Hadoop 官方WordCount案例带你手把手的解析 _WordCount

生也有涯，知也无涯。这篇文章主要讲述Hadoop 官方WordCount案例带你手把手的解析相关的知识，希望能为你提供帮助。

文章目录

- 1.需求
- 2.需求分析
- 3.项目结构图
- 4.项目依赖包
- 5.编写Mapper
- 6.编写Reducer
- 7.编写Driver
- - 出现如下所示就欧克，接着看结果

1.需求 【Hadoop 官方WordCount案例带你手把手的解析】在给定的文本文件中统计输出每一个单词出现的总次数
hello.txt

hadoop hadoop ss ss cls cls jiao banzhang xue

2.需求分析

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3.项目结构图

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4.项目依赖包

< dependencies> < dependency> < groupId> junit< /groupId> < artifactId> junit< /artifactId> < version> RELEASE< /version> < /dependency> < dependency> < groupId> org.apache.logging.log4j< /groupId> < artifactId> log4j-core< /artifactId> < version> 2.8.2< /version> < /dependency> < dependency> < groupId> org.apache.hadoop< /groupId> < artifactId> hadoop-common< /artifactId> < version> 2.7.2< /version> < /dependency> < dependency> < groupId> org.apache.hadoop< /groupId> < artifactId> hadoop-client< /artifactId> < version> 2.7.2< /version> < /dependency> < dependency> < groupId> org.apache.hadoop< /groupId> < artifactId> hadoop-hdfs< /artifactId> < version> 2.7.2< /version> < /dependency> < dependency> < groupId> junit< /groupId> < artifactId> junit< /artifactId> < version> RELEASE< /version> < scope> compile< /scope> < /dependency> < /dependencies>

5.编写Mapper

package wordcount_hdfs; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; import java.io.IOException; public class WordCountMapper extends Mapper< LongWritable, Text, Text, IntWritable> {//0. 将创建对象的操作提取成变量，防止在 map 方法重复创建 private Text text = new Text(); private IntWritable i = new IntWritable(1); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 1. 将 Hadoop 内置的text 数据类型转换为string类型 // 方便操作 String string = value.toString(); // 2. 对字符串进行切分 String[] split = string.split(" "); // 3. 对字符串数组遍历，将单词映射成（单词，1） for (String s : split) { text.set(s); context.write(text, i); }} }

6.编写Reducer

package wordcount_hdfs; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; import java.io.IOException; public class WordCountReducer extends Reducer< Text, IntWritable,Text,IntWritable> {private IntWritable total= new IntWritable(); @Override protected void reduce(Text key, Iterable< IntWritable> values, Context context) throws IOException, InterruptedException {// 定义一个 sum，用来对每个键值对的值做累加操作 int sum = 0; for (IntWritable value : values) { int i = value.get(); sum+=i; } total.set(sum); // 最后写出到文件 context.write(key, total); } }

7.编写Driver

package wordcount_hdfs; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.KeyValueLineRecordReader; import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import java.io.IOException; public class WordCountDriver {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {// 0 指定路径这里路径有两种写法： args = new String[]{"E:\\\\Hadoop\\\\src\\\\main\\\\resources\\\\input", "E:\\\\Hadoop\\\\src\\\\main\\\\resources\\\\ouput"}; //args = new String[]{"E:/Hadoop/src/main/resources/", "E:/Hadoop/src/main/resources/"}; // 1 获取配置信息configuration以及封装任务job Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); // 2 设置Driver加载路径 setJarByClass job.setJarByClass(WordCountDriver.class); // 3 设置map和reduce类 setMaper setReducer job.setMapperClass(WordCountMapper.class); job.setReducerClass(WordCountReducer.class); // 4 设置map输出setmapoutputkeysetmapoutputvalue job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); // 5 设置最终输出kv类型 (reducer的输出kv类型) setoutoutkeysetoutputvalue job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); // 6 设置本地的输入和输出路径fileinputformat.setinputpath FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // 7 提交 boolean completion = job.waitForCompletion(true); System.exit(completion ? 0 : 1); } }

8.运行结果

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出现如下所示就欧克，接着看结果

"D:\\Program Files\\Java\\bin\\java.exe" "-javaagent:D:\\office\\Program Files\\IntelliJ2018.2.6\\lib\\idea_rt.jar=53527:D:\\office\\Program Files\\IntelliJ2018.2.6\\bin" -Dfile.encoding=UTF-8 -classpath "D:\\Program Files\\Java\\jre\\lib\\charsets.jar; D:\\Program Files\\Java\\jre\\lib\\deploy.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\access-bridge-64.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\cldrdata.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\dnsns.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\jaccess.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\jfxrt.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\localedata.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\nashorn.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\sunec.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\sunjce_provider.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\sunmscapi.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\sunpkcs11.jar; D:\\Program Files\\Java\\jre\\lib\\ext\\zipfs.jar; D:\\Program Files\\Java\\jre\\lib\\javaws.jar; 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C:\\Users\\Administrator\\.m2\\repository\\org\\apache\\hadoop\\hadoop-yarn-common\\2.7.2\\hadoop-yarn-common-2.7.2.jar; C:\\Users\\Administrator\\.m2\\repository\\com\\sun\\jersey\\jersey-client\\1.9\\jersey-client-1.9.jar; C:\\Users\\Administrator\\.m2\\repository\\org\\apache\\hadoop\\hadoop-mapreduce-client-jobclient\\2.7.2\\hadoop-mapreduce-client-jobclient-2.7.2.jar; C:\\Users\\Administrator\\.m2\\repository\\org\\apache\\hadoop\\hadoop-hdfs\\2.7.2\\hadoop-hdfs-2.7.2.jar; C:\\Users\\Administrator\\.m2\\repository\\commons-daemon\\commons-daemon\\1.0.13\\commons-daemon-1.0.13.jar; C:\\Users\\Administrator\\.m2\\repository\\io\\netty\\netty\\3.6.2.Final\\netty-3.6.2.Final.jar; C:\\Users\\Administrator\\.m2\\repository\\io\\netty\\netty-all\\4.0.23.Final\\netty-all-4.0.23.Final.jar; C:\\Users\\Administrator\\.m2\\repository\\xerces\\xercesImpl\\2.9.1\\xercesImpl-2.9.1.jar; C:\\Users\\Administrator\\.m2\\repository\\xml-apis\\xml-apis\\1.3.04\\xml-apis-1.3.04.jar; C:\\Users\\Administrator\\.m2\\repository\\org\\fusesource\\leveldbjni\\leveldbjni-all\\1.8\\leveldbjni-all-1.8.jar" KVText.KVTextDriver 2020-10-21 14:41:01,541 INFO [org.apache.hadoop.conf.Configuration.deprecation] - session.id is deprecated. Instead, use dfs.metrics.session-id 2020-10-21 14:41:01,551 INFO [org.apache.hadoop.metrics.jvm.JvmMetrics] - Initializing JVM Metrics with processName=JobTracker, sessionId= 2020-10-21 14:41:02,916 WARN [org.apache.hadoop.mapreduce.JobResourceUploader] - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this. 2020-10-21 14:41:02,936 WARN [org.apache.hadoop.mapreduce.JobResourceUploader] - No job jar file set.User classes may not be found. See Job or Job#setJar(String). 2020-10-21 14:41:03,236 INFO [org.apache.hadoop.mapreduce.lib.input.FileInputFormat] - Total input paths to process : 1 2020-10-21 14:41:03,256 INFO [org.apache.hadoop.mapreduce.JobSubmitter] - number of splits:1 2020-10-21 14:41:03,326 INFO [org.apache.hadoop.mapreduce.JobSubmitter] - Submitting tokens for job: job_local297471183_0001 2020-10-21 14:41:03,476 INFO [org.apache.hadoop.mapreduce.Job] - The url to track the job: http://localhost:8080/ 2020-10-21 14:41:03,476 INFO [org.apache.hadoop.mapreduce.Job] - Running job: job_local297471183_0001 2020-10-21 14:41:03,476 INFO [org.apache.hadoop.mapred.LocalJobRunner] - OutputCommitter set in config null 2020-10-21 14:41:03,486 INFO [org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter] - File Output Committer Algorithm version is 1 2020-10-21 14:41:03,486 INFO [org.apache.hadoop.mapred.LocalJobRunner] - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter 2020-10-21 14:41:03,536 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Waiting for map tasks 2020-10-21 14:41:03,536 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Starting task: attempt_local297471183_0001_m_000000_0 2020-10-21 14:41:03,566 INFO [org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter] - File Output Committer Algorithm version is 1 2020-10-21 14:41:03,576 INFO [org.apache.hadoop.yarn.util.ProcfsBasedProcessTree] - ProcfsBasedProcessTree currently is supported only on Linux. 2020-10-21 14:41:03,616 INFO [org.apache.hadoop.mapred.Task] -Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@6cec6fbe 2020-10-21 14:41:03,626 INFO [org.apache.hadoop.mapred.MapTask] - Processing split: file:/E:/Hadoop/src/main/resources/input/englishconment.txt:0+80 2020-10-21 14:41:03,666 INFO [org.apache.hadoop.mapred.MapTask] - (EQUATOR) 0 kvi 26214396(104857584) 2020-10-21 14:41:03,666 INFO [org.apache.hadoop.mapred.MapTask] - mapreduce.task.io.sort.mb: 100 2020-10-21 14:41:03,666 INFO [org.apache.hadoop.mapred.MapTask] - soft limit at 83886080 2020-10-21 14:41:03,666 INFO [org.apache.hadoop.mapred.MapTask] - bufstart = 0; bufvoid = 104857600 2020-10-21 14:41:03,666 INFO [org.apache.hadoop.mapred.MapTask] - kvstart = 26214396; length = 6553600 2020-10-21 14:41:03,666 INFO [org.apache.hadoop.mapred.MapTask] - Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer 2020-10-21 14:41:03,676 INFO [org.apache.hadoop.mapred.LocalJobRunner] - 2020-10-21 14:41:03,676 INFO [org.apache.hadoop.mapred.MapTask] - Starting flush of map output 2020-10-21 14:41:03,676 INFO [org.apache.hadoop.mapred.MapTask] - Spilling map output 2020-10-21 14:41:03,676 INFO [org.apache.hadoop.mapred.MapTask] - bufstart = 0; bufend = 48; bufvoid = 104857600 2020-10-21 14:41:03,676 INFO [org.apache.hadoop.mapred.MapTask] - kvstart = 26214396(104857584); kvend = 26214384(104857536); length = 13/6553600 2020-10-21 14:41:03,796 INFO [org.apache.hadoop.mapred.MapTask] - Finished spill 0 2020-10-21 14:41:03,806 INFO [org.apache.hadoop.mapred.Task] - Task:attempt_local297471183_0001_m_000000_0 is done. And is in the process of committing 2020-10-21 14:41:03,826 INFO [org.apache.hadoop.mapred.LocalJobRunner] - file:/E:/Hadoop/src/main/resources/input/englishconment.txt:0+80 2020-10-21 14:41:03,826 INFO [org.apache.hadoop.mapred.Task] - Task \'attempt_local297471183_0001_m_000000_0\' done. 2020-10-21 14:41:03,826 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Finishing task: attempt_local297471183_0001_m_000000_0 2020-10-21 14:41:03,826 INFO [org.apache.hadoop.mapred.LocalJobRunner] - map task executor complete. 2020-10-21 14:41:03,826 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Waiting for reduce tasks 2020-10-21 14:41:03,826 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Starting task: attempt_local297471183_0001_r_000000_0 2020-10-21 14:41:03,836 INFO [org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter] - File Output Committer Algorithm version is 1 2020-10-21 14:41:03,836 INFO [org.apache.hadoop.yarn.util.ProcfsBasedProcessTree] - ProcfsBasedProcessTree currently is supported only on Linux. 2020-10-21 14:41:04,115 INFO [org.apache.hadoop.mapred.Task] -Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@2f6f6193 2020-10-21 14:41:04,115 INFO [org.apache.hadoop.mapred.ReduceTask] - Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@15648f8c 2020-10-21 14:41:04,135 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - MergerManager: memoryLimit=648858816, maxSingleShuffleLimit=162214704, mergeThreshold=428246848, iosortFactor=10, memToMemMergeOutputsThreshold=10 2020-10-21 14:41:04,135 INFO [org.apache.hadoop.mapreduce.task.reduce.EventFetcher] - attempt_local297471183_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events 2020-10-21 14:41:04,165 INFO [org.apache.hadoop.mapreduce.task.reduce.LocalFetcher] - localfetcher#1 about to shuffle output of map attempt_local297471183_0001_m_000000_0 decomp: 58 len: 62 to MEMORY 2020-10-21 14:41:04,175 INFO [org.apache.hadoop.mapreduce.task.reduce.InMemoryMapOutput] - Read 58 bytes from map-output for attempt_local297471183_0001_m_000000_0 2020-10-21 14:41:04,175 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - closeInMemoryFile -> map-output of size: 58, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory -> 58 2020-10-21 14:41:04,175 INFO [org.apache.hadoop.mapreduce.task.reduce.EventFetcher] - EventFetcher is interrupted.. Returning 2020-10-21 14:41:04,175 INFO [org.apache.hadoop.mapred.LocalJobRunner] - 1 / 1 copied. 2020-10-21 14:41:04,175 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs 2020-10-21 14:41:04,195 INFO [org.apache.hadoop.mapred.Merger] - Merging 1 sorted segments 2020-10-21 14:41:04,195 INFO [org.apache.hadoop.mapred.Merger] - Down to the last merge-pass, with 1 segments left of total size: 47 bytes 2020-10-21 14:41:04,205 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - Merged 1 segments, 58 bytes to disk to satisfy reduce memory limit 2020-10-21 14:41:04,215 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - Merging 1 files, 62 bytes from disk 2020-10-21 14:41:04,215 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - Merging 0 segments, 0 bytes from memory into reduce 2020-10-21 14:41:04,215 INFO [org.apache.hadoop.mapred.Merger] - Merging 1 sorted segments 2020-10-21 14:41:04,215 INFO [org.apache.hadoop.mapred.Merger] - Down to the last merge-pass, with 1 segments left of total size: 47 bytes 2020-10-21 14:41:04,215 INFO [org.apache.hadoop.mapred.LocalJobRunner] - 1 / 1 copied. 2020-10-21 14:41:04,215 INFO [org.apache.hadoop.conf.Configuration.deprecation] - mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords 2020-10-21 14:41:04,225 INFO [org.apache.hadoop.mapred.Task] - Task:attempt_local297471183_0001_r_000000_0 is done. And is in the process of committing 2020-10-21 14:41:04,225 INFO [org.apache.hadoop.mapred.LocalJobRunner] - 1 / 1 copied. 2020-10-21 14:41:04,225 INFO [org.apache.hadoop.mapred.Task] - Task attempt_local297471183_0001_r_000000_0 is allowed to commit now 2020-10-21 14:41:04,235 INFO [org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter] - Saved output of task \'attempt_local297471183_0001_r_000000_0\' to file:/E:/Hadoop/src/main/resources/ouput/_temporary/0/task_local297471183_0001_r_000000 2020-10-21 14:41:04,235 INFO [org.apache.hadoop.mapred.LocalJobRunner] - reduce > reduce 2020-10-21 14:41:04,235 INFO [org.apache.hadoop.mapred.Task] - Task \'attempt_local297471183_0001_r_000000_0\' done. 2020-10-21 14:41:04,235 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Finishing task: attempt_local297471183_0001_r_000000_0 2020-10-21 14:41:04,235 INFO [org.apache.hadoop.mapred.LocalJobRunner] - reduce task executor complete. 2020-10-21 14:41:04,485 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local297471183_0001 running in uber mode : false 2020-10-21 14:41:04,485 INFO [org.apache.hadoop.mapreduce.Job] -map 100% reduce 100% 2020-10-21 14:41:04,485 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local297471183_0001 completed successfully 2020-10-21 14:41:04,515 INFO [org.apache.hadoop.mapreduce.Job] - Counters: 30 File System Counters FILE: Number of bytes read=672 FILE: Number of bytes written=583822 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 Map-Reduce Framework Map input records=4 Map output records=4 Map output bytes=48 Map output materialized bytes=62 Input split bytes=124 Combine input records=0 Combine output records=0 Reduce input groups=2 Reduce shuffle bytes=62 Reduce input records=4 Reduce output records=2 Spilled Records=8 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=12 Total committed heap usage (bytes)=374865920 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=80 File Output Format Counters Bytes Written=32Process finished with exit code 0