Hadoop Benchmark

Table of Contents

1 TestDFSIO

测试hdfs吞吐

hdfs@hadoop1:~$ hadoop jar /usr/lib/hadoop/hadoop-test-0.20.2-cdh3u3.jar TestDFSIO
Usage: TestDFSIO [genericOptions] -read | -write | -append | -clean [-nrFiles N] [-fileSize Size[B|KB|MB|GB|TB]] [-resFile resultFileName] [-bufferSize Bytes] [-rootDir]%
  • read / write / append / clean 操作类型 append和write执行效率差别不大,但是write会创建新文件所以使用比较方便 (default read)
  • nrFiles 文件数目(default 1) 启动相同数量的map
  • fileSize 每个文件大小(1MB)
  • resFile 结果报告文件(TestDFSIO_results.log)
  • bufferSize write buffer size(单次write写入大小)(1000000 bytes)
  • rootDir 操作文件根目录(/benchmarks/TestDFSIO/)
----- TestDFSIO ----- : write
           Date & time: Thu Apr 25 19:14:21 CST 2013
       Number of files: 2
Total MBytes processed: 2.0
     Throughput mb/sec: 7.575757575757576
Average IO rate mb/sec: 7.61113977432251
 IO rate std deviation: 0.5189420757292891
    Test exec time sec: 14.565

----- TestDFSIO ----- : read
           Date & time: Thu Apr 25 19:15:13 CST 2013
       Number of files: 2
Total MBytes processed: 2.0
     Throughput mb/sec: 27.77777777777778
Average IO rate mb/sec: 28.125
 IO rate std deviation: 3.125
    Test exec time sec: 14.664
  • throughtput = sum(filesize) / sum(time)
  • avaerage io rate = sum(filesize/time) / n
  • io rate std deviation

2 TeraSort

通过排序测试MR执行效率 我看了一下代码map/reduce都有CPU操作,并且这个也非常依靠shuffle/copy.因此这个测试应该会是比较全面的

hdfs@hadoop1:~$ hadoop jar /usr/lib/hadoop/hadoop-examples-0.20.2-cdh3u3.jar <command>
  • teragen 产生排序数据
    • <number of 100-byte rows>
      • 10 bytes key(random characters)
      • 10 bytes rowid(right justified row id as a int)
      • 78 bytes filler
      • \r\n
    • <output dir>
  • terasort 对数据排序
    • <input dir>
    • <output dir>
  • teravalidate 对排序数据做验证

可以使用hadoop job -history all <job-output-dir>来观察程序运行数据,也可以通过web page来分析。

3 nnbench

测试nn负载能力

➜  ~HADOOP_HOME  hadoop jar hadoop-test-0.20.2-cdh3u3.jar nnbench
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
  • startTime 作用是为了能够让所有的map同时启动以便对nn造成压力
➜  ~HADOOP_HOME  hadoop jar hadoop-test-0.20.2-cdh3u3.jar nnbench -operation create_write -bytesToWrite 0 -numberOfFiles 1200
➜  ~HADOOP_HOME  hadoop jar hadoop-test-0.20.2-cdh3u3.jar nnbench -operation open_read

结果报告文件是 NNBench_results.log

-------------- NNBench -------------- :
                               Version: NameNode Benchmark 0.4
                           Date & time: 2013-04-25 19:41:02,873

                        Test Operation: create_write
                            Start time: 2013-04-25 19:40:21,70
                           Maps to run: 1
                        Reduces to run: 1
                    Block Size (bytes): 1
                        Bytes to write: 0
                    Bytes per checksum: 1
                       Number of files: 1200
                    Replication factor: 1
            Successful file operations: 1200

        # maps that missed the barrier: 0
                          # exceptions: 0

               TPS: Create/Write/Close: 75
Avg exec time (ms): Create/Write/Close: 26.526666666666667
            Avg Lat (ms): Create/Write: 13.236666666666666
                   Avg Lat (ms): Close: 13.164166666666667

                 RAW DATA: AL Total #1: 15884
                 RAW DATA: AL Total #2: 15797
              RAW DATA: TPS Total (ms): 31832
       RAW DATA: Longest Map Time (ms): 31832.0
                   RAW DATA: Late maps: 0
             RAW DATA: # of exceptions: 0

-------------- NNBench -------------- :
                               Version: NameNode Benchmark 0.4
                           Date & time: 2013-04-25 19:44:42,354

                        Test Operation: open_read
                            Start time: 2013-04-25 19:44:31,921
                           Maps to run: 1
                        Reduces to run: 1
                    Block Size (bytes): 1
                        Bytes to write: 0
                    Bytes per checksum: 1
                       Number of files: 1
                    Replication factor: 1
            Successful file operations: 1

        # maps that missed the barrier: 0
                          # exceptions: 0

                        TPS: Open/Read: 500
         Avg Exec time (ms): Open/Read: 2.0
                    Avg Lat (ms): Open: 2.0

                 RAW DATA: AL Total #1: 2
                 RAW DATA: AL Total #2: 0
              RAW DATA: TPS Total (ms): 2
       RAW DATA: Longest Map Time (ms): 2.0
                   RAW DATA: Late maps: 0
             RAW DATA: # of exceptions: 0
  • maps that missed the barrier 从代码上分析是,在等待到start time期间中,如果sleep出现异常的话。
  • exceptions 表示在操作文件系统时候的exception数量
  • TPS transactions per second
  • exec(execution) 执行时间
  • lat(latency) 延迟时间
  • late maps 和 maps missed the barrier是一个概念。

对于后面RAW DATA部分的话,从代码上看,就是为了计算出上面那些指标的,所以没有必要关注。

4 mrbench

测试运行small mr jobs执行效率,主要关注响应时间。

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]
  • baseDir 输入输出目录
  • jar 通常不需要指定,用默认即可。
  • inputLines 输入条数
  • inputType 输入是否有序
hdfs@hadoop1:~$ hadoop jar /usr/lib/hadoop/hadoop-test-0.20.2-cdh3u3.jar mrbench -verbose

结果直接输出在终端上面,

Total MapReduce jobs executed: 1
Total lines of data per job: 1
Maps per job: 2
Reduces per job: 1
Total milliseconds for task: 1 = 16452
DataLines	Maps	Reduces	AvgTime (milliseconds)
1		2	1	16452

可以看到每个任务平均执行时间在16.452s.

5 hbase.PerformanceEvaluation

hdfs@hadoop1:~$ hbase org.apache.hadoop.hbase.PerformanceEvaluation
Usage: java org.apache.hadoop.hbase.PerformanceEvaluation \
  [--miniCluster] [--nomapred] [--rows=ROWS] <command> <nclients>

Options:
 miniCluster     Run the test on an HBaseMiniCluster
 nomapred        Run multiple clients using threads (rather than use mapreduce)
 rows            Rows each client runs. Default: One million
 flushCommits    Used to determine if the test should flush the table.  Default: false
 writeToWAL      Set writeToWAL on puts. Default: True

Command:
 filterScan      Run scan test using a filter to find a specific row based on it's value (make sure to use --rows=20)
 randomRead      Run random read test
 randomSeekScan  Run random seek and scan 100 test
 randomWrite     Run random write test
 scan            Run scan test (read every row)
 scanRange10     Run random seek scan with both start and stop row (max 10 rows)
 scanRange100    Run random seek scan with both start and stop row (max 100 rows)
 scanRange1000   Run random seek scan with both start and stop row (max 1000 rows)
 scanRange10000  Run random seek scan with both start and stop row (max 10000 rows)
 sequentialRead  Run sequential read test
 sequentialWrite Run sequential write test

Args:
 nclients        Integer. Required. Total number of clients (and HRegionServers)
                 running: 1 <= value <= 500
Examples:
 To run a single evaluation client:
 $ bin/hbase org.apache.hadoop.hbase.PerformanceEvaluation sequentialWrite 1

从参数上看还是比较直接的。benchmark每个client通常对应10个mapper, 每个client操作<rows>个row,因此每个mapper操作<rows>/10个row,每个row大约1000bytes.

  • filterScan 随机生成value,然后从头开始scan直到equal
  • randomRead 随机选取key读取
  • randomSeekScan 从某个随机位置开始scan最多100个
  • randomWrite 随即生成key写入
  • scan 每次scan 1个row,start随机
  • scan<num> 每次scan num个row,start随机
  • seqRead 顺序地读取每个key
  • seqWrite 顺序地写入每个key
  • #note: 这里的key都非常简单,10个字符的数字,printf("%010d",row)
hdfs@hadoop1:~$ time hbase org.apache.hadoop.hbase.PerformanceEvaluation --rows=1000 sequentialWrite 2
13/04/25 23:47:56 INFO mapred.JobClient:   HBase Performance Evaluation
13/04/25 23:47:56 INFO mapred.JobClient:     Row count=2000
13/04/25 23:47:56 INFO mapred.JobClient:     Elapsed time in milliseconds=258

输出结果是在counter里面,这里面row count = 2000, 占用时间为258 ms.

comments powered by Disqus