一. Hadoop基准测试

Hadoop自带了几个基准测试,被打包在几个jar包中。本文主要是cloudera版本测试

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[hsu@server01 ~]$ ls /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop* | egrep "examples|test"
/opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples-2.5.0-mr1-cdh5.2.0.jar
/opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples.jar
/opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test-2.5.0-mr1-cdh5.2.0.jar
/opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.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:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.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.
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。

(2) TestDFSIO write

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:

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TestDFSIO
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:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.jar TestDFSIO -write -nrFiles 10 -fileSize 1000
15/01/13 15:14:17 INFO fs.TestDFSIO: TestDFSIO.1.7
15/01/13 15:14:17 INFO fs.TestDFSIO: nrFiles = 10
15/01/13 15:14:17 INFO fs.TestDFSIO: nrBytes (MB) = 1000.0
15/01/13 15:14:17 INFO fs.TestDFSIO: bufferSize = 1000000
15/01/13 15:14:17 INFO fs.TestDFSIO: baseDir = /benchmarks/TestDFSIO
15/01/13 15:14:18 INFO fs.TestDFSIO: creating control file: 1048576000 bytes, 10 files
15/01/13 15:14:19 INFO fs.TestDFSIO: created control files for: 10 files
15/01/13 15:15:23 INFO fs.TestDFSIO: ----- TestDFSIO ----- : write
15/01/13 15:15:23 INFO fs.TestDFSIO: Date & time: Tue Jan 13 15:15:23 CST 2015
15/01/13 15:15:23 INFO fs.TestDFSIO: Number of files: 10
15/01/13 15:15:23 INFO fs.TestDFSIO: Total MBytes processed: 10000.0
15/01/13 15:15:23 INFO fs.TestDFSIO: Throughput mb/sec: 29.67623230554649
15/01/13 15:15:23 INFO fs.TestDFSIO: Average IO rate mb/sec: 29.899526596069336
15/01/13 15:15:23 INFO fs.TestDFSIO: IO rate std deviation: 2.6268824639446526
15/01/13 15:15:23 INFO fs.TestDFSIO: Test exec time sec: 64.203
15/01/13 15:15:23 INFO fs.TestDFSIO:

(3) TestDFSIO read

以下的例子将从HDFS中读取10个1000MB的文件:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.jar TestDFSIO -read -nrFiles 10 -fileSize 1000
15/01/13 15:42:35 INFO fs.TestDFSIO: TestDFSIO.1.7
15/01/13 15:42:35 INFO fs.TestDFSIO: nrFiles = 10
15/01/13 15:42:35 INFO fs.TestDFSIO: nrBytes (MB) = 1000.0
15/01/13 15:42:35 INFO fs.TestDFSIO: bufferSize = 1000000
15/01/13 15:42:35 INFO fs.TestDFSIO: baseDir = /benchmarks/TestDFSIO
15/01/13 15:42:36 INFO fs.TestDFSIO: creating control file: 1048576000 bytes, 10 files
15/01/13 15:42:37 INFO fs.TestDFSIO: created control files for: 10 files

(4) 清空测试数据

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.jar TestDFSIO -clean
15/01/13 15:46:51 INFO fs.TestDFSIO: TestDFSIO.1.7
15/01/13 15:46:51 INFO fs.TestDFSIO: nrFiles = 1
15/01/13 15:46:51 INFO fs.TestDFSIO: nrBytes (MB) = 1.0
15/01/13 15:46:51 INFO fs.TestDFSIO: bufferSize = 1000000
15/01/13 15:46:51 INFO fs.TestDFSIO: baseDir = /benchmarks/TestDFSIO
15/01/13 15:46:52 INFO fs.TestDFSIO: Cleaning up test files

(4) nnbench测试[NameNode benchmark (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:

以下例子使用12个mapper和6个reducer来创建1000个文件:
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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.jar nnbench -operation create_write -maps 12 -reduces 6 -blockSize 1 -bytesToWrite 0 -numberOfFiles 1000 -replicationFactorPerFile 3 -readFileAfterOpen true -baseDir /benchmarks/NNBench-`hostname -s`
NameNode Benchmark 0.4
15/01/13 15:53:33 INFO hdfs.NNBench: Test Inputs:
15/01/13 15:53:33 INFO hdfs.NNBench: Test Operation: create_write
15/01/13 15:53:33 INFO hdfs.NNBench: Start time: 2015-01-13 15:55:33,585
15/01/13 15:53:33 INFO hdfs.NNBench: Number of maps: 12
15/01/13 15:53:33 INFO hdfs.NNBench: Number of reduces: 6
15/01/13 15:53:33 INFO hdfs.NNBench: Block Size: 1
15/01/13 15:53:33 INFO hdfs.NNBench: Bytes to write: 0
15/01/13 15:53:33 INFO hdfs.NNBench: Bytes per checksum: 1
15/01/13 15:53:33 INFO hdfs.NNBench: Number of files: 1000
15/01/13 15:53:33 INFO hdfs.NNBench: Replication factor: 3
15/01/13 15:53:33 INFO hdfs.NNBench: Base dir: /benchmarks/NNBench-server01
15/01/13 15:53:33 INFO hdfs.NNBench: Read file after open: true
15/01/13 15:53:34 INFO hdfs.NNBench: Deleting data directory
15/01/13 15:53:34 INFO hdfs.NNBench: Creating 12 control files

15/01/13 15:56:06 INFO hdfs.NNBench: -------------- NNBench -------------- :
15/01/13 15:56:06 INFO hdfs.NNBench: Version: NameNode Benchmark 0.4
15/01/13 15:56:06 INFO hdfs.NNBench: Date & time: 2015-01-13 15:56:06,539
15/01/13 15:56:06 INFO hdfs.NNBench:
15/01/13 15:56:06 INFO hdfs.NNBench: Test Operation: create_write
15/01/13 15:56:06 INFO hdfs.NNBench: Start time: 2015-01-13 15:55:33,585
15/01/13 15:56:06 INFO hdfs.NNBench: Maps to run: 12
15/01/13 15:56:06 INFO hdfs.NNBench: Reduces to run: 6
15/01/13 15:56:06 INFO hdfs.NNBench: Block Size (bytes): 1
15/01/13 15:56:06 INFO hdfs.NNBench: Bytes to write: 0
15/01/13 15:56:06 INFO hdfs.NNBench: Bytes per checksum: 1
15/01/13 15:56:06 INFO hdfs.NNBench: Number of files: 1000
15/01/13 15:56:06 INFO hdfs.NNBench: Replication factor: 3
15/01/13 15:56:06 INFO hdfs.NNBench: Successful file operations: 0
15/01/13 15:56:06 INFO hdfs.NNBench:
15/01/13 15:56:06 INFO hdfs.NNBench: # maps that missed the barrier: 0
15/01/13 15:56:06 INFO hdfs.NNBench: # exceptions: 0
15/01/13 15:56:06 INFO hdfs.NNBench:
15/01/13 15:56:06 INFO hdfs.NNBench: TPS: Create/Write/Close: 0
15/01/13 15:56:06 INFO hdfs.NNBench: Avg exec time (ms): Create/Write/Close: 0.0
15/01/13 15:56:06 INFO hdfs.NNBench: Avg Lat (ms): Create/Write: NaN
15/01/13 15:56:06 INFO hdfs.NNBench: Avg Lat (ms): Close: NaN
15/01/13 15:56:06 INFO hdfs.NNBench:
15/01/13 15:56:06 INFO hdfs.NNBench: RAW DATA: AL Total #1: 0
15/01/13 15:56:06 INFO hdfs.NNBench: RAW DATA: AL Total #2: 0
15/01/13 15:56:06 INFO hdfs.NNBench: RAW DATA: TPS Total (ms): 0
15/01/13 15:56:06 INFO hdfs.NNBench: RAW DATA: Longest Map Time (ms): 0.0
15/01/13 15:56:06 INFO hdfs.NNBench: RAW DATA: Late maps: 0
15/01/13 15:56:06 INFO hdfs.NNBench: RAW DATA: # of exceptions: 0
15/01/13 15:56:06 INFO hdfs.NNBench:

(5) mrbench测试[MapReduce benchmark (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:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.jar mrbench --help
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:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.jar mrbench -numRuns 50
MRBenchmark.0.0.2
15/01/13 16:17:19 INFO mapred.MRBench: creating control file: 1 numLines, ASCENDING sortOrder
15/01/13 16:17:20 INFO mapred.MRBench: created control file: /benchmarks/MRBench/mr_input/input_331064064.txt
15/01/13 16:17:20 INFO mapred.MRBench: Running job 0: input=hdfs://server01:8020/benchmarks/MRBench/mr_input output=hdfs://server01:8020/benchmarks/MRBench/mr_output/output_556018847

DataLines Maps Reduces AvgTime (milliseconds)
1 2 1 26748

以上结果表示平均作业完成时间是26秒。

以下例子会运行一个小作业500次:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-test.jar mrbench -numRuns 500 -maps 20 -reduces 10 -inputLines 50 -verbose
MRBenchmark.0.0.2
15/01/14 10:43:53 INFO mapred.MRBench: creating control file: 1 numLines, ASCENDING sortOrder
15/01/14 10:43:54 INFO mapred.MRBench: created control file: /benchmarks/MRBench/mr_input/input_-1773312505.txt
15/01/14 10:43:54 INFO mapred.MRBench: Running job 0: input=hdfs://server01:8020/benchmarks/MRBench/mr_input output=hdfs://server01:8020/benchmarks/MRBench/mr_output/output_-447811996
15/01/14 10:43:54 INFO client.RMProxy: Connecting to ResourceManager at server01/135.33.5.53:8032
15/01/14 10:43:54 INFO client.RMProxy: Connecting to ResourceManager at server01/135.33.5.53:8032
15/01/14 10:43:54 INFO mapred.FileInputFormat: Total input paths to process : 1
15/01/14 10:43:55 INFO mapreduce.JobSubmitter: number of splits:2
15/01/14 10:43:55 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1420542591388_0112
15/01/14 10:43:55 INFO impl.YarnClientImpl: Submitted application application_1420542591388_0112
15/01/14 10:43:55 INFO mapreduce.Job: The url to track the job: http://server01:8088/proxy/application_1420542591388_0112/
15/01/14 10:43:55 INFO mapreduce.Job: Running job: job_1420542591388_0112
15/01/14 10:44:06 INFO mapreduce.Job: Job job_1420542591388_0112 running in uber mode : false
Total milliseconds for task: 494 = 29859
Total milliseconds for task: 495 = 29878
Total milliseconds for task: 496 = 29908
Total milliseconds for task: 497 = 29943
Total milliseconds for task: 498 = 29897
Total milliseconds for task: 499 = 29919
Total milliseconds for task: 500 = 28881
DataLines Maps Reduces AvgTime (milliseconds)
50 40 20 31298

以上结果表示平均作业完成时间是31秒。

(6) 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*.jar中。

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[hsu@server01 ~]$ ls /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples*
/opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples-2.5.0-mr1-cdh5.2.0.jar
/opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples.jar

执行以下命令会列出所有的示例程序:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples.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.
bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount: An example job that count the pageview counts from a database.
distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
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 a quasi-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.
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.
wordmean: A map/reduce program that counts the average length of the words in the input files.
wordmedian: A map/reduce program that counts the median length of the words in the input files.
wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files.

(7) TeraSort[TeraSort: Run the actual TeraSort benchmark]

一个完整的TeraSort测试需要按以下三步执行:

  • 1、用TeraGen生成随机数据
  • 2、对输入数据运行TeraSort
  • 3、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:

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以下命令运行TeraGen生成10GB的输入数据,并输出到目录/examples/terasort-input:

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples.jar teragen 100000000 /examples/terasort-input
15/01/13 16:57:34 INFO client.RMProxy: Connecting to ResourceManager at server01/135.33.5.53:8032
15/01/13 16:57:35 INFO terasort.TeraSort: Generating 100000000 using 2
15/01/13 16:57:35 INFO mapreduce.JobSubmitter: number of splits:2
15/01/13 16:59:07 INFO mapreduce.Job: Job job_1420542591388_0105 completed successfully
15/01/13 16:59:08 INFO mapreduce.Job: Counters: 31
File System Counters
FILE: Number of bytes read=0
FILE: Number of bytes written=211922
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=170
HDFS: Number of bytes written=10000000000
HDFS: Number of read operations=8
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Job Counters
Launched map tasks=2
Other local map tasks=2
Total time spent by all maps in occupied slots (ms)=150416
Total time spent by all reduces in occupied slots (ms)=0
Total time spent by all map tasks (ms)=150416
Total vcore-seconds taken by all map tasks=150416
Total megabyte-seconds taken by all map tasks=154025984
Map-Reduce Framework
Map input records=100000000
Map output records=100000000
Input split bytes=170
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=1230
CPU time spent (ms)=175090
Physical memory (bytes) snapshot=504807424
Virtual memory (bytes) snapshot=3230924800
Total committed heap usage (bytes)=1363148800
org.apache.hadoop.examples.terasort.TeraGen$Counters
CHECKSUM=214760662691937609
File Input Format Counters
Bytes Read=0
File Output Format Counters
Bytes Written=10000000000
  • TeraGen产生的数据每行的格式如下:

    <10 bytes key><10 bytes rowid><78 bytes filler>\r\n
    

    ** 其中:
    1、key是一些随机字符,每个字符的ASCII码取值范围为[32, 126]
    2、rowid是一个整数,右对齐
    3、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:
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    [hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples.jar terasort /examples/terasort-input /examples/terasort-output
    15/01/13 17:08:08 INFO terasort.TeraSort: starting
    15/01/13 17:08:10 INFO input.FileInputFormat: Total input paths to process : 2
    Spent 187ms computing base-splits.
    Spent 3ms computing TeraScheduler splits.
    Computing input splits took 192ms
    Sampling 10 splits of 76
    Making 144 from 100000 sampled records
    Computing parititions took 596ms
    Spent 791ms computing partitions.terasort /examples/terasort-input /examples/terasort-output
    15/01/13 17:09:13 INFO mapreduce.Job: Counters: 50
    File System Counters
    FILE: Number of bytes read=4461968618
    FILE: Number of bytes written=8889668662
    FILE: Number of read operations=0
    FILE: Number of large read operations=0
    FILE: Number of write operations=0
    HDFS: Number of bytes read=10000010260
    HDFS: Number of bytes written=10000000000
    HDFS: Number of read operations=660
    HDFS: Number of large read operations=0
    HDFS: Number of write operations=288
    Job Counters
    Launched map tasks=76
    Launched reduce tasks=144
    Data-local map tasks=75
    Rack-local map tasks=1
    Total time spent by all maps in occupied slots (ms)=933160
    Total time spent by all reduces in occupied slots (ms)=1227475
    Total time spent by all map tasks (ms)=933160
    Total time spent by all reduce tasks (ms)=1227475
    Total vcore-seconds taken by all map tasks=933160
    Total vcore-seconds taken by all reduce tasks=1227475
    Total megabyte-seconds taken by all map tasks=955555840
    Total megabyte-seconds taken by all reduce tasks=1256934400
    Map-Reduce Framework
    Map input records=100000000
    Map output records=100000000
    Map output bytes=10200000000
    Map output materialized bytes=4403942936
    Input split bytes=10260
    Combine input records=0
    Combine output records=0
    Reduce input groups=100000000
    Reduce shuffle bytes=4403942936
    Reduce input records=100000000
    Reduce output records=100000000
    Spilled Records=200000000
    Shuffled Maps =10944
    Failed Shuffles=0
    Merged Map outputs=10944
    GC time elapsed (ms)=45169
    CPU time spent (ms)=2021010
    Physical memory (bytes) snapshot=95792517120
    Virtual memory (bytes) snapshot=357225058304
    Total committed heap usage (bytes)=174283816960
    Shuffle Errors
    BAD_ID=0
    CONNECTION=0
    IO_ERROR=0
    WRONG_LENGTH=0
    WRONG_MAP=0
    WRONG_REDUCE=0
    File Input Format Counters
    Bytes Read=10000000000
    File Output Format Counters
    Bytes Written=10000000000
    15/01/13 17:09:13 INFO terasort.TeraSort: done

(8) terasort-validate 验证是否有序

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

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[hsu@server01 ~]$ sudo hadoop jar /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop-0.20-mapreduce/hadoop-examples.jar teravalidate /examples/terasort-output /examples/terasort-validate
15/01/13 17:17:37 INFO client.RMProxy: Connecting to ResourceManager at server01/135.33.5.53:8032
15/01/13 17:17:38 INFO input.FileInputFormat: Total input paths to process : 144
Spent 93ms computing base-splits.
Spent 3ms computing TeraScheduler splits.
15/01/13 17:17:38 INFO mapreduce.JobSubmitter: number of splits:144
15/01/13 17:17:38 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1420542591388_0107
15/01/13 17:17:38 INFO impl.YarnClientImpl: Submitted application application_1420542591388_0107teravalidate /examples/terasort-output /examples/terasort-validate
15/01/13 17:18:12 INFO mapreduce.Job: Job job_1420542591388_0107 completed successfully
15/01/13 17:18:12 INFO mapreduce.Job: Counters: 50
File System Counters
FILE: Number of bytes read=6963
FILE: Number of bytes written=15445453
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=10000019584
HDFS: Number of bytes written=25
HDFS: Number of read operations=435
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=144
Launched reduce tasks=1
Data-local map tasks=142
Rack-local map tasks=2
Total time spent by all maps in occupied slots (ms)=685624
Total time spent by all reduces in occupied slots (ms)=3384
Total time spent by all map tasks (ms)=685624
Total time spent by all reduce tasks (ms)=3384
Total vcore-seconds taken by all map tasks=685624
Total vcore-seconds taken by all reduce tasks=3384
Total megabyte-seconds taken by all map tasks=702078976
Total megabyte-seconds taken by all reduce tasks=3465216
Map-Reduce Framework
Map input records=100000000
Map output records=432
Map output bytes=11664
Map output materialized bytes=13830
Input split bytes=19584
Combine input records=0
Combine output records=0
Reduce input groups=289
Reduce shuffle bytes=13830
Reduce input records=432
Reduce output records=1
Spilled Records=864
Shuffled Maps =144
Failed Shuffles=0
Merged Map outputs=144
GC time elapsed (ms)=4014
CPU time spent (ms)=334280
Physical memory (bytes) snapshot=85470654464
Virtual memory (bytes) snapshot=234019295232
Total committed heap usage (bytes)=114868879360
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=10000000000
File Output Format Counters
Bytes Written=25

[hsu@server01 ~]$ hadoop fs -cat /examples/terasort-validate/* checksum 2fafbaf537afd49

结论:检测通过

(10) 总结

在提交任务目录下会生成两个文件

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[hsu@server01 ~]$ LANG=en
[hsu@server01 ~]$ ll
total 16
-rw-r--r-- 1 root root 1142 Jan 13 15:56 NNBench_results.log
-rw-r--r-- 1 root root 903 Jan 13 15:43 TestDFSIO_results.log

约对176838144行数据进行排序,部分数据:

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0000000: 	00 00 00 a7 0d 2a a8 02 da da 00 11 30 30 30 30	  .....*......0000
0000010: 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 0000000000000000

参考资料:

http://www.michael-noll.com/blog/2011/04/09/benchmarking-and-stress-testing-an-hadoop-cluster-with-terasort-testdfsio-nnbench-mrbench/
https://github.com/intel-hadoop/HiBench

二、hive/impala测试

Impala/hive性能报告:

下面对event_calling_201410(39.8G)和event_sms_201410(39.8G)做join操作和count(*):

(1) count(*)操作

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[yndx-bigdata-hadoop02:21000] > select count(*) from event_calling_201410;
Query: select count(*) from event_calling_201410

+-----------+
| count(*) |
+-----------+

| 425883373 |
+-----------+

Fetched 1 row(s) in 192.75s
hive (i_bil_hb_m)> select count(*) from event_calling_201410;
Total jobs = 1
Launching Job 1 out of 1
OK
_c0

425883373
Time taken: 386.804 seconds, Fetched: 1 row(s)
[yndx-bigdata-hadoop02:21000] > select count(*) from event_sms_201410;
Query: select count(*) from event_sms_201410

+----------+
| count(*) |
+----------+

| 80675409 |
+----------+

Fetched 1 row(s) in 33.52s

(2) 两表join操作

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hive (i_bil_hb_m)> select count(*) from event_calling_201410 c left outer join event_sms_201410 s on(s.calling_nbr=c.calling_nbr);  
Total jobs = 2
Stage-1 is selected by condition resolver.
Launching Job 1 out of 2
Number of reduce tasks not specified. Estimated from input data size: 279
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1420542591388_0987, Tracking URL = http://yndx-bigdata-hadoop01:8088/proxy/application_1420542591388_0987/
Kill Command = /opt/cloudera/parcels/CDH-5.2.0-1.cdh5.2.0.p0.36/lib/hadoop/bin/hadoop job -kill job_1420542591388_0987
Hadoop job information for Stage-1: number of mappers: 1110; number of reducers: 279
2015-01-15 10:44:01,665 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 19731.27 sec
MapReduce Total cumulative CPU time: 0 days 5 hours 28 minutes 51 seconds 270 msec
Ended Job = job_1420542591388_0987
Launching Job 2 out of 2
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
2015-01-15 10:44:33,709 Stage-2 map = 100%, reduce = 100%, Cumulative CPU 15.28 sec
MapReduce Total cumulative CPU time: 15 seconds 280 msec
Ended Job = job_1420542591388_0988
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1110 Reduce: 279 Cumulative CPU: 19731.27 sec HDFS Read: 298693978456 HDFS Write: 32922 SUCCESS
Stage-Stage-2: Map: 7 Reduce: 1 Cumulative CPU: 15.28 sec HDFS Read: 97828 HDFS Write: 12 SUCCESS
Total MapReduce CPU Time Spent: 0 days 5 hours 29 minutes 6 seconds 550 msec
OK
_c0
13106534553
Time taken: 413.651 seconds, Fetched: 1 row(s)
[yndx-bigdata-hadoop02:21000] > select count(*) from event_calling_201410 c left outer join event_sms_201410 s on(s.calling_nbr=c.calling_nbr);
Query: select count(*) from event_calling_201410 c left outer join event_sms_201410 s on(s.calling_nbr=c.calling_nbr)
+-------------+
| count(*) |
+-------------+
| 13106534553 |
+-------------+
Fetched 1 row(s) in 525.48s

(3) 统计结果

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Action		数据量(G)	   HiveTime(s)	   ImpalaTime(s) Hive结论 Imapla结论
Count(*) 39.8 386.804 192.75 通过 警告阈值(内存)
join(2) 39.8*2 413.651 525.48 通过 警告阈值(内存)