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 [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： [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： 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： [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： [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 [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 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： [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 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.1.7 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： [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。 (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。 [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： [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 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： $ hadoop jar hadoop-*examples*.jar teragen <number of 100-byte rows> <output dir> 以下命令运行TeraGen生成10GB的输入数据，并输出到目录/examples/terasort-input： [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$Counters CHECKSUM=214760662691937609 File Input Format Counters Bytes Read=0 File Output Format Counters Bytes Written=10000000000 TeraGen产生的数据每行的格式如下： <10 bytes key><10 bytes rowid><78 bytes filler>\r\n 其中： 1、key是一些随机字符，每个字符的ASCII码取值范围为[32, 126] 2、rowid是一个整数，右对齐 3、filler由7组字符组成，每组有10个字符（最后一组8个），字符从’A'到’Z'依次取值 以下命令运行TeraSort对数据进行排序，并将结果输出到目录/examples/terasort-output： [hsu@server01 ~]$ 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 [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 [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行数据进行排序刚好1分钟时间，部分数据： 0000000: 00 00 00 a7 0d 2a a8 02 da da 00 11 30 30 30 30 .....*......00000000010: 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 0000000000000000
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*2 413.651525.48 通过警告阈值(内存)
结论：
1、对于大数据量impala并不占优势，而且还可能节点impalad节点崩溃，impala非常吃内存，parquet也非常吃内存！
2、hive运行会出现大量IO操作，往往impala运行不下来的任务hive能够运行。
3、impala对sql支持度以及对hive一些分析函数特殊数据格式支持仍然有待新版本。
