Hadoop Streaming with Java Mapper / Reducer

时间:2013-04-16 17:41:20

标签: hadoop jar hadoop-streaming wikimedia

我正在尝试使用java Mapper / Reducer在一些wikipedia转储上运行hadoop流式传输作业(以压缩的bz2形式)。我正在尝试使用WikiHadoop,这是维基媒体最近发布的一个界面。

WikiReader_Mapper.java

package courseproj.example;

// Mapper: emits (token, 1) for every article occurrence.
public class WikiReader_Mapper extends MapReduceBase implements Mapper<Text, Text, Text, IntWritable> {

    // Reuse objects to save overhead of object creation.
    private final static Text KEY = new Text();
    private final static IntWritable VALUE = new IntWritable(1);

    @Override
    public void map(Text key, Text value, OutputCollector<Text, IntWritable> collector, Reporter reporter)
            throws IOException {
        KEY.set("article count");
        collector.collect(KEY, VALUE);
    }
}

WikiReader_Reducer.java

package courseproj.example;

//Reducer: sums up all the counts.
public class WikiReader_Reducer extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {

    private final static IntWritable SUM = new IntWritable();

    public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> collector,
            Reporter reporter) throws IOException {
        int sum = 0;
        while (values.hasNext()) {
            sum += values.next().get();
        }
        SUM.set(sum);
        collector.collect(key, SUM);
    }
}

我正在运行的命令是

hadoop jar lib/hadoop-streaming-2.0.0-cdh4.2.0.jar \
       -libjars lib2/wikihadoop-0.2.jar \
       -D mapreduce.input.fileinputformat.split.minsize=300000000 \
       -D mapreduce.task.timeout=6000000 \
       -D org.wikimedia.wikihadoop.previousRevision=false \
       -input enwiki-latest-pages-articles10.xml-p000925001p001325000.bz2 \
       -output out \
       -inputformat org.wikimedia.wikihadoop.StreamWikiDumpInputFormat \
       -mapper WikiReader_Mapper \
       -reducer WikiReader_Reducer

我收到的错误消息是

Error: java.lang.RuntimeException: Error in configuring object
    at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:106)
    at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:72)
    at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:130)
    at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:424)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:340)
    at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:157)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:396)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1408)
    at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:152)

Caused by: java.lang.reflect.InvocationTargetException
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
    at java.lang.reflect.Method.invoke(Method.java:597)
    at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:103)

Caused by: java.io.IOException: Cannot run program "WikiReader_Mapper": java.io.IOException: error=2, No such file or directory
    at java.lang.ProcessBuilder.start(ProcessBuilder.java:460)
    at org.apache.hadoop.streaming.PipeMapRed.configure(PipeMapRed.java:209)

我对新的hadoop API与旧版本比较熟悉。由于我的mapper和reducer代码在两个不同的文件中,我在哪里定义作业的JobConf配置参数,同时遵循hadoop流的命令结构(显式设置mapper和reducer类)。有没有办法可以将mapper和reducer代码全部包装到一个类中(扩展了Configured和implements Tool,这是在新API中完成的)并将类名传递给hadoop流命令行而不是设置分别映射和减少类?

1 个答案:

答案 0 :(得分:0)

Streaming使用旧的API(org.apache.hadoop.mapred) - 但mapper和reducer类扩展了新的API类(org.apache.hadoop.mapreduce)。

尝试更改映射器以实现org.apache.hadoop.mapred.Mapper,并尝试使用reducer来实现org.apache.hadoop.mapred.Reducer,例如:

package courseproj.example;

// Mapper: emits ("article", 1) for every article occurrence.
public class WikiReader_Mapper implements Mapper<Text, Text, Text, IntWritable> {

  // Reuse objects to save overhead of object creation.
  private final static Text KEY = new Text();
  private final static IntWritable VALUE = new IntWritable(1);

  @Override
  public void map(Text key, Text value, OutputCollector<Text, IntWritable> collector, Reporter reporter)
      throws IOException, InterruptedException {
    KEY.set("article count");
    collector.collect(KEY, VALUE);
  }
}