示例文件同sample join analysis
之前的示例是使用map端的join.这次使用reduce端的join.
根据源的类别写不同的mapper，处理不同的文件，输出的key都是studentno.value是其他的信息同时加上类别信息。
然后使用multipleinputs不同的路径注册不同的mapper.
reduce端相同的studentno的学生信息和考试成绩分配给同一个reduce,而且value中包含了这些信息，
把这些信息抽取出来，再做笛卡尔积即可。
下面的示例代码中，我没有使用multipleinputs来处理，自己修改了TextInputFormat的一些信息，使用返回文件名和当前行的信息。
根据文件名我在mapper中处理两个不同文件的信息，加上不同的类别送出去。
下面的代码中还有很多可以优化的地方，以后再更新。
package myexamples; import java.io.IOException;
import java.util.ArrayList;
import java.util.List; import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionCodecFactory;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.LineReader; public class reducejoin { public static class MyTextInputFormat extends FileInputFormat<Text, Text> { @Override
public MyLineRecordReader createRecordReader(InputSplit split,
TaskAttemptContext context) {
return new MyLineRecordReader();
} @Override
protected boolean isSplitable(JobContext context, Path file) {
CompressionCodec codec = new CompressionCodecFactory(
context.getConfiguration()).getCodec(file);
return codec == null;
} } public static class MyLineRecordReader extends RecordReader<Text, Text> {
private static final Log LOG = LogFactory
.getLog(LineRecordReader.class); private CompressionCodecFactory compressionCodecs = null;
private long start;
private long pos;
private long end;
private LineReader in;
private int maxLineLength;
private Text key = null;
private Text value = null; Text filename = null; public void initialize(InputSplit genericSplit,
TaskAttemptContext context) throws IOException {
FileSplit split = (FileSplit) genericSplit;
Configuration job = context.getConfiguration();
this.maxLineLength = job.getInt(
"mapred.linerecordreader.maxlength", Integer.MAX_VALUE);
start = split.getStart();
end = start + split.getLength();
final Path file = split.getPath();
key = new Text(file.getName());
compressionCodecs = new CompressionCodecFactory(job);
final CompressionCodec codec = compressionCodecs.getCodec(file); // open the file and seek to the start of the split
FileSystem fs = file.getFileSystem(job);
FSDataInputStream fileIn = fs.open(split.getPath());
boolean skipFirstLine = false;
if (codec != null) {
in = new LineReader(codec.createInputStream(fileIn), job);
end = Long.MAX_VALUE;
} else {
if (start != 0) {
skipFirstLine = true;
--start;
fileIn.seek(start);
}
in = new LineReader(fileIn, job);
}
if (skipFirstLine) { // skip first line and re-establish "start".
start += in.readLine(new Text(), 0,
(int) Math.min((long) Integer.MAX_VALUE, end - start));
}
this.pos = start;
} public boolean nextKeyValue() throws IOException {
if (key == null) { } if (value == null) {
value = new Text();
}
int newSize = 0;
while (pos < end) {
newSize = in.readLine(value, maxLineLength, Math.max(
(int) Math.min(Integer.MAX_VALUE, end - pos),
maxLineLength));
if (newSize == 0) {
break;
}
pos += newSize;
if (newSize < maxLineLength) {
break;
} // line too long. try again
LOG.info("Skipped line of size " + newSize + " at pos "
+ (pos - newSize));
}
if (newSize == 0) {
key = null;
value = null;
return false;
} else {
return true;
}
} @Override
public Text getCurrentKey() {
return key;
} @Override
public Text getCurrentValue() {
return value;
} /**
* Get the progress within the split
*/
public float getProgress() {
if (start == end) {
return 0.0f;
} else {
return Math.min(1.0f, (pos - start) / (float) (end - start));
}
} public synchronized void close() throws IOException {
if (in != null) {
in.close();
}
}
} public static class studentMapper extends Mapper<Text, Text, Text, Text> {
public void map(Text key, Text value, Context context)
throws IOException, InterruptedException {
Text newvalue = null;
String strv = value.toString().substring(
value.toString().indexOf(","));
if (key.toString().contains("student")) // student file
newvalue = new Text("student" + strv);
else
newvalue = new Text("score" + strv);
Text newkey = new Text(value.toString().substring(0,
value.toString().indexOf(",")));
context.write(newkey, newvalue);
}
} public static class studentReducer extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
List<String> students = new ArrayList<String>();
List<String> scores = new ArrayList<String>();
for (Text value : values)
if (value.toString().startsWith("student"))
students.add(value.toString().substring(8));
else
scores.add(value.toString().substring(6));
// split real results
for (String student : students)
for (String score : scores)
context.write(key, new Text(student + "," + score));
}
} public static void main(String[] args) throws Exception {
args = "hdfs://namenode:9000/user/hadoop/student/ hdfs://namenode:9000/user/hadoop/reducejoinout"
.split(" "); Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args)
.getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
} myUtils.myUtils.DeleteFolder(conf, otherArgs[1]);
conf.set("io.sort.mb", "10");
Job job = new Job(conf, "reduce join");
job.setInputFormatClass(MyTextInputFormat.class);
// job.setOutputFormatClass(SequenceFileOutputFormat.class); job.setJarByClass(reducejoin.class);
job.setMapperClass(studentMapper.class);
job.setReducerClass(studentReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}