从WordCount了解MapReduce的运行机制

淡淡的烟草味﹌ 2022-01-14 14:57 274阅读 0赞

### 前言 ###

大数据环境CDH已搭建完毕，第一个MapReduce程序wordcount也已成功运行，接下来我们需要通过WordCount来了解MapReduce的运行机制。

如何大致了解MapReduce的运行机制呢？有两点，（1）**源码**，对一个搞开发的人来说，我都有你MapReduce所有相关的源码jar包，有你所有的类文件，还能不知道你是怎么执行的吗？当然，要具备一定的源码阅读调试经验和编码功底；（2）**运行日志**，日志真的是特别重要的东西，它详细地记录了程序的运行流程，当然，前提是必须将有用的运行信息都打印出来，基本上没有分析日志解决不了的问题，如果有，那就多打几行。

### 改造源码 ###

所以，我们在针对WordCount.java中相关的类做了一些源码阅读的认识后，需要稍微改造一下WordCount.java，即为其map、combine和reduce函数添加一些必要的日志输出，当然在实际的过程中，随着对MR的认识逐渐全面，其实是一部分一部分添加的。另外，为了区分开combine和reduce的日志，我们将*job.setCombinerClass(IntSumCombiner.class)*替换为*job.setCombinerClass(IntSumReducer.class)*，而*IntSumCombiner*和*IntSumReducer*的区别仅在于类名的不同。如下贴出我在微整后的WordCount：

package com.szh.hadoop;
    
    import java.io.File;
    import java.io.IOException;
    import java.net.InetAddress;
    import java.util.Arrays;
    import java.util.StringTokenizer;
    
    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.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.apache.hadoop.util.GenericOptionsParser;
    import org.slf4j.Logger;
    import org.slf4j.LoggerFactory;
    
    public class WordCount {
    
        protected static final Logger LOG = LoggerFactory.getLogger(WordCount.class);
    
        @SuppressWarnings("deprecation")
        public static void main(String[] args) throws Exception {
            LOG.info("main方法执行在机器: {}上", InetAddress.getLocalHost());
            LOG.info("Args: " + Arrays.toString(args));
            Configuration conf = new Configuration();
            // instead of mapred.jar(deprecated)
            conf.set("mapreduce.job.jar", System.getProperty("user.dir") + File.separator + "wc.jar");
            LOG.info("当前工作目录: {}", System.getProperty("user.dir"));
            File jarFile = new File(conf.get("mapreduce.job.jar"));
            LOG.info("Jar to run: {}, {}, {}", jarFile.getAbsolutePath(), jarFile.isFile(), jarFile.exists());
            String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
            if (otherArgs.length < 2) {
                LOG.error("Usage: wordcount <in> [<in>...] <out>");
                System.exit(2);
            }
            Job job = new Job(conf, "szh's word count 20190613");
            job.setJarByClass(WordCount.class);
            job.setMapperClass(TokenizerMapper.class);
            job.setCombinerClass(IntSumCombiner.class);
            job.setReducerClass(IntSumReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            for (int i = 0; i < otherArgs.length - 1; i++) {
                FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
                LOG.info("Input path: {}", otherArgs[i]);
            }
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[(otherArgs.length - 1)]));
            LOG.info("Output path: {}", otherArgs[(otherArgs.length - 1)]);
    
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        }
    
        public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
            private static final IntWritable one = new IntWritable(1);
            private Text word = new Text();
            
            public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context)
                    throws IOException, InterruptedException {
                LOG.info("map方法执行在机器: {}上", InetAddress.getLocalHost());
                LOG.info("源文本数据（map输入数据）: {} : {}", key, value);
                StringTokenizer itr = new StringTokenizer(value.toString());
                while (itr.hasMoreTokens()) {
                    this.word.set(itr.nextToken());
                    context.write(this.word, one);
                    LOG.info("\tmap循环: <{},{}>", word, one);
                }
                LOG.info("本次map结束！");
            }
        }
    
        public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
            private IntWritable result = new IntWritable();
    
            public void reduce(Text key, Iterable<IntWritable> values,
                    Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                    throws IOException, InterruptedException {
                LOG.info("reduce方法执行在机器: {}上", InetAddress.getLocalHost());
                LOG.info("reduce输入数据: {},{}", key, values);
                int sum = 0;
                for (IntWritable val : values) {
                    sum += val.get();
                    LOG.info("\treduce循环: <{},{}>", key, val.get());
                }
                this.result.set(sum);
                context.write(key, this.result);
                LOG.info("本次reduce结束: <{},{}>", key, result);
            }
        }
    
        public static class IntSumCombiner extends Reducer<Text, IntWritable, Text, IntWritable> {
            private IntWritable result = new IntWritable();
    
            public void reduce(Text key, Iterable<IntWritable> values,
                    Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                    throws IOException, InterruptedException {
                LOG.info("combine方法执行在机器: {}上", InetAddress.getLocalHost());
                LOG.info("combine输入数据: {},{}", key, values);
                int sum = 0;
                for (IntWritable val : values) {
                    sum += val.get();
                    LOG.info("\tcombine循环: <{},{}>", key, val.get());
                }
                this.result.set(sum);
                context.write(key, this.result);
                LOG.info("本次combine结束: <{},{}>", key, result);
            }
        }
    }

如想对比其与hadoop自带WordCount的区别，可自行反编译或去[https://blog.csdn.net/songzehao/article/details/91560692][https_blog.csdn.net_songzehao_article_details_91560692]复制查看即可。另外，附上我的源文本数据：

[root@bi-zhaopeng04 ~]# hadoop fs -cat /tmp/songzehao/words_input
    szh dk tyn cj cj zp zp szh dk szh dk dk dk tyn
    szh dk tyn cj cj zp zp szh dk szh dk dk dk tyn

### 运行获取日志 ###

该加的日志输出都已添加完毕，接下来就该运行一把了，最后获取相应日志，并结合理论知识指导进行分析总结。

运行步骤在此不做赘述，有问题可参考[https://blog.csdn.net/songzehao/article/details/91560692][https_blog.csdn.net_songzehao_article_details_91560692]，那么，本次运行相关的日志从何获取？

很显然，在MR执行过程中，控制台上会输出一些日志，我们姑且称其为**console.log**，如下：

19/06/13 16:20:06 INFO hadoop.WordCount: main方法执行在机器: bi-zhaopeng04/10.1.4.19上
    19/06/13 16:20:06 INFO hadoop.WordCount: Args: [/tmp/songzehao/words_input, /tmp/songzehao/words_output]
    19/06/13 16:20:06 INFO hadoop.WordCount: 当前工作目录: /opt/szh
    19/06/13 16:20:06 INFO hadoop.WordCount: Jar to run: /opt/szh/wc.jar, false, false
    19/06/13 16:20:06 INFO hadoop.WordCount: Input path: /tmp/songzehao/words_input
    19/06/13 16:20:06 INFO hadoop.WordCount: Output path: /tmp/songzehao/words_output
    19/06/13 16:20:06 INFO client.RMProxy: Connecting to ResourceManager at bi-zhaopeng03/10.1.4.18:8032
    19/06/13 16:20:07 INFO input.FileInputFormat: Total input paths to process : 1
    19/06/13 16:20:07 INFO mapreduce.JobSubmitter: number of splits:1
    19/06/13 16:20:07 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1559721784881_0053
    19/06/13 16:20:07 INFO impl.YarnClientImpl: Submitted application application_1559721784881_0053
    19/06/13 16:20:07 INFO mapreduce.Job: The url to track the job: http://bi-zhaopeng03:8088/proxy/application_1559721784881_0053/
    19/06/13 16:20:07 INFO mapreduce.Job: Running job: job_1559721784881_0053
    19/06/13 16:20:11 INFO mapreduce.Job: Job job_1559721784881_0053 running in uber mode : false
    19/06/13 16:20:11 INFO mapreduce.Job:  map 0% reduce 0%
    19/06/13 16:20:15 INFO mapreduce.Job:  map 100% reduce 0%
    19/06/13 16:20:19 INFO mapreduce.Job:  map 100% reduce 13%
    19/06/13 16:20:22 INFO mapreduce.Job:  map 100% reduce 25%
    19/06/13 16:20:26 INFO mapreduce.Job:  map 100% reduce 38%
    19/06/13 16:20:29 INFO mapreduce.Job:  map 100% reduce 50%
    19/06/13 16:20:32 INFO mapreduce.Job:  map 100% reduce 63%
    19/06/13 16:20:35 INFO mapreduce.Job:  map 100% reduce 75%
    19/06/13 16:20:38 INFO mapreduce.Job:  map 100% reduce 88%
    19/06/13 16:20:41 INFO mapreduce.Job:  map 100% reduce 100%
    19/06/13 16:20:41 INFO mapreduce.Job: Job job_1559721784881_0053 completed successfully
    19/06/13 16:20:41 INFO mapreduce.Job: Counters: 49
            File System Counters
                    FILE: Number of bytes read=367
                    FILE: Number of bytes written=2019909
                    FILE: Number of read operations=0
                    FILE: Number of large read operations=0
                    FILE: Number of write operations=0
                    HDFS: Number of bytes read=210
                    HDFS: Number of bytes written=28
                    HDFS: Number of read operations=51
                    HDFS: Number of large read operations=0
                    HDFS: Number of write operations=32
            Job Counters 
                    Launched map tasks=1
                    Launched reduce tasks=16
                    Data-local map tasks=1
                    Total time spent by all maps in occupied slots (ms)=1731
                    Total time spent by all reduces in occupied slots (ms)=28579
                    Total time spent by all map tasks (ms)=1731
                    Total time spent by all reduce tasks (ms)=28579
                    Total vcore-seconds taken by all map tasks=1731
                    Total vcore-seconds taken by all reduce tasks=28579
                    Total megabyte-seconds taken by all map tasks=1772544
                    Total megabyte-seconds taken by all reduce tasks=29264896
            Map-Reduce Framework
                    Map input records=2
                    Map output records=28
                    Map output bytes=206
                    Map output materialized bytes=303
                    Input split bytes=116
                    Combine input records=28
                    Combine output records=5
                    Reduce input groups=5
                    Reduce shuffle bytes=303
                    Reduce input records=5
                    Reduce output records=5
                    Spilled Records=10
                    Shuffled Maps =16
                    Failed Shuffles=0
                    Merged Map outputs=16
                    GC time elapsed (ms)=863
                    CPU time spent (ms)=12960
                    Physical memory (bytes) snapshot=3826233344
                    Virtual memory (bytes) snapshot=48430043136
                    Total committed heap usage (bytes)=3508535296
            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=94
            File Output Format Counters 
                    Bytes Written=28

控制台上确实有日志打印，但是细看只有main方法的日志输出，map、combine和reduce各自的日志跑去哪里了？可访问yarn的web管理地址（masterIp:8088），找到对应应用程序ApplicationId（比如，通过查看console.log，得知我的ApplicationId是application\_1559721784881\_0053）的History，界面如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70][]

再分别点进各MapTask和各ReduceTask里获取完整日志，因为我的ReduceTask中只有3个有输出（task\_1559721784881\_0053\_r\_000000，task\_1559721784881\_0053\_r\_000007，task\_1559721784881\_0053\_r\_000008），所以如下贴出**map.log**和3+1个**reduce.log**，其中多出的那1个，是13个空跑的reduce中的任意一个代表：

map.log:
    2019-06-13 16:20:13,286 INFO [main] org.apache.hadoop.metrics2.impl.MetricsConfig: loaded properties from hadoop-metrics2.properties
    2019-06-13 16:20:13,344 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Scheduled snapshot period at 10 second(s).
    2019-06-13 16:20:13,344 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: MapTask metrics system started
    2019-06-13 16:20:13,351 INFO [main] org.apache.hadoop.mapred.YarnChild: Executing with tokens:
    2019-06-13 16:20:13,351 INFO [main] org.apache.hadoop.mapred.YarnChild: Kind: mapreduce.job, Service: job_1559721784881_0053, Ident: (org.apache.hadoop.mapreduce.security.token.JobTokenIdentifier@59309333)
    2019-06-13 16:20:13,407 INFO [main] org.apache.hadoop.mapred.YarnChild: Sleeping for 0ms before retrying again. Got null now.
    2019-06-13 16:20:13,591 INFO [main] org.apache.hadoop.mapred.YarnChild: mapreduce.cluster.local.dir for child: /yarn/nm/usercache/hdfs/appcache/application_1559721784881_0053
    2019-06-13 16:20:13,726 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
    2019-06-13 16:20:14,050 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: File Output Committer Algorithm version is 1
    2019-06-13 16:20:14,060 INFO [main] org.apache.hadoop.mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
    2019-06-13 16:20:14,177 INFO [main] org.apache.hadoop.mapred.MapTask: Processing split: hdfs://bi-zhaopeng03:8020/tmp/songzehao/words_input:0+94
    2019-06-13 16:20:14,249 INFO [main] org.apache.hadoop.mapred.MapTask: (EQUATOR) 0 kvi 67108860(268435440)
    2019-06-13 16:20:14,250 INFO [main] org.apache.hadoop.mapred.MapTask: mapreduce.task.io.sort.mb: 256
    2019-06-13 16:20:14,250 INFO [main] org.apache.hadoop.mapred.MapTask: soft limit at 214748368
    2019-06-13 16:20:14,250 INFO [main] org.apache.hadoop.mapred.MapTask: bufstart = 0; bufvoid = 268435456
    2019-06-13 16:20:14,250 INFO [main] org.apache.hadoop.mapred.MapTask: kvstart = 67108860; length = 16777216
    2019-06-13 16:20:14,255 INFO [main] org.apache.hadoop.mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: map方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 源文本数据（map输入数据）: 0 : szh dk tyn cj cj zp zp szh dk szh dk dk dk tyn
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <szh,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <tyn,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <cj,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <cj,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <zp,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <zp,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <szh,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <szh,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,267 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <tyn,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 本次map结束！
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: map方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 源文本数据（map输入数据）: 47 : szh dk tyn cj cj zp zp szh dk szh dk dk dk tyn
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <szh,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <tyn,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <cj,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <cj,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <zp,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <zp,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <szh,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <szh,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <dk,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 	map循环: <tyn,1>
    2019-06-13 16:20:14,268 INFO [main] com.szh.hadoop.WordCount: 本次map结束！
    2019-06-13 16:20:14,275 INFO [main] org.apache.hadoop.mapred.MapTask: Starting flush of map output
    2019-06-13 16:20:14,275 INFO [main] org.apache.hadoop.mapred.MapTask: Spilling map output
    2019-06-13 16:20:14,275 INFO [main] org.apache.hadoop.mapred.MapTask: bufstart = 0; bufend = 206; bufvoid = 268435456
    2019-06-13 16:20:14,275 INFO [main] org.apache.hadoop.mapred.MapTask: kvstart = 67108860(268435440); kvend = 67108752(268435008); length = 109/16777216
    2019-06-13 16:20:14,283 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new compressor [.snappy]
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: combine方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: combine输入数据: szh,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@2ccca26f
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <szh,1>
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <szh,1>
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <szh,1>
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <szh,1>
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <szh,1>
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <szh,1>
    2019-06-13 16:20:14,290 INFO [main] com.szh.hadoop.WordCount: 本次combine结束: <szh,6>
    2019-06-13 16:20:14,292 INFO [main] com.szh.hadoop.WordCount: combine方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:14,292 INFO [main] com.szh.hadoop.WordCount: combine输入数据: zp,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@3542162a
    2019-06-13 16:20:14,292 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <zp,1>
    2019-06-13 16:20:14,292 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <zp,1>
    2019-06-13 16:20:14,292 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <zp,1>
    2019-06-13 16:20:14,292 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <zp,1>
    2019-06-13 16:20:14,292 INFO [main] com.szh.hadoop.WordCount: 本次combine结束: <zp,4>
    2019-06-13 16:20:14,293 INFO [main] com.szh.hadoop.WordCount: combine方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:14,293 INFO [main] com.szh.hadoop.WordCount: combine输入数据: cj,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@698122b2
    2019-06-13 16:20:14,293 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <cj,1>
    2019-06-13 16:20:14,293 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <cj,1>
    2019-06-13 16:20:14,293 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <cj,1>
    2019-06-13 16:20:14,293 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <cj,1>
    2019-06-13 16:20:14,293 INFO [main] com.szh.hadoop.WordCount: 本次combine结束: <cj,4>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: combine方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: combine输入数据: dk,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@698122b2
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <dk,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 本次combine结束: <dk,10>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: combine方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: combine输入数据: tyn,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@698122b2
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <tyn,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <tyn,1>
    2019-06-13 16:20:14,294 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <tyn,1>
    2019-06-13 16:20:14,295 INFO [main] com.szh.hadoop.WordCount: 	combine循环: <tyn,1>
    2019-06-13 16:20:14,295 INFO [main] com.szh.hadoop.WordCount: 本次combine结束: <tyn,4>
    2019-06-13 16:20:14,296 INFO [main] org.apache.hadoop.mapred.MapTask: Finished spill 0
    2019-06-13 16:20:14,299 INFO [main] org.apache.hadoop.mapred.Task: Task:attempt_1559721784881_0053_m_000000_0 is done. And is in the process of committing
    2019-06-13 16:20:14,353 INFO [main] org.apache.hadoop.mapred.Task: Task 'attempt_1559721784881_0053_m_000000_0' done.
    2019-06-13 16:20:14,454 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Stopping MapTask metrics system...
    2019-06-13 16:20:14,454 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: MapTask metrics system stopped.
    2019-06-13 16:20:14,454 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: MapTask metrics system shutdown complete.

reduce-00000.log:
    2019-06-13 16:20:17,153 INFO [main] org.apache.hadoop.metrics2.impl.MetricsConfig: loaded properties from hadoop-metrics2.properties
    2019-06-13 16:20:17,210 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Scheduled snapshot period at 10 second(s).
    2019-06-13 16:20:17,210 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system started
    2019-06-13 16:20:17,217 INFO [main] org.apache.hadoop.mapred.YarnChild: Executing with tokens:
    2019-06-13 16:20:17,217 INFO [main] org.apache.hadoop.mapred.YarnChild: Kind: mapreduce.job, Service: job_1559721784881_0053, Ident: (org.apache.hadoop.mapreduce.security.token.JobTokenIdentifier@59309333)
    2019-06-13 16:20:17,273 INFO [main] org.apache.hadoop.mapred.YarnChild: Sleeping for 0ms before retrying again. Got null now.
    2019-06-13 16:20:17,454 INFO [main] org.apache.hadoop.mapred.YarnChild: mapreduce.cluster.local.dir for child: /yarn/nm/usercache/hdfs/appcache/application_1559721784881_0053
    2019-06-13 16:20:17,595 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
    2019-06-13 16:20:17,922 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: File Output Committer Algorithm version is 1
    2019-06-13 16:20:17,932 INFO [main] org.apache.hadoop.mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
    2019-06-13 16:20:17,964 INFO [main] org.apache.hadoop.mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1a760689
    2019-06-13 16:20:17,978 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: MergerManager: memoryLimit=535088320, maxSingleShuffleLimit=133772080, mergeThreshold=353158304, ioSortFactor=64, memToMemMergeOutputsThreshold=64
    2019-06-13 16:20:17,980 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
    2019-06-13 16:20:17,987 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000000_0: Got 1 new map-outputs
    2019-06-13 16:20:17,987 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: Assigning bi-zhaopeng05:13562 with 1 to fetcher#10
    2019-06-13 16:20:17,988 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: assigned 1 of 1 to bi-zhaopeng05:13562 to fetcher#10
    2019-06-13 16:20:18,088 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.Fetcher: for url=13562/mapOutput?job=job_1559721784881_0053&reduce=0&map=attempt_1559721784881_0053_m_000000_0 sent hash and received reply
    2019-06-13 16:20:18,094 INFO [fetcher#10] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
    2019-06-13 16:20:18,097 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.Fetcher: fetcher#10 about to shuffle output of map attempt_1559721784881_0053_m_000000_0 decomp: 12 len: 26 to MEMORY
    2019-06-13 16:20:18,099 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.InMemoryMapOutput: Read 12 bytes from map-output for attempt_1559721784881_0053_m_000000_0
    2019-06-13 16:20:18,100 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 12, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->12
    2019-06-13 16:20:18,101 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: EventFetcher is interrupted.. Returning
    2019-06-13 16:20:18,101 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: bi-zhaopeng05:13562 freed by fetcher#10 in 113ms
    2019-06-13 16:20:18,105 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
    2019-06-13 16:20:18,110 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:18,110 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 6 bytes
    2019-06-13 16:20:18,112 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new compressor [.snappy]
    2019-06-13 16:20:18,115 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merged 1 segments, 12 bytes to disk to satisfy reduce memory limit
    2019-06-13 16:20:18,115 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 1 files, 30 bytes from disk
    2019-06-13 16:20:18,116 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
    2019-06-13 16:20:18,116 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:18,118 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 6 bytes
    2019-06-13 16:20:18,187 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
    2019-06-13 16:20:18,194 INFO [main] com.szh.hadoop.WordCount: reduce方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:18,194 INFO [main] com.szh.hadoop.WordCount: reduce输入数据: szh,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@4218500f
    2019-06-13 16:20:18,194 INFO [main] com.szh.hadoop.WordCount: 	reduce循环: <szh,6>
    2019-06-13 16:20:18,195 INFO [main] com.szh.hadoop.WordCount: 本次reduce结束: <szh,6>
    2019-06-13 16:20:18,299 INFO [main] org.apache.hadoop.mapred.Task: Task:attempt_1559721784881_0053_r_000000_0 is done. And is in the process of committing
    2019-06-13 16:20:18,327 INFO [main] org.apache.hadoop.mapred.Task: Task attempt_1559721784881_0053_r_000000_0 is allowed to commit now
    2019-06-13 16:20:18,332 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: Saved output of task 'attempt_1559721784881_0053_r_000000_0' to hdfs://bi-zhaopeng03:8020/tmp/songzehao/words_output/_temporary/1/task_1559721784881_0053_r_000000
    2019-06-13 16:20:18,350 INFO [main] org.apache.hadoop.mapred.Task: Task 'attempt_1559721784881_0053_r_000000_0' done.
    2019-06-13 16:20:18,451 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Stopping ReduceTask metrics system...
    2019-06-13 16:20:18,451 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system stopped.
    2019-06-13 16:20:18,451 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system shutdown complete.

reduce-00007.log:
    2019-06-13 16:20:26,195 INFO [main] org.apache.hadoop.metrics2.impl.MetricsConfig: loaded properties from hadoop-metrics2.properties
    2019-06-13 16:20:26,255 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Scheduled snapshot period at 10 second(s).
    2019-06-13 16:20:26,255 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system started
    2019-06-13 16:20:26,263 INFO [main] org.apache.hadoop.mapred.YarnChild: Executing with tokens:
    2019-06-13 16:20:26,263 INFO [main] org.apache.hadoop.mapred.YarnChild: Kind: mapreduce.job, Service: job_1559721784881_0053, Ident: (org.apache.hadoop.mapreduce.security.token.JobTokenIdentifier@59309333)
    2019-06-13 16:20:26,320 INFO [main] org.apache.hadoop.mapred.YarnChild: Sleeping for 0ms before retrying again. Got null now.
    2019-06-13 16:20:26,497 INFO [main] org.apache.hadoop.mapred.YarnChild: mapreduce.cluster.local.dir for child: /yarn/nm/usercache/hdfs/appcache/application_1559721784881_0053
    2019-06-13 16:20:26,637 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
    2019-06-13 16:20:26,960 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: File Output Committer Algorithm version is 1
    2019-06-13 16:20:26,970 INFO [main] org.apache.hadoop.mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
    2019-06-13 16:20:26,999 INFO [main] org.apache.hadoop.mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1a760689
    2019-06-13 16:20:27,012 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: MergerManager: memoryLimit=535088320, maxSingleShuffleLimit=133772080, mergeThreshold=353158304, ioSortFactor=64, memToMemMergeOutputsThreshold=64
    2019-06-13 16:20:27,014 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000007_0 Thread started: EventFetcher for fetching Map Completion Events
    2019-06-13 16:20:27,021 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000007_0: Got 1 new map-outputs
    2019-06-13 16:20:27,021 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: Assigning bi-zhaopeng05:13562 with 1 to fetcher#10
    2019-06-13 16:20:27,021 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: assigned 1 of 1 to bi-zhaopeng05:13562 to fetcher#10
    2019-06-13 16:20:27,130 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.Fetcher: for url=13562/mapOutput?job=job_1559721784881_0053&reduce=7&map=attempt_1559721784881_0053_m_000000_0 sent hash and received reply
    2019-06-13 16:20:27,135 INFO [fetcher#10] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
    2019-06-13 16:20:27,139 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.Fetcher: fetcher#10 about to shuffle output of map attempt_1559721784881_0053_m_000000_0 decomp: 11 len: 25 to MEMORY
    2019-06-13 16:20:27,141 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.InMemoryMapOutput: Read 11 bytes from map-output for attempt_1559721784881_0053_m_000000_0
    2019-06-13 16:20:27,141 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 11, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->11
    2019-06-13 16:20:27,142 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: EventFetcher is interrupted.. Returning
    2019-06-13 16:20:27,142 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: bi-zhaopeng05:13562 freed by fetcher#10 in 120ms
    2019-06-13 16:20:27,145 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
    2019-06-13 16:20:27,151 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:27,151 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 6 bytes
    2019-06-13 16:20:27,152 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new compressor [.snappy]
    2019-06-13 16:20:27,156 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merged 1 segments, 11 bytes to disk to satisfy reduce memory limit
    2019-06-13 16:20:27,157 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 1 files, 29 bytes from disk
    2019-06-13 16:20:27,157 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
    2019-06-13 16:20:27,157 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:27,159 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 6 bytes
    2019-06-13 16:20:27,220 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
    2019-06-13 16:20:27,227 INFO [main] com.szh.hadoop.WordCount: reduce方法执行在机器: bi-zhaopeng05/10.1.4.20上
    2019-06-13 16:20:27,227 INFO [main] com.szh.hadoop.WordCount: reduce输入数据: zp,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@4218500f
    2019-06-13 16:20:27,227 INFO [main] com.szh.hadoop.WordCount: 	reduce循环: <zp,4>
    2019-06-13 16:20:27,227 INFO [main] com.szh.hadoop.WordCount: 本次reduce结束: <zp,4>
    2019-06-13 16:20:27,323 INFO [main] org.apache.hadoop.mapred.Task: Task:attempt_1559721784881_0053_r_000007_0 is done. And is in the process of committing
    2019-06-13 16:20:27,355 INFO [main] org.apache.hadoop.mapred.Task: Task attempt_1559721784881_0053_r_000007_0 is allowed to commit now
    2019-06-13 16:20:27,360 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: Saved output of task 'attempt_1559721784881_0053_r_000007_0' to hdfs://bi-zhaopeng03:8020/tmp/songzehao/words_output/_temporary/1/task_1559721784881_0053_r_000007
    2019-06-13 16:20:27,378 INFO [main] org.apache.hadoop.mapred.Task: Task 'attempt_1559721784881_0053_r_000007_0' done.
    2019-06-13 16:20:27,478 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Stopping ReduceTask metrics system...
    2019-06-13 16:20:27,479 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system stopped.
    2019-06-13 16:20:27,479 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system shutdown complete.

reduce-00008.log:
    2019-06-13 16:20:29,221 INFO [main] org.apache.hadoop.metrics2.impl.MetricsConfig: loaded properties from hadoop-metrics2.properties
    2019-06-13 16:20:29,275 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Scheduled snapshot period at 10 second(s).
    2019-06-13 16:20:29,275 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system started
    2019-06-13 16:20:29,282 INFO [main] org.apache.hadoop.mapred.YarnChild: Executing with tokens:
    2019-06-13 16:20:29,283 INFO [main] org.apache.hadoop.mapred.YarnChild: Kind: mapreduce.job, Service: job_1559721784881_0053, Ident: (org.apache.hadoop.mapreduce.security.token.JobTokenIdentifier@59309333)
    2019-06-13 16:20:29,338 INFO [main] org.apache.hadoop.mapred.YarnChild: Sleeping for 0ms before retrying again. Got null now.
    2019-06-13 16:20:29,513 INFO [main] org.apache.hadoop.mapred.YarnChild: mapreduce.cluster.local.dir for child: /yarn/nm/usercache/hdfs/appcache/application_1559721784881_0053
    2019-06-13 16:20:29,665 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
    2019-06-13 16:20:30,002 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: File Output Committer Algorithm version is 1
    2019-06-13 16:20:30,011 INFO [main] org.apache.hadoop.mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
    2019-06-13 16:20:30,043 INFO [main] org.apache.hadoop.mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1a760689
    2019-06-13 16:20:30,057 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: MergerManager: memoryLimit=535088320, maxSingleShuffleLimit=133772080, mergeThreshold=353158304, ioSortFactor=64, memToMemMergeOutputsThreshold=64
    2019-06-13 16:20:30,059 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000008_0 Thread started: EventFetcher for fetching Map Completion Events
    2019-06-13 16:20:30,068 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000008_0: Got 1 new map-outputs
    2019-06-13 16:20:30,068 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: Assigning bi-zhaopeng05:13562 with 1 to fetcher#10
    2019-06-13 16:20:30,068 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: assigned 1 of 1 to bi-zhaopeng05:13562 to fetcher#10
    2019-06-13 16:20:30,171 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.Fetcher: for url=13562/mapOutput?job=job_1559721784881_0053&reduce=8&map=attempt_1559721784881_0053_m_000000_0 sent hash and received reply
    2019-06-13 16:20:30,177 INFO [fetcher#10] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
    2019-06-13 16:20:30,180 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.Fetcher: fetcher#10 about to shuffle output of map attempt_1559721784881_0053_m_000000_0 decomp: 30 len: 44 to MEMORY
    2019-06-13 16:20:30,182 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.InMemoryMapOutput: Read 30 bytes from map-output for attempt_1559721784881_0053_m_000000_0
    2019-06-13 16:20:30,183 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 30, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->30
    2019-06-13 16:20:30,183 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: EventFetcher is interrupted.. Returning
    2019-06-13 16:20:30,184 INFO [fetcher#10] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: bi-zhaopeng05:13562 freed by fetcher#10 in 116ms
    2019-06-13 16:20:30,187 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
    2019-06-13 16:20:30,192 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:30,192 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 25 bytes
    2019-06-13 16:20:30,193 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new compressor [.snappy]
    2019-06-13 16:20:30,197 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merged 1 segments, 30 bytes to disk to satisfy reduce memory limit
    2019-06-13 16:20:30,197 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 1 files, 48 bytes from disk
    2019-06-13 16:20:30,197 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
    2019-06-13 16:20:30,197 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:30,199 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 25 bytes
    2019-06-13 16:20:30,259 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
    2019-06-13 16:20:30,266 INFO [main] com.szh.hadoop.WordCount: reduce方法执行在机器: bi-zhaopeng03/10.1.4.18上
    2019-06-13 16:20:30,266 INFO [main] com.szh.hadoop.WordCount: reduce输入数据: cj,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@4218500f
    2019-06-13 16:20:30,266 INFO [main] com.szh.hadoop.WordCount: 	reduce循环: <cj,4>
    2019-06-13 16:20:30,266 INFO [main] com.szh.hadoop.WordCount: 本次reduce结束: <cj,4>
    2019-06-13 16:20:30,266 INFO [main] com.szh.hadoop.WordCount: reduce方法执行在机器: bi-zhaopeng03/10.1.4.18上
    2019-06-13 16:20:30,266 INFO [main] com.szh.hadoop.WordCount: reduce输入数据: dk,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@4218500f
    2019-06-13 16:20:30,266 INFO [main] com.szh.hadoop.WordCount: 	reduce循环: <dk,10>
    2019-06-13 16:20:30,267 INFO [main] com.szh.hadoop.WordCount: 本次reduce结束: <dk,10>
    2019-06-13 16:20:30,267 INFO [main] com.szh.hadoop.WordCount: reduce方法执行在机器: bi-zhaopeng03/10.1.4.18上
    2019-06-13 16:20:30,267 INFO [main] com.szh.hadoop.WordCount: reduce输入数据: tyn,org.apache.hadoop.mapreduce.task.ReduceContextImpl$ValueIterable@4218500f
    2019-06-13 16:20:30,267 INFO [main] com.szh.hadoop.WordCount: 	reduce循环: <tyn,4>
    2019-06-13 16:20:30,267 INFO [main] com.szh.hadoop.WordCount: 本次reduce结束: <tyn,4>
    2019-06-13 16:20:30,371 INFO [main] org.apache.hadoop.mapred.Task: Task:attempt_1559721784881_0053_r_000008_0 is done. And is in the process of committing
    2019-06-13 16:20:30,407 INFO [main] org.apache.hadoop.mapred.Task: Task attempt_1559721784881_0053_r_000008_0 is allowed to commit now
    2019-06-13 16:20:30,412 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: Saved output of task 'attempt_1559721784881_0053_r_000008_0' to hdfs://bi-zhaopeng03:8020/tmp/songzehao/words_output/_temporary/1/task_1559721784881_0053_r_000008
    2019-06-13 16:20:30,433 INFO [main] org.apache.hadoop.mapred.Task: Task 'attempt_1559721784881_0053_r_000008_0' done.
    2019-06-13 16:20:30,533 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Stopping ReduceTask metrics system...
    2019-06-13 16:20:30,534 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system stopped.
    2019-06-13 16:20:30,534 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system shutdown complete.

reduce-00009.log:
    2019-06-13 16:20:29,205 INFO [main] org.apache.hadoop.metrics2.impl.MetricsConfig: loaded properties from hadoop-metrics2.properties
    2019-06-13 16:20:29,263 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Scheduled snapshot period at 10 second(s).
    2019-06-13 16:20:29,263 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system started
    2019-06-13 16:20:29,270 INFO [main] org.apache.hadoop.mapred.YarnChild: Executing with tokens:
    2019-06-13 16:20:29,270 INFO [main] org.apache.hadoop.mapred.YarnChild: Kind: mapreduce.job, Service: job_1559721784881_0053, Ident: (org.apache.hadoop.mapreduce.security.token.JobTokenIdentifier@59309333)
    2019-06-13 16:20:29,324 INFO [main] org.apache.hadoop.mapred.YarnChild: Sleeping for 0ms before retrying again. Got null now.
    2019-06-13 16:20:29,497 INFO [main] org.apache.hadoop.mapred.YarnChild: mapreduce.cluster.local.dir for child: /yarn/nm/usercache/hdfs/appcache/application_1559721784881_0053
    2019-06-13 16:20:29,636 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
    2019-06-13 16:20:29,969 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: File Output Committer Algorithm version is 1
    2019-06-13 16:20:29,978 INFO [main] org.apache.hadoop.mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
    2019-06-13 16:20:30,006 INFO [main] org.apache.hadoop.mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1a760689
    2019-06-13 16:20:30,019 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: MergerManager: memoryLimit=535088320, maxSingleShuffleLimit=133772080, mergeThreshold=353158304, ioSortFactor=64, memToMemMergeOutputsThreshold=64
    2019-06-13 16:20:30,020 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000009_0 Thread started: EventFetcher for fetching Map Completion Events
    2019-06-13 16:20:30,027 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: attempt_1559721784881_0053_r_000009_0: Got 1 new map-outputs
    2019-06-13 16:20:30,027 INFO [fetcher#8] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: Assigning bi-zhaopeng05:13562 with 1 to fetcher#8
    2019-06-13 16:20:30,027 INFO [fetcher#8] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: assigned 1 of 1 to bi-zhaopeng05:13562 to fetcher#8
    2019-06-13 16:20:30,130 INFO [fetcher#8] org.apache.hadoop.mapreduce.task.reduce.Fetcher: for url=13562/mapOutput?job=job_1559721784881_0053&reduce=9&map=attempt_1559721784881_0053_m_000000_0 sent hash and received reply
    2019-06-13 16:20:30,135 INFO [fetcher#8] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
    2019-06-13 16:20:30,139 INFO [fetcher#8] org.apache.hadoop.mapreduce.task.reduce.Fetcher: fetcher#8 about to shuffle output of map attempt_1559721784881_0053_m_000000_0 decomp: 2 len: 16 to MEMORY
    2019-06-13 16:20:30,141 INFO [fetcher#8] org.apache.hadoop.mapreduce.task.reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_1559721784881_0053_m_000000_0
    2019-06-13 16:20:30,141 INFO [fetcher#8] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->2
    2019-06-13 16:20:30,142 INFO [EventFetcher for fetching Map Completion Events] org.apache.hadoop.mapreduce.task.reduce.EventFetcher: EventFetcher is interrupted.. Returning
    2019-06-13 16:20:30,142 INFO [fetcher#8] org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl: bi-zhaopeng05:13562 freed by fetcher#8 in 116ms
    2019-06-13 16:20:30,145 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
    2019-06-13 16:20:30,150 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:30,150 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 0 segments left of total size: 0 bytes
    2019-06-13 16:20:30,151 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new compressor [.snappy]
    2019-06-13 16:20:30,154 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merged 1 segments, 2 bytes to disk to satisfy reduce memory limit
    2019-06-13 16:20:30,155 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 1 files, 20 bytes from disk
    2019-06-13 16:20:30,155 INFO [main] org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
    2019-06-13 16:20:30,155 INFO [main] org.apache.hadoop.mapred.Merger: Merging 1 sorted segments
    2019-06-13 16:20:30,157 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 0 segments left of total size: 0 bytes
    2019-06-13 16:20:30,216 INFO [main] org.apache.hadoop.conf.Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
    2019-06-13 16:20:30,224 INFO [main] org.apache.hadoop.mapred.Task: Task:attempt_1559721784881_0053_r_000009_0 is done. And is in the process of committing
    2019-06-13 16:20:30,251 INFO [main] org.apache.hadoop.mapred.Task: Task attempt_1559721784881_0053_r_000009_0 is allowed to commit now
    2019-06-13 16:20:30,256 INFO [main] org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter: Saved output of task 'attempt_1559721784881_0053_r_000009_0' to hdfs://bi-zhaopeng03:8020/tmp/songzehao/words_output/_temporary/1/task_1559721784881_0053_r_000009
    2019-06-13 16:20:30,270 INFO [main] org.apache.hadoop.mapred.Task: Task 'attempt_1559721784881_0053_r_000009_0' done.
    2019-06-13 16:20:30,371 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Stopping ReduceTask metrics system...
    2019-06-13 16:20:30,371 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system stopped.
    2019-06-13 16:20:30,371 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: ReduceTask metrics system shutdown complete.

好了，到这里我们已经拿到了所需的日志文件，再接下来先熟悉一下MapReduce的理论知识，以便结合实际了解掌握。

### 熟悉MR理论 ###

熟悉MR的理论，可以去翻看各种书籍和网上资源，如《大数据技术原理与应用》第2版的第7章 - MapReduce，这里则贴出MapReduce的百度百科：

[https://baike.baidu.com/item/MapReduce/133425?fr=aladdin&tdsourcetag=s\_pcqq\_aiomsg&qq-pf-to=pcqq.group\#8][https_baike.baidu.com_item_MapReduce_133425_fr_aladdin_tdsourcetag_s_pcqq_aiomsg_qq-pf-to_pcqq.group_8]

由于篇幅略大，这里贴出其工作原理的部分：

> **工作原理**
> 
> 下图是论文里给出的流程图。一切都是从最上方的user program开始的，user program链接了MapReduce库，实现了最基本的Map函数和Reduce函数。图中执行的顺序都用数字标记了。  
> MapReduce执行流程图：
> 
> ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70 1][]
> 
> 1.  MapReduce库先把user program的输入文件划分为M份（M为用户定义），每一份通常有16MB到64MB，如图左方所示分成了split0~4；然后使用fork将用户进程拷贝到集群内其它机器上。
> 2.  user program的副本中有一个称为master，其余称为worker，master是负责调度的，为空闲worker分配作业（Map作业或者Reduce作业），worker的数量也是可以由用户指定的。
> 3.  被分配了Map作业的worker，开始读取对应分片的输入数据，Map作业数量是由M决定的，和split一一对应；Map作业从输入数据中抽取出键值对，每一个键值对都作为参数传递给map函数，map函数产生的中间键值对被缓存在内存中。
> 4.  缓存的中间键值对会被定期写入本地磁盘，而且被分为R个区，R的大小是由用户定义的，将来每个区会对应一个Reduce作业；这些中间键值对的位置会被通报给master，master负责将信息转发给Reduce worker。
> 5.  master通知分配了Reduce作业的worker它负责的分区在什么位置（肯定不止一个地方，每个Map作业产生的中间键值对都可能映射到所有R个不同分区），当Reduce worker把所有它负责的中间键值对都读过来后，先对它们进行排序，使得相同键的键值对聚集在一起。因为不同的键可能会映射到同一个分区也就是同一个Reduce作业（谁让分区少呢），所以排序是必须的。
> 6.  reduce worker遍历排序后的中间键值对，对于每个唯一的键，都将键与关联的值传递给reduce函数，reduce函数产生的输出会添加到这个分区的输出文件中。
> 7.  当所有的Map和Reduce作业都完成了，master唤醒正版的user program，MapReduce函数调用返回user program的代码。所有执行完毕后，MapReduce输出放在了R个分区的输出文件中（分别对应一个Reduce作业）。用户通常并不需要合并这R个文件，而是将其作为输入交给另一个MapReduce程序处理。整个过程中，输入数据是来自底层分布式文件系统（GFS）的，中间数据是放在本地文件系统的，最终输出数据是写入底层分布式文件系统（GFS）的。而且我们要注意Map/Reduce作业和map/reduce函数的区别：Map作业处理一个输入数据的分片，可能需要调用多次map函数来处理每个输入键值对；Reduce作业处理一个分区的中间键值对，期间要对每个不同的键调用一次reduce函数，Reduce作业最终也对应一个输出文件。

### 分析日志 ###

知识储备后，进入到真正的分析总结升华阶段，更多的是对理论的验证。

我们有b3,b4,b5三台机器，其中b3为master，通过查看日志可验证以下几点：

1.  MR任务确实由集群中的许多节点机器参与，b4跑main()，b5跑了2次map()，b5跑了5次combine()，b5跑了2次reduce()，b3跑了1次reduce()；
2.  总共有16个reduce Task，对应16个hdfs上的结果输出文件，尽管有13个算是在空跑；
3.  源文本文件默认被一行一行读取，map()的输入数据<k,v>具体代表<某行文本在文件中的偏移位置,对应行的文本内容>，当然也可通过InputFormat来自定义读取方式；
4.  map()跑了2次代表源文件被逻辑切分了2片；
5.  map()的输出数据<k,v>具体代表<单词字符串,1>；
6.  combine()的输入数据<k,vlist>具体代表<单词字符串,1的集合>，证明在combine之前，map()的输出数据被进行了整理，即排序；
7.  查看reduce-00008.log第16行，得知，在reduce之前，reduce Worker所在节点会先去对应一个或多个map机器（b5）的磁盘“领取”（Fetch）数据；
8.  查看/tmp/songzehao/words\_output/part-r-00000，得知，每个reduce任务的输出结果，默认都会以key进行排序；
9.  shuffle阶段分为在map和在reduce两部分，包括了sort/partition/combine/merge，可在map.log中发现combine的日志，可在各reduce-000xx.log中看到shuffle的处理日志。

以下是程序流程（暂不详细讨论shuffle）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70 2][]

以上只是基于很小数据量下的MR运行的初步了解，若想深扒具体过程，可以阅读《大数据技术原理与应用》章节7.2 - MapReduce的工作流程。

[https_blog.csdn.net_songzehao_article_details_91560692]: https://blog.csdn.net/songzehao/article/details/91560692
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70]: /images/20220114/ed937437356e45a49cdda898ed672d5f.png
[https_baike.baidu.com_item_MapReduce_133425_fr_aladdin_tdsourcetag_s_pcqq_aiomsg_qq-pf-to_pcqq.group_8]: https://baike.baidu.com/item/MapReduce/133425?fr=aladdin&tdsourcetag=s_pcqq_aiomsg&qq-pf-to=pcqq.group#8
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70 1]: /images/20220114/0349a8443ffb40e98d9b0a1ccb2cc91c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3Nvbmd6ZWhhbw_size_16_color_FFFFFF_t_70 2]: /images/20220114/37dbbca2ef804eca91f0b2878a482fc5.png