DataJoins in Hadoop MapReduce

Question

我正在尝试实现Book Hadoop In Action中给出的一个用例，但我不是编译代码。我是Java的新手，所以无法理解错误背后的确切原因。

有趣的是，另一段使用相同的类和方法的编码已成功编译。

hadoop@hadoopnode1:~/hadoop-0.20.2/playground/src$ javac -classpath /home/hadoop/hadoop-0.20.2/hadoop-0.20.2-core.jar:/home/hadoop/hadoop-0.20.2/lib/commons-cli-1.2.jar:/home/hadoop/hadoop-0.20.2/contrib/datajoin/hadoop-0.20.2-datajoin.jar -d ../classes DataJoin2.java 
DataJoin2.java:49: cannot find symbol
symbol  : constructor TaggedWritable(org.apache.hadoop.io.Text)
location: class DataJoin2.TaggedWritable
            TaggedWritable retv = new TaggedWritable((Text) value);
                                  ^
DataJoin2.java:69: cannot find symbol
symbol  : constructor TaggedWritable(org.apache.hadoop.io.Text)
location: class DataJoin2.TaggedWritable
            TaggedWritable retv = new TaggedWritable(new Text(joinedStr));
                                  ^
DataJoin2.java:113: setMapperClass(java.lang.Class<? extends org.apache.hadoop.mapreduce.Mapper>) in org.apache.hadoop.mapreduce.Job cannot be applied to (java.lang.Class<DataJoin2.MapClass>)
        job.setMapperClass(MapClass.class);
           ^
DataJoin2.java:114: setReducerClass(java.lang.Class<? extends org.apache.hadoop.mapreduce.Reducer>) in org.apache.hadoop.mapreduce.Job cannot be applied to (java.lang.Class<DataJoin2.Reduce>)
        job.setReducerClass(Reduce.class);
           ^
4 errors

----------------法典----------------------

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.mapred.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
// DataJoin Classes
import org.apache.hadoop.contrib.utils.join.DataJoinMapperBase;
import org.apache.hadoop.contrib.utils.join.TaggedMapOutput;
import org.apache.hadoop.contrib.utils.join.DataJoinReducerBase;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;

public class DataJoin2
{
    public static class MapClass extends DataJoinMapperBase
    {
        protected Text generateInputTag(String inputFile)
        {
            String datasource = inputFile.split("-")[0];
            return new Text(datasource);            
        }
        protected Text generateGroupKey(TaggedMapOutput aRecord)
        {
            String line = ((Text) aRecord.getData()).toString();
            String[] tokens = line.split(",");
            String groupKey = tokens[0];
            return new Text(groupKey);
        }
        protected TaggedMapOutput generateTaggedMapOutput(Object value)
        {
            TaggedWritable retv = new TaggedWritable((Text) value);
            retv.setTag(this.inputTag);
            return retv;
        }
    } // End of class MapClass
    public static class Reduce extends DataJoinReducerBase
    {
        protected TaggedMapOutput combine(Object[] tags, Object[] values)
        {
            if (tags.length < 2) return null;
            String joinedStr = "";
            for (int i=0;i<values.length;i++)
            {
                if (i>0) joinedStr += ",";
                TaggedWritable tw = (TaggedWritable) values[i];
                String line = ((Text) tw.getData()).toString();
                String[] tokens = line.split(",",2);
                joinedStr += tokens[1];
            }
            TaggedWritable retv = new TaggedWritable(new Text(joinedStr));
            retv.setTag((Text) tags[0]);
            return retv;
        }
    } // End of class Reduce
    public static class TaggedWritable extends TaggedMapOutput 
    {
        private Writable data;
        public TaggedWritable()
        {
            this.tag = new Text("");
            this.data = data;
        }
        public Writable getData()
        {
            return data;
        }
        public void write(DataOutput out) throws IOException
        {
            this.tag.write(out);
            this.data.write(out);
        }
        public void readFields(DataInput in) throws IOException
        {
            this.tag.readFields(in);
            this.data.readFields(in);
        }       
    } // End of class TaggedWritable
    public static void main(String[] args) throws Exception
    {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length != 2) {
          System.err.println("Usage: DataJoin2 <in> <out>");
          System.exit(2);
        }
        Job job = new Job(conf, "DataJoin");
        job.setJarByClass(DataJoin2.class);     
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(TaggedWritable.class);
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);               
    }
}

Answer 1

错误消息没有任何歧义。 它告诉您您没有为TaggedWritable提供构造函数，该构造函数采用 Text 类型的参数。您只在发布的代码中显示无参数构造函数。

Answer 2

对于前两条错误消息，编译器错误清楚地告诉您，您没有接受类型为 Text 的参数的 TaggedWritable 构造函数。在我看来，您正在TaggedWritable作为Writable添加标签的包装器，因此我建议使用以下方法添加构造函数：

public TaggedWritable(Writable data) {
    this.tag = new Text("");
    this.data = data;
}

事实上，正如你写的那样，这一行

this.data = data;

只是将data重新分配给自己，所以我很确定你打算有一个名为 data 的构造函数参数。请参阅我上面的推理，了解为什么我认为您应该将其Writable而不是Text.由于Text实现了Writable，这将解决您的前两条错误消息。

但是，您需要保留默认的无参数构造函数。这是因为Hadoop将使用反射来实例化实例Writable值，因为它在mapreduce阶段之间通过网络序列化它们。我认为对于默认的 no-arg 构造函数来说，您在这里有点混乱：

public TaggedWritable() {
    this.tag = new Text("");
}

我认为这是一团糟的原因是，如果您不为TaggedWritable.data包装的Writable值分配一个有效实例，那么在TaggedWritable.readFields(DataInput)中调用this.data.readFields(in)时，您将获得NullPointerException。由于它是一个通用包装器，因此您可能应该将TaggedWritable设为泛型类型，然后在默认的 no-arg 构造函数中使用反射分配给TaggedWritable.data。

对于最后两个编译器错误，要使用hadoop-datajoin我注意到您需要使用旧的 API 类。因此，所有这些

org.apache.hadoop.mapreduce.Job;
org.apache.hadoop.mapreduce.Mapper;
org.apache.hadoop.mapreduce.Reducer;
org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
org.apache.hadoop.mapreduce.lib.input.TextInputFormat;

应替换为其旧的 API 等效项。所以org.apache.hadoop.mapred.JobConf而不是org.apache.hadoop.mapreduce.Job等等。这将处理您的最后两条错误消息。

Answer 3

我有hadoop-2，7，1，对我来说，在pom中添加了来自MAven的依赖.xml

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-datajoin</artifactId>
<version>2.7.1</version>
</dependency>

这是 hadoop-datajoin 的 URL： https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-datajoin