但有时候我们像将其设置为其它的分隔符输出，比如","，如下图所示：
此时可以在Mapreduce的主函数中添加如下的两行代码：
[java] view plain copy print?
conf.set("mapred.textoutputformat.ignoreseparator","true")
conf.set("mapred.textoutputformat.separator",",")
具体如下的WordCount程序：
[java] view plain copy print?
import java.io.IOException
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
public class WordCount {
public static class TokenizerMapper extends
Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1)
private Text word = new Text()
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString())
while (itr.hasMoreTokens()) {
word.set(itr.nextToken())
context.write(word, one)
}
}
}
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)
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration()
//设置MapReduce的输出的分隔符为逗号
conf.set("mapred.textoutputformat.ignoreseparator", "true")
conf.set("mapred.textoutputformat.separator", ",")
String[] otherArgs = new GenericOptionsParser(conf, args)
.getRemainingArgs()
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in><out>")
System.exit(2)
}
Job job = new Job(conf, "word count")
job.setJarByClass(WordCount.class)
job.setMapperClass(TokenizerMapper.class)
job.setCombinerClass(IntSumReducer.class)
job.setReducerClass(IntSumReducer.class)
job.setOutputKeyClass(Text.class)
job.setOutputValueClass(IntWritable.class)
FileInputFormat.addInputPath(job, new Path(otherArgs[0]))
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]))
System.exit(job.waitForCompletion(true) ? 0 : 1)
}
}
今日我在eclipse上运行Map/Reduce框架进行数据分析的时候遇到了一个很奇怪的错误：一开始我发现运行程序之后也没在控制台报错，也生成了目标目录，但一直出不来统计好的数据文件。
我先通过位置标记输出来判断各个类的加载是否正常，
发现Map是可以正常加载执行的，但是Reduce一直无法加载执行。
然后我通过设置，让其在运行时显示日志信息，再运行。
文末会分享设置显示日志信息的方法。
这一次便发现了如下的报错：
日志中的报错信息：
java.lang.Exception: org.apache.hadoop.mapreduce.task.reduce.Shuffle$ShuffleError: error in shuffle in localfetcher#1at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:529)Caused by: org.apache.hadoop.mapreduce.task.reduce.Shuffle$ShuffleError: error in shuffle in localfetcher#1at org.apache.hadoop.mapreduce.task.reduce.Shuffle.run(Shuffle.java:134)at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:376)at org.apache.hadoop.mapred.LocalJobRunner$Job$ReduceTaskRunnable.run(LocalJobRunner.java:319)at java.util.concurrent.Executors$RunnableAdapter.call(Unknown Source)at java.util.concurrent.FutureTask.run(Unknown Source)at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)at java.lang.Thread.run(Unknown Source)Caused by:（1）]java.io.FileNotFoundException: E:/tmp/hadoop-Alan%20Yang/mapred/local/localRunner/Alan%20Yang/jobcache/job_local214639494_0001/attempt_local214639494_0001_m_000003_0/output/file.out.indexat org.apache.hadoop.fs.RawLocalFileSystem.open(RawLocalFileSystem.java:200)at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:767)at （2）]org.apache.hadoop.io.SecureIOUtils.openFSDataInputStream(SecureIOUtils.java:156)at org.apache.hadoop.mapred.SpillRecord.<init>(SpillRecord.java:70)at org.apache.hadoop.mapred.SpillRecord.<init>(SpillRecord.java:62)at org.apache.hadoop.mapred.SpillRecord.<init>(SpillRecord.java:57)at org.apache.hadoop.mapreduce.task.reduce.LocalFetcher.copyMapOutput(LocalFetcher.java:124)at org.apache.hadoop.mapreduce.task.reduce.LocalFetcher.doCopy(LocalFetcher.java:102)at org.apache.hadoop.mapreduce.task.reduce.LocalFetcher.run(LocalFetcher.java:85)2017-07-12 16:19:02,616 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local214639494_0001 failed with state FAILED due to: NA2017-07-12 16:19:02,663 INFO [org.apache.hadoop.mapreduce.Job] - Counters: 22File System CountersFILE: Number of bytes read=62223FILE: Number of bytes written=176635984FILE: Number of read operations=0FILE: Number of large read operations=0FILE: Number of write operations=0HDFS: Number of bytes read=898750946HDFS: Number of bytes written=0HDFS: Number of read operations=140HDFS: Number of large read operations=0HDFS: Number of write operations=10Map-Reduce FrameworkMap input records=2629660Map output records=2626091Map output bytes=26260910Map output materialized bytes=31513152Input split bytes=1210Combine input records=0Spilled Records=2626091Failed Shuffles=0Merged Map outputs=0GC time elapsed (ms)=496Total committed heap usage (bytes)=7754743808File Input Format Counters Bytes Read=163038920
按照通常的排错思路，后面的错误往往是前面的错误导致产生的，所以我先入为主的直接去搜索 error in shuffle in localfetcher的解决办法，发现几乎都在说是内存的问题。百般尝试始终是毫无成效。
后来当我往下看的时候发现其实日志给出了大概的错误原因，是路径的问题。这一下子就找到了正确的方向。通过查找资料，发现有的朋友通过修改默认的路径即可解决，我尝试了一下，结果只是换个路径报错而已。这就耐人寻味了，我再自己看了看这条报错信息，里面用到了我的Windows的用户名，但是中间的空格变成了%20，在路径中出现%这意味着什么？配过环境变量的人都知道。我一下子恍然大悟。很有可能是Windows用户名的问题。所以我去修改了Windows本机的用户名，把空格去掉。一下子就解决了！！！
eclipse控制台不显示MapReduce程序日志的解决方法：
使用Hadoop2.6.0，在eclipse下面调试mapreduce程序的时候，控制台不打印程序运行时的日志，而是显示如下信息：
log4j:WARN No appenders could be found for logger (org.apache.[Hadoop]
log4j:WARN Please initialize the log4j system properly.
log4j:WARN [hadoop] See noconfig for more info.
说明没有配置log4j.properties文件。这虽然不影响程序的正常运行，但是看不到日志难免不爽。解决方法：把Hadoop2.6.0的安装目录下面的/etc/hadoop／目录下面的log4j.properties文件拷贝放到MapReduce工程的src目录下面。
如果你自己用“调”api,来读写hbase的话，我觉得具体考虑的话是任务能否最终实现的问题了,毕竟mapreduce所做的工作很多，它自己的master，zookeeper，hbase的master之间的通信，计算任务的reduce和mapping，细节太多，考虑到mapreduce通常处理的数据量，即便不考虑fault tolerant 都不一定能有效协调各个任务，更何况怎么可能不考虑？...所以，自己用java来实现的话，也许是个不错的学习过程，但是基本出不了东西，也就没有实用的可能...
