wordcount):统计输入数据中每个单词的出现次数,输入数据使用Hadoop RandomTextWriter生成TeraSort (terasort):这是由微软的数据库大牛Jim Gray(2007年失踪)创建的标准benchmark,输入数据由Hadoop TeraGen产生
HDFS Benchmarks:
增强的DFSIO (dfsioe):通过产生大量同时执行读写请求的任务来测试Hadoop机群的HDFS吞吐量
Web Search Benchmarks:
Nutch indexing (nutchindexing):大规模搜索引擎索引是MapReduce的一个重要应用,这个负载测试Nutch(Apache的一个开源搜索引擎)的索引子系统,使用自动生成的Web数据,Web数据中的链接和单词符合Zipfian分布PageRank (pagerank):这个负载包含一种在Hadoop上的PageRank算法实现,使用自动生成的Web数据,Web数据中的链接符合Zipfian分布
Machine Learning Benchmarks:
Mahout Bayesian classification (bayes):大规模机器学习也是MapReduce的一个重要应用,这个负载测试Mahout 0.7(Apache的一个开源机器学习库)中的Naive Bayesian训练器,输入数据是自动生成的文档,文档中的单词符合Zipfian分布Mahout K-means clustering (kmeans):这个负载测试Mahout 0.7中的K-means聚类算法,输入数据集由基于均匀分布和高斯分布的GenKMeansDataset产生
Data Analytics Benchmarks:
Hive Query Benchmarks (hivebench):这个负载的开发基于SIGMOD 09的一篇论文“A Comparison of Approaches to Large-Scale Data Analysis”和HIVE-396,包含执行典型OLAP查询的Hive查询(Aggregation and Join),使用自动生成的Web数据,Web数据中的链接符合Zipfian分布
下文将${HIBENCH_HOME}定义为HiBench的解压缩目录。
(1). 安装与配置
建立环境:
HiBench-2.2:从https://github.com/intel-hadoop/HiBench/zipball/HiBench-2.2下载Hadoop:在运行任何负载之前,请确保Hadoop环境能正常运行,所有负载在Cloudera Distribution of Hadoop 3 update 4 (cdh3u4)和Hadoop 1.0.3上测试通过Hive:如果要测试hivebench,请确保已正确建立了Hive环境
配置所有负载:
需要在${HIBENCH_HOME}/bin/hibench-config.sh文件中设置一些全局的环境变量。
$ unzip HiBench-2.2.zip $ cd HiBench-2.2 $ vim bin/hibench-config.sh HADOOP_HOME <The Hadoop installation location> HADOOP_CONF_DIR <The hadoop configuration DIR, default is $HADOOP_HOME/conf> COMPRESS_GLOBAL <Whether to enable the in/out compression for all workloads, 0 is disable, 1 is enable> COMPRESS_CODEC_GLOBAL <The default codec used for in/out data compression>
配置单个负载:
在每个负载目录下,可以修改conf/configure.sh这个文件,设置负载运行的参数。
同步每个节点的时间
(2). 运行
同时运行几个负载:
修改${HIBENCH_HOME}/conf/benchmarks.lst文件,该文件定义了将要运行的负载,每行指定一个负载,在任意一行前可以使用#跳过该行运行${HIBENCH_HOME}/bin/run-all.sh脚本
单独运行每个负载:
可以单独运行每个负载,通常,在每个负载目录下有三个不同的文件:
conf/configure.sh 包含所有参数的配置文件,可以设置数据大小及测试选项等 bin/prepare*.sh 生成或拷贝作业输入数据到HDFS bin/run*.sh 运行benchmark
(3). 小结
HiBench覆盖了一些广被使用的Hadoop Benchmark,如果看过该项目的源码,会发现该项目很精悍,代码不多,通过一些脚本使每个benchmark的配置、准备和运行变得规范化,用起来十分方便。