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设计师网站建设,西宁网站seo,wordpress开发 文档下载,网站备案空间备案部署完hadoop单机版后，试下mapreduce是怎么分析处理数据的 Word Count Word Count 就是"词语统计"，这是 MapReduce 工作程序中最经典的一种。它的主要任务是对一个文本文件中的词语作归纳统计，统计出每个出现过的词语一共出现的次…

部署完hadoop单机版后，试下mapreduce是怎么分析处理数据的

Word Count

Word Count 就是"词语统计"，这是 MapReduce 工作程序中最经典的一种。它的主要任务是对一个文本文件中的词语作归纳统计，统计出每个出现过的词语一共出现的次数。

Hadoop 中包含了许多经典的 MapReduce 示例程序，其中就包含 Word Count.

准备演示文件input.txt

# cat input.txt 
I LOVE GG
I LIKE YY
I LOVE UU
I LIKE RR

复制input.txt至hadoop中

# hdfs dfs -put input.txt /test
# hdfs dfs -ls /test
Found 4 items
drwxr-xr-x   - yunwei supergroup          0 2023-02-24 17:14 /test/a
-rw-r--r--   2 yunwei supergroup         51 2023-02-24 17:25 /test/b.txt
-rw-r--r--   2 yunwei supergroup         51 2023-02-24 17:21 /test/hello-hadoop.txt
-rw-r--r--   2 yunwei supergroup         40 2023-02-27 15:59 /test/input.txt

查看hadoop下的mapreduce包 

# ll $HADOOP_HOME/share/hadoop/mapreduce/
total 4876
-rw-rw-r-- 1 yunwei yunwei  526732 Oct  3  2016 hadoop-mapreduce-client-app-2.6.5.jar
-rw-rw-r-- 1 yunwei yunwei  686773 Oct  3  2016 hadoop-mapreduce-client-common-2.6.5.jar
-rw-rw-r-- 1 yunwei yunwei 1535776 Oct  3  2016 hadoop-mapreduce-client-core-2.6.5.jar
-rw-rw-r-- 1 yunwei yunwei  259326 Oct  3  2016 hadoop-mapreduce-client-hs-2.6.5.jar
-rw-rw-r-- 1 yunwei yunwei   27489 Oct  3  2016 hadoop-mapreduce-client-hs-plugins-2.6.5.jar
-rw-rw-r-- 1 yunwei yunwei   61309 Oct  3  2016 hadoop-mapreduce-client-jobclient-2.6.5.jar
-rw-rw-r-- 1 yunwei yunwei 1514166 Oct  3  2016 hadoop-mapreduce-client-jobclient-2.6.5-tests.jar
-rw-rw-r-- 1 yunwei yunwei   67762 Oct  3  2016 hadoop-mapreduce-client-shuffle-2.6.5.jar
-rw-rw-r-- 1 yunwei yunwei  292710 Oct  3  2016 hadoop-mapreduce-examples-2.6.5.jar
drwxrwxr-x 2 yunwei yunwei    4096 Oct  3  2016 lib
drwxrwxr-x 2 yunwei yunwei      30 Oct  3  2016 lib-examples
drwxrwxr-x 2 yunwei yunwei    4096 Oct  3  2016 sources

vi hadoop-mapreduce-examples-2.6.5.jar

hadoop的命令执行jar

# hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar  WordCount  input.txt output      
Unknown program 'WordCount' chosen.
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 datasetsmultifilewc: 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 terasortterasort: Run the terasortteravalidate: Checking results of terasortwordcount: 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.

执行报错，虽然example.jar中有这个WordCount类，但填下类名没起作用。改为小写后，继续执行，报错。hdfs的目录下没有这个文件，将input.txt文件上传至hadoop中。

# hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar  wordcount  input.txt output               
23/02/27 15:58:58 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
23/02/27 15:58:58 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
23/02/27 15:58:58 INFO mapreduce.JobSubmitter: Cleaning up the staging area file:/tmp/hadoop-yunwei/mapred/staging/yunwei293247600/.staging/job_local293247600_0001
org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: hdfs://10.15.49.26:8020/user/yunwei/input.txtat org.apache.hadoop.mapreduce.lib.input.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:321)at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.listStatus(FileInputFormat.java:264)at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplits(FileInputFormat.java:385)at org.apache.hadoop.mapreduce.JobSubmitter.writeNewSplits(JobSubmitter.java:302)at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:319)at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:197)at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1297)at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1294)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:422)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1692)at org.apache.hadoop.mapreduce.Job.submit(Job.java:1294)at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1315)at org.apache.hadoop.examples.WordCount.main(WordCount.java:87)at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)at java.lang.reflect.Method.invoke(Method.java:498)at org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:71)at org.apache.hadoop.util.ProgramDriver.run(ProgramDriver.java:144)at org.apache.hadoop.examples.ExampleDriver.main(ExampleDriver.java:74)at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)at java.lang.reflect.Method.invoke(Method.java:498)at org.apache.hadoop.util.RunJar.run(RunJar.java:221)at org.apache.hadoop.util.RunJar.main(RunJar.java:136)

# hdfs dfs -put input.txt /test
# hdfs dfs -ls /test 

# hdfs dfs -put input.txt /test
# hdfs dfs -ls /test
Found 4 items
drwxr-xr-x   - yunwei supergroup          0 2023-02-24 17:14 /test/a
-rw-r--r--   2 yunwei supergroup         51 2023-02-24 17:25 /test/b.txt
-rw-r--r--   2 yunwei supergroup         51 2023-02-24 17:21 /test/hello-hadoop.txt
-rw-r--r--   2 yunwei supergroup         40 2023-02-27 15:59 /test/input.txt

# hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar  wordcount  /test/input.txt  output

解释一下含义：

hadoop jar从 jar 文件执行 MapReduce 任务，之后跟着的是示例程序包的路径。

wordcount表示执行示例程序包中的 Word Count 程序，之后跟这两个参数，第一个是输入文件，第二个是输出结果的目录名（因为输出结果是多个文件）。

执行之后，应该会输出一个文件夹 output，在这个文件夹里有两个文件：_SUCCESS 和 part-r-00000。

/test/output  上面命令如果指定output在hadoop的路径，相关执行结果便不会生成在默认位置，而是命令指定的位置。

其中 _SUCCESS 只是用于表达执行成功的空文件，part-r-00000 则是处理结果，当我们显示一下它的内容：

指定到hadoop目录下的input.txt文件

# hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar  wordcount  /test/input.txt output      
23/02/27 16:00:13 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
23/02/27 16:00:13 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
23/02/27 16:00:13 INFO input.FileInputFormat: Total input paths to process : 1
23/02/27 16:00:13 INFO mapreduce.JobSubmitter: number of splits:1
23/02/27 16:00:13 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1519269801_0001
23/02/27 16:00:13 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
23/02/27 16:00:13 INFO mapreduce.Job: Running job: job_local1519269801_0001
23/02/27 16:00:13 INFO mapred.LocalJobRunner: OutputCommitter set in config null
23/02/27 16:00:13 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
23/02/27 16:00:13 INFO mapred.LocalJobRunner: Waiting for map tasks
23/02/27 16:00:13 INFO mapred.LocalJobRunner: Starting task: attempt_local1519269801_0001_m_000000_0
23/02/27 16:00:13 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
23/02/27 16:00:13 INFO mapred.MapTask: Processing split: hdfs://10.15.49.26:8020/test/input.txt:0+40
23/02/27 16:00:14 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
23/02/27 16:00:14 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
23/02/27 16:00:14 INFO mapred.MapTask: soft limit at 83886080
23/02/27 16:00:14 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
23/02/27 16:00:14 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
23/02/27 16:00:14 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
23/02/27 16:00:14 INFO mapred.LocalJobRunner: 
23/02/27 16:00:14 INFO mapred.MapTask: Starting flush of map output
23/02/27 16:00:14 INFO mapred.MapTask: Spilling map output
23/02/27 16:00:14 INFO mapred.MapTask: bufstart = 0; bufend = 88; bufvoid = 104857600
23/02/27 16:00:14 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214352(104857408); length = 45/6553600
23/02/27 16:00:14 INFO mapred.MapTask: Finished spill 0
23/02/27 16:00:14 INFO mapred.Task: Task:attempt_local1519269801_0001_m_000000_0 is done. And is in the process of committing
23/02/27 16:00:14 INFO mapred.LocalJobRunner: map
23/02/27 16:00:14 INFO mapred.Task: Task 'attempt_local1519269801_0001_m_000000_0' done.
23/02/27 16:00:14 INFO mapred.LocalJobRunner: Finishing task: attempt_local1519269801_0001_m_000000_0
23/02/27 16:00:14 INFO mapred.LocalJobRunner: map task executor complete.
23/02/27 16:00:14 INFO mapred.LocalJobRunner: Waiting for reduce tasks
23/02/27 16:00:14 INFO mapred.LocalJobRunner: Starting task: attempt_local1519269801_0001_r_000000_0
23/02/27 16:00:14 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
23/02/27 16:00:14 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@5cd45799
23/02/27 16:00:14 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=334338464, maxSingleShuffleLimit=83584616, mergeThreshold=220663392, ioSortFactor=10, memToMemMergeOutputsThreshold=10
23/02/27 16:00:14 INFO reduce.EventFetcher: attempt_local1519269801_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
23/02/27 16:00:14 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1519269801_0001_m_000000_0 decomp: 68 len: 72 to MEMORY
23/02/27 16:00:14 INFO reduce.InMemoryMapOutput: Read 68 bytes from map-output for attempt_local1519269801_0001_m_000000_0
23/02/27 16:00:14 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 68, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->68
23/02/27 16:00:14 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
23/02/27 16:00:14 INFO mapred.LocalJobRunner: 1 / 1 copied.
23/02/27 16:00:14 INFO reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
23/02/27 16:00:14 INFO mapred.Merger: Merging 1 sorted segments
23/02/27 16:00:14 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 63 bytes
23/02/27 16:00:14 INFO reduce.MergeManagerImpl: Merged 1 segments, 68 bytes to disk to satisfy reduce memory limit
23/02/27 16:00:14 INFO reduce.MergeManagerImpl: Merging 1 files, 72 bytes from disk
23/02/27 16:00:14 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
23/02/27 16:00:14 INFO mapred.Merger: Merging 1 sorted segments
23/02/27 16:00:14 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 63 bytes
23/02/27 16:00:14 INFO mapred.LocalJobRunner: 1 / 1 copied.
23/02/27 16:00:14 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
23/02/27 16:00:14 INFO mapred.Task: Task:attempt_local1519269801_0001_r_000000_0 is done. And is in the process of committing
23/02/27 16:00:14 INFO mapred.LocalJobRunner: 1 / 1 copied.
23/02/27 16:00:14 INFO mapred.Task: Task attempt_local1519269801_0001_r_000000_0 is allowed to commit now
23/02/27 16:00:14 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1519269801_0001_r_000000_0' to hdfs://xx.xx.xx.xx:xx/user/yunwei/output/_temporary/0/task_local1519269801_0001_r_000000
23/02/27 16:00:14 INFO mapred.LocalJobRunner: reduce > reduce
23/02/27 16:00:14 INFO mapred.Task: Task 'attempt_local1519269801_0001_r_000000_0' done.
23/02/27 16:00:14 INFO mapred.LocalJobRunner: Finishing task: attempt_local1519269801_0001_r_000000_0
23/02/27 16:00:14 INFO mapred.LocalJobRunner: reduce task executor complete.
23/02/27 16:00:14 INFO mapreduce.Job: Job job_local1519269801_0001 running in uber mode : false
23/02/27 16:00:14 INFO mapreduce.Job:  map 100% reduce 100%
23/02/27 16:00:14 INFO mapreduce.Job: Job job_local1519269801_0001 completed successfully
23/02/27 16:00:14 INFO mapreduce.Job: Counters: 38File System CountersFILE: Number of bytes read=585924FILE: Number of bytes written=1105104FILE: Number of read operations=0FILE: Number of large read operations=0FILE: Number of write operations=0HDFS: Number of bytes read=80HDFS: Number of bytes written=38HDFS: Number of read operations=13HDFS: Number of large read operations=0HDFS: Number of write operations=4Map-Reduce FrameworkMap input records=4Map output records=12Map output bytes=88Map output materialized bytes=72Input split bytes=103Combine input records=12Combine output records=7Reduce input groups=7Reduce shuffle bytes=72Reduce input records=7Reduce output records=7Spilled Records=14Shuffled Maps =1Failed Shuffles=0Merged Map outputs=1GC time elapsed (ms)=0CPU time spent (ms)=0Physical memory (bytes) snapshot=0Virtual memory (bytes) snapshot=0Total committed heap usage (bytes)=716177408Shuffle ErrorsBAD_ID=0CONNECTION=0IO_ERROR=0WRONG_LENGTH=0WRONG_MAP=0WRONG_REDUCE=0File Input Format Counters Bytes Read=40File Output Format Counters Bytes Written=38

查看执行结果，从上面执行的日志中，可以看到output文件生成的位置信息

执行之后，应该会输出一个文件夹 output，在这个文件夹里有两个文件：_SUCCESS 和 part-r-00000。

其中 _SUCCESS 只是用于表达执行成功的空文件，part-r-00000 则是处理结果

# hdfs dfs -lsr /user/yunwei
lsr: DEPRECATED: Please use 'ls -R' instead.
drwxr-xr-x   - yunwei supergroup          0 2023-02-27 16:00 /user/yunwei/output
-rw-r--r--   2 yunwei supergroup          0 2023-02-27 16:00 /user/yunwei/output/_SUCCESS
-rw-r--r--   2 yunwei supergroup         38 2023-02-27 16:00 /user/yunwei/output/part-r-00000

 hdfs dfs -cat /user/yunwei/output/part-r-00000

# hdfs dfs -cat /user/yunwei/output/part-r-00000
GG      1
I       4
LIKE    2
LOVE    2
RR      1
UU      1
YY      1

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