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高可用Hadoop平台－运行MapReduce程序详解大数据

• 大数据

1.概述

　　最近有同学反应，如何在配置了HA的Hadoop平台运行MapReduce程序呢？对于刚步入Hadoop行业的同学，这个疑问却是会存在，其实仔细想想，如果你之前的语言功底不错的，应该会想到自动重连，自动重连也可以帮我我们解决运行MapReduce程序的问题。然后，今天我赘述的是利用Hadoop的Java API 来实现。

2.介绍

　　下面直接附上代码，代码中我都有注释。

2.1Java操作HDFS HA的API

　　代码如下：

/** 
 *  
 */ 
package cn.hdfs.mr.example; 
 
import java.io.IOException; 
 
import org.apache.hadoop.conf.Configuration; 
import org.apache.hadoop.fs.FileStatus; 
import org.apache.hadoop.fs.FileSystem; 
import org.apache.hadoop.fs.Path; 
 
/** 
 * @author dengjie 
 * @date 2015年3月24日 
 * @description TODO 
 */ 
public class DFS { 
 
    public static void main(String[] args) { 
    Configuration conf = new Configuration(); 
    conf.set("fs.defaultFS", "hdfs://cluster1");//指定hdfs的nameservice为cluster1,是NameNode的URI 
    conf.set("dfs.nameservices", "cluster1");//指定hdfs的nameservice为cluster1 
    conf.set("dfs.ha.namenodes.cluster1", "nna,nns");//cluster1下面有两个NameNode，分别是nna，nns 
    conf.set("dfs.namenode.rpc-address.cluster1.nna", "10.211.55.26:9000");//nna的RPC通信地址 
    conf.set("dfs.namenode.rpc-address.cluster1.nns", "10.211.55.27:9000");//nns的RPC通信地址 
    conf.set("dfs.client.failover.proxy.provider.cluster1", "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider");//配置失败自动切换实现方式 
    FileSystem fs = null; 
    try { 
        fs = FileSystem.get(conf);//获取文件对象 
        FileStatus[] list = fs.listStatus(new Path("/"));//文件状态集合 
        for (FileStatus file : list) { 
        System.out.println(file.getPath().getName());//打印目录名 
        } 
    } catch (IOException e) { 
        e.printStackTrace(); 
    } finally { 
        try { 
        if (fs != null) { 
            fs.close(); 
        } 
        } catch (IOException e) { 
        e.printStackTrace(); 
        } 
    } 
    } 
 
}

　　接下来，附上 Java 运行 MapReduce 程序的 API 代码。

2.2Java 运行 MapReduce 程序的 API 

　　以 WordCount 为例子，代码如下：

package cn.jpush.hdfs.mr.example; 
import java.io.IOException; 
import java.util.Random; 
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.slf4j.Logger; 
import org.slf4j.LoggerFactory; 
import cn.jpush.hdfs.utils.ConfigUtils; 
/** 
*  
* @author dengjie 
* @date 2014年11月29日 
* @description Wordcount的例子是一个比较经典的mapreduce例子，可以叫做Hadoop版的hello world。 
*              它将文件中的单词分割取出，然后shuffle，sort（map过程），接着进入到汇总统计 
*              （reduce过程），最后写道hdfs中。基本流程就是这样。 
*/ 
public class WordCount { 
private static Logger log = LoggerFactory.getLogger(WordCount.class); 
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { 
private final static IntWritable one = new IntWritable(1); 
private Text word = new Text(); 
/* 
* 源文件：a b b 
*  
* map之后： 
*  
* a 1 
*  
* b 1 
*  
* b 1 
*/ 
public void map(Object key, Text value, Context context) throws IOException, InterruptedException { 
StringTokenizer itr = new StringTokenizer(value.toString());// 整行读取 
while (itr.hasMoreTokens()) { 
word.set(itr.nextToken());// 按空格分割单词 
context.write(word, one);// 每次统计出来的单词+1 
        } 
} 
} 
/* 
* reduce之前： 
*  
* a 1 
*  
* b 1 
*  
* b 1 
*  
* reduce之后: 
*  
* a 1 
*  
* b 2 
*/ 
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> { 
private IntWritable result = new IntWritable(); 
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { 
int sum = 0; 
for (IntWritable val : values) { 
sum += val.get(); 
} 
result.set(sum); 
context.write(key, result); 
} 
} 
@SuppressWarnings("deprecation") 
public static void main(String[] args) throws Exception { 
Configuration conf = new Configuration(); 
conf.set("fs.defaultFS", "hdfs://cluster1"); 
conf.set("dfs.nameservices", "cluster1"); 
conf.set("dfs.ha.namenodes.cluster1", "nna,nns"); 
conf.set("dfs.namenode.rpc-address.cluster1.nna", "10.211.55.26:9000"); 
conf.set("dfs.namenode.rpc-address.cluster1.nns", "10.211.55.27:9000"); 
conf.set("dfs.client.failover.proxy.provider.cluster1", "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider"); 
long random1 = new Random().nextLong();// 重定下输出目录 
log.info("random1 -> " + random1); 
Job job1 = new Job(conf, "word count"); 
job1.setJarByClass(WordCount.class); 
job1.setMapperClass(TokenizerMapper.class);// 指定Map计算的类 
job1.setCombinerClass(IntSumReducer.class);// 合并的类 
job1.setReducerClass(IntSumReducer.class);// Reduce的类 
job1.setOutputKeyClass(Text.class);// 输出Key类型 
job1.setOutputValueClass(IntWritable.class);// 输出值类型   
     
FileInputFormat.addInputPath(job1, new Path("/home/hdfs/test/hello.txt"));// 指定输入路径 
FileOutputFormat.setOutputPath(job1, new Path(String.format(ConfigUtils.HDFS.WORDCOUNT_OUT, random1)));// 指定输出路径 
 
System.exit(job1.waitForCompletion(true) ? 0 : 1);// 执行完MR任务后退出应用 
    } 
}

3.运行结果

　　下面附上部分运行 Log 日志，如下所示：

[Job.main] - Running job: job_local551164419_0001 
2015-03-24 11:52:09 INFO  [LocalJobRunner.Thread-12] - OutputCommitter set in config null 
2015-03-24 11:52:09 INFO  [LocalJobRunner.Thread-12] - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter 
2015-03-24 11:52:10 INFO  [LocalJobRunner.Thread-12] - Waiting for map tasks 
2015-03-24 11:52:10 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] - Starting task: attempt_local551164419_0001_m_000000_0 
2015-03-24 11:52:10 INFO  [ProcfsBasedProcessTree.LocalJobRunner Map Task Executor #0] - ProcfsBasedProcessTree currently is supported only on Linux. 
2015-03-24 11:52:10 INFO  [Task.LocalJobRunner Map Task Executor #0] -  Using ResourceCalculatorProcessTree : null 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Processing split: hdfs://cluster1/home/hdfs/test/hello.txt:0+24 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - (EQUATOR) 0 kvi 26214396(104857584) 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - mapreduce.task.io.sort.mb: 100 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - soft limit at 83886080 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - bufstart = 0; bufvoid = 104857600 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - kvstart = 26214396; length = 6553600 
2015-03-24 11:52:10 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] -  
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Starting flush of map output 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Spilling map output 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - bufstart = 0; bufend = 72; bufvoid = 104857600 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - kvstart = 26214396(104857584); kvend = 26214352(104857408); length = 45/6553600 
2015-03-24 11:52:10 INFO  [MapTask.LocalJobRunner Map Task Executor #0] - Finished spill 0 
2015-03-24 11:52:10 INFO  [Task.LocalJobRunner Map Task Executor #0] - Task:attempt_local551164419_0001_m_000000_0 is done. And is in the process of committing 
2015-03-24 11:52:10 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] - map 
2015-03-24 11:52:10 INFO  [Task.LocalJobRunner Map Task Executor #0] - Task 'attempt_local551164419_0001_m_000000_0' done. 
2015-03-24 11:52:10 INFO  [LocalJobRunner.LocalJobRunner Map Task Executor #0] - Finishing task: attempt_local551164419_0001_m_000000_0 
2015-03-24 11:52:10 INFO  [LocalJobRunner.Thread-12] - map task executor complete. 
2015-03-24 11:52:10 INFO  [LocalJobRunner.Thread-12] - Waiting for reduce tasks 
2015-03-24 11:52:10 INFO  [LocalJobRunner.pool-6-thread-1] - Starting task: attempt_local551164419_0001_r_000000_0 
2015-03-24 11:52:10 INFO  [ProcfsBasedProcessTree.pool-6-thread-1] - ProcfsBasedProcessTree currently is supported only on Linux. 
2015-03-24 11:52:10 INFO  [Task.pool-6-thread-1] -  Using ResourceCalculatorProcessTree : null 
2015-03-24 11:52:10 INFO  [ReduceTask.pool-6-thread-1] - Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1197414 
2015-03-24 11:52:10 INFO  [MergeManagerImpl.pool-6-thread-1] - MergerManager: memoryLimit=1503238528, maxSingleShuffleLimit=375809632, mergeThreshold=992137472, ioSortFactor=10, memToMemMergeOutputsThreshold=10 
2015-03-24 11:52:10 INFO  [EventFetcher.EventFetcher for fetching Map Completion Events] - attempt_local551164419_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events 
2015-03-24 11:52:10 INFO  [LocalFetcher.localfetcher#1] - localfetcher#1 about to shuffle output of map attempt_local551164419_0001_m_000000_0 decomp: 50 len: 54 to MEMORY 
2015-03-24 11:52:10 INFO  [InMemoryMapOutput.localfetcher#1] - Read 50 bytes from map-output for attempt_local551164419_0001_m_000000_0 
2015-03-24 11:52:10 INFO  [MergeManagerImpl.localfetcher#1] - closeInMemoryFile -> map-output of size: 50, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->50 
2015-03-24 11:52:10 INFO  [EventFetcher.EventFetcher for fetching Map Completion Events] - EventFetcher is interrupted.. Returning 
2015-03-24 11:52:10 INFO  [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied. 
2015-03-24 11:52:10 INFO  [MergeManagerImpl.pool-6-thread-1] - finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs 
2015-03-24 11:52:10 INFO  [Merger.pool-6-thread-1] - Merging 1 sorted segments 
2015-03-24 11:52:10 INFO  [Merger.pool-6-thread-1] - Down to the last merge-pass, with 1 segments left of total size: 46 bytes 
2015-03-24 11:52:10 INFO  [MergeManagerImpl.pool-6-thread-1] - Merged 1 segments, 50 bytes to disk to satisfy reduce memory limit 
2015-03-24 11:52:10 INFO  [MergeManagerImpl.pool-6-thread-1] - Merging 1 files, 54 bytes from disk 
2015-03-24 11:52:10 INFO  [MergeManagerImpl.pool-6-thread-1] - Merging 0 segments, 0 bytes from memory into reduce 
2015-03-24 11:52:10 INFO  [Merger.pool-6-thread-1] - Merging 1 sorted segments 
2015-03-24 11:52:10 INFO  [Merger.pool-6-thread-1] - Down to the last merge-pass, with 1 segments left of total size: 46 bytes 
2015-03-24 11:52:10 INFO  [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied. 
2015-03-24 11:52:10 INFO  [deprecation.pool-6-thread-1] - mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords 
2015-03-24 11:52:10 INFO  [Task.pool-6-thread-1] - Task:attempt_local551164419_0001_r_000000_0 is done. And is in the process of committing 
2015-03-24 11:52:10 INFO  [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied. 
2015-03-24 11:52:10 INFO  [Task.pool-6-thread-1] - Task attempt_local551164419_0001_r_000000_0 is allowed to commit now 
2015-03-24 11:52:10 INFO  [FileOutputCommitter.pool-6-thread-1] - Saved output of task 'attempt_local551164419_0001_r_000000_0' to hdfs://cluster1/output/result/-3636988299559297154/_temporary/0/task_local551164419_0001_r_000000 
2015-03-24 11:52:10 INFO  [LocalJobRunner.pool-6-thread-1] - reduce > reduce 
2015-03-24 11:52:10 INFO  [Task.pool-6-thread-1] - Task 'attempt_local551164419_0001_r_000000_0' done. 
2015-03-24 11:52:10 INFO  [LocalJobRunner.pool-6-thread-1] - Finishing task: attempt_local551164419_0001_r_000000_0 
2015-03-24 11:52:10 INFO  [LocalJobRunner.Thread-12] - reduce task executor complete. 
2015-03-24 11:52:10 INFO  [Job.main] - Job job_local551164419_0001 running in uber mode : false 
2015-03-24 11:52:10 INFO  [Job.main] -  map 100% reduce 100% 
2015-03-24 11:52:10 INFO  [Job.main] - Job job_local551164419_0001 completed successfully 
2015-03-24 11:52:10 INFO  [Job.main] - Counters: 35 
File System Counters 
FILE: Number of bytes read=462 
FILE: Number of bytes written=466172 
FILE: Number of read operations=0 
FILE: Number of large read operations=0 
FILE: Number of write operations=0 
HDFS: Number of bytes read=48 
HDFS: Number of bytes written=24 
HDFS: Number of read operations=13 
HDFS: Number of large read operations=0 
HDFS: Number of write operations=4 
Map-Reduce Framework 
Map input records=2 
Map output records=12 
Map output bytes=72 
Map output materialized bytes=54 
Input split bytes=105 
Combine input records=12 
Combine output records=6 
Reduce input groups=6 
Reduce shuffle bytes=54 
Reduce input records=6 
Reduce output records=6 
Spilled Records=12 
Shuffled Maps =1 
Failed Shuffles=0 
Merged Map outputs=1 
GC time elapsed (ms)=13 
Total committed heap usage (bytes)=514850816 
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=24 
File Output Format Counters  
Bytes Written=24

　　原文件如下所示：

a a c v d d 
a d d s s x

　　Reduce 结果图，如下所示：

高可用Hadoop平台－运行MapReduce程序详解大数据

4.总结

　　我们可以按以下步骤进行验证代码的可用性：

  1. 保证 NNA（ active 状态）和 NNS（ standby 状态）。注意，DN 节点都是正常运行的。
  2. 然后，我们运行 WordCount 程序，看能否统计出结果。
  3. 若安上述步骤下来，可以统计；我们接着往下执行。若不行，请排查错误，然后继续。
  4. 然后，我们 kill 掉 NNA 节点的 NameNode 进程，此时，NNS 的状态会由 standby 转变为 active
  5. 接着我们在支持 WordCount 程序，看能否统计结果；若是能统计结果，表示代码可用。

　　以上就是整个验证的流程。

5.结束语

　　这篇文章就分享到这里，如果在验证的过程当中有什么问题，可以加群进行讨论或发送邮件给我，我会尽我所能为您解答，与君共勉！

原创文章，作者：bd101bd101，如若转载，请注明出处：https://blog.ytso.com/228093.html

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