Hadoop MapReduce编程 API入门系列之处理Excel通话记录（二十）

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　　不多说，直接上代码。

      与家庭成员之间的通话记录一份，存储在Excel文件中，如下面的数据集所示。我们需要基于这份数据，统计每个月每个家庭成员给自己打电话的次数，并按月份输出到不同文件夹。

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2016-12-12 20:04:10,576 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,577 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,578 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,579 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,579 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,580 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,581 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,582 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,582 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,583 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,583 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,584 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,585 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,585 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,586 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,587 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,587 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,588 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,589 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,589 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,590 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,590 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,592 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,592 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,593 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,594 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,594 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,595 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,596 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,596 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,597 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,597 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,598 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,599 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,599 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,600 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,600 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,601 INFO [zhouls.bigdata.myMapReduce.ExcelContactCount.ExcelContactCount$ExcelMapper] - Map processing finished
2016-12-12 20:04:10,609 INFO [org.apache.hadoop.mapred.LocalJobRunner] -
2016-12-12 20:04:10,609 INFO [org.apache.hadoop.mapred.MapTask] - Starting flush of map output
2016-12-12 20:04:10,609 INFO [org.apache.hadoop.mapred.MapTask] - Spilling map output
2016-12-12 20:04:10,610 INFO [org.apache.hadoop.mapred.MapTask] - bufstart = 0; bufend = 21978; bufvoid = 104857600
2016-12-12 20:04:10,610 INFO [org.apache.hadoop.mapred.MapTask] - kvstart = 26214396(104857584); kvend = 26210404(104841616); length = 3993/6553600
2016-12-12 20:04:10,768 INFO [org.apache.hadoop.mapred.MapTask] - Finished spill 0
2016-12-12 20:04:10,782 INFO [org.apache.hadoop.mapred.Task] - Task:attempt_local1649478955_0001_m_000000_0 is done. And is in the process of committing
2016-12-12 20:04:10,804 INFO [org.apache.hadoop.mapred.LocalJobRunner] - map
2016-12-12 20:04:10,804 INFO [org.apache.hadoop.mapred.Task] - Task ‘attempt_local1649478955_0001_m_000000_0‘ done.
2016-12-12 20:04:10,805 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Finishing task: attempt_local1649478955_0001_m_000000_0
2016-12-12 20:04:10,805 INFO [org.apache.hadoop.mapred.LocalJobRunner] - map task executor complete.
2016-12-12 20:04:10,812 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Waiting for reduce tasks
2016-12-12 20:04:10,812 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Starting task: attempt_local1649478955_0001_r_000000_0
2016-12-12 20:04:10,832 INFO [org.apache.hadoop.yarn.util.ProcfsBasedProcessTree] - ProcfsBasedProcessTree currently is supported only on Linux.
2016-12-12 20:04:10,875 INFO [org.apache.hadoop.mapred.Task] - Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@ab691c6
2016-12-12 20:04:10,884 INFO [org.apache.hadoop.mapred.ReduceTask] - Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@cbd11c4
2016-12-12 20:04:10,922 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - MergerManager: memoryLimit=1327077760, maxSingleShuffleLimit=331769440, mergeThreshold=875871360, ioSortFactor=10, memToMemMergeOutputsThreshold=10
2016-12-12 20:04:10,929 INFO [org.apache.hadoop.mapreduce.task.reduce.EventFetcher] - attempt_local1649478955_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
2016-12-12 20:04:11,007 INFO [org.apache.hadoop.mapreduce.task.reduce.LocalFetcher] - localfetcher#1 about to shuffle output of map attempt_local1649478955_0001_m_000000_0 decomp: 23978 len: 23982 to MEMORY
2016-12-12 20:04:11,021 INFO [org.apache.hadoop.mapreduce.task.reduce.InMemoryMapOutput] - Read 23978 bytes from map-output for attempt_local1649478955_0001_m_000000_0
2016-12-12 20:04:11,114 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - closeInMemoryFile -> map-output of size: 23978, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->23978
2016-12-12 20:04:11,117 INFO [org.apache.hadoop.mapreduce.task.reduce.EventFetcher] - EventFetcher is interrupted.. Returning
2016-12-12 20:04:11,119 INFO [org.apache.hadoop.mapred.LocalJobRunner] - 1 / 1 copied.
2016-12-12 20:04:11,120 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
2016-12-12 20:04:11,141 INFO [org.apache.hadoop.mapred.Merger] - Merging 1 sorted segments
2016-12-12 20:04:11,142 INFO [org.apache.hadoop.mapred.Merger] - Down to the last merge-pass, with 1 segments left of total size: 23966 bytes
2016-12-12 20:04:11,186 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - Merged 1 segments, 23978 bytes to disk to satisfy reduce memory limit
2016-12-12 20:04:11,188 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - Merging 1 files, 23982 bytes from disk
2016-12-12 20:04:11,190 INFO [org.apache.hadoop.mapreduce.task.reduce.MergeManagerImpl] - Merging 0 segments, 0 bytes from memory into reduce
2016-12-12 20:04:11,190 INFO [org.apache.hadoop.mapred.Merger] - Merging 1 sorted segments
2016-12-12 20:04:11,193 INFO [org.apache.hadoop.mapred.Merger] - Down to the last merge-pass, with 1 segments left of total size: 23966 bytes
2016-12-12 20:04:11,195 INFO [org.apache.hadoop.mapred.LocalJobRunner] - 1 / 1 copied.
2016-12-12 20:04:11,214 INFO [org.apache.hadoop.conf.Configuration.deprecation] - mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
2016-12-12 20:04:11,424 INFO [org.apache.hadoop.mapred.Task] - Task:attempt_local1649478955_0001_r_000000_0 is done. And is in the process of committing
2016-12-12 20:04:11,427 INFO [org.apache.hadoop.mapred.LocalJobRunner] - 1 / 1 copied.
2016-12-12 20:04:11,428 INFO [org.apache.hadoop.mapred.Task] - Task attempt_local1649478955_0001_r_000000_0 is allowed to commit now
2016-12-12 20:04:11,439 INFO [org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter] - Saved output of task ‘attempt_local1649478955_0001_r_000000_0‘ to file:/D:/Code/MyEclipseJavaCode/myMapReduce/out/phone/_temporary/0/task_local1649478955_0001_r_000000
2016-12-12 20:04:11,441 INFO [org.apache.hadoop.mapred.LocalJobRunner] - reduce > reduce
2016-12-12 20:04:11,442 INFO [org.apache.hadoop.mapred.Task] - Task ‘attempt_local1649478955_0001_r_000000_0‘ done.
2016-12-12 20:04:11,442 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Finishing task: attempt_local1649478955_0001_r_000000_0
2016-12-12 20:04:11,442 INFO [org.apache.hadoop.mapred.LocalJobRunner] - reduce task executor complete.
2016-12-12 20:04:11,482 INFO [org.apache.hadoop.mapreduce.Job] - map 100% reduce 100%
2016-12-12 20:04:12,484 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local1649478955_0001 completed successfully
2016-12-12 20:04:12,526 INFO [org.apache.hadoop.mapreduce.Job] - Counters: 33
File System Counters
FILE: Number of bytes read=253168
FILE: Number of bytes written=465827
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
Map-Reduce Framework
Map input records=999
Map output records=999
Map output bytes=21978
Map output materialized bytes=23982
Input split bytes=129
Combine input records=0
Combine output records=0
Reduce input groups=84
Reduce shuffle bytes=23982
Reduce input records=999
Reduce output records=84
Spilled Records=1998
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=0
CPU time spent (ms)=0
Physical memory (bytes) snapshot=0
Virtual memory (bytes) snapshot=0
Total committed heap usage (bytes)=534773760
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=102400
File Output Format Counters
Bytes Written=2213

代码

package zhouls.bigdata.myMapReduce.ExcelContactCount;

import java.io.IOException;

import java.io.InputStream;

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.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;

/**
* <p>
* An {@link org.apache.hadoop.mapreduce.InputFormat} for excel spread sheet files.
* Multiple sheets are supported
* <p/>
* Keys are the position in the file, and values are the row containing all columns for the
* particular row.
*/
public class ExcelInputFormat extends FileInputFormat<LongWritable,Text>
{
@Override
public RecordReader<LongWritable, Text> createRecordReader(InputSplit split,
TaskAttemptContext context) throws IOException, InterruptedException
{

return new ExcelRecordReader();
}

/**
* Reads excel spread sheet , where keys are offset in file and value is the row
* containing all column as a string.
*/
public class ExcelRecordReader extends RecordReader<LongWritable, Text>
{
private LongWritable key;
private Text value;
private InputStream is;
private String[] strArrayofLines;

@Override
public void initialize(InputSplit genericSplit, TaskAttemptContext context)
throws IOException, InterruptedException
{

FileSplit split = (FileSplit) genericSplit;
Configuration job = context.getConfiguration();
final Path file = split.getPath();

FileSystem fs = file.getFileSystem(job);
FSDataInputStream fileIn = fs.open(split.getPath());

is = fileIn;
String line = new ExcelParser().parseExcelData(is);//调用解析excel方法
this.strArrayofLines = line.split("\n");
}

@Override
public boolean nextKeyValue() throws IOException, InterruptedException
{
if (key == null)
{
key = new LongWritable(0);
value = new Text(strArrayofLines[0]);
} else
{
if (key.get() < (this.strArrayofLines.length - 1))
{
long pos = (int) key.get();

key.set(pos + 1);
value.set(this.strArrayofLines[(int) (pos + 1)]);

pos++;
} else
{
return false;
}
}

if (key == null || value == null)
{
return false;
} else
{
return true;
}

}

@Override
public LongWritable getCurrentKey() throws IOException,
InterruptedException
{
return key;
}

@Override
public Text getCurrentValue() throws IOException, InterruptedException
{
return value;
}

@Override
public float getProgress() throws IOException, InterruptedException
{
return 0;
}

@Override
public void close() throws IOException
{
if (is != null)
{
is.close();
}
}
}
}

package zhouls.bigdata.myMapReduce.ExcelContactCount;

import java.io.IOException;

import java.io.InputStream;
import java.util.Iterator;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.poi.hssf.usermodel.HSSFSheet;
import org.apache.poi.hssf.usermodel.HSSFWorkbook;
import org.apache.poi.ss.usermodel.Cell;
import org.apache.poi.ss.usermodel.Row;

public class ExcelParser
{
private static final Log LOG = LogFactory.getLog(ExcelParser.class);
private StringBuilder currentString = null;
private long bytesRead = 0;

public String parseExcelData(InputStream is)
{
try {
HSSFWorkbook workbook = new HSSFWorkbook(is);

// Taking first sheet from the workbook
HSSFSheet sheet = workbook.getSheetAt(0);

// Iterate through each rows from first sheet
Iterator<Row> rowIterator = sheet.iterator();
currentString = new StringBuilder();
while (rowIterator.hasNext())
{
Row row = rowIterator.next();

// For each row, iterate through each columns
Iterator<Cell> cellIterator = row.cellIterator();

while (cellIterator.hasNext())
{
Cell cell = cellIterator.next();

switch (cell.getCellType())
{
case Cell.CELL_TYPE_BOOLEAN:
bytesRead++;
currentString.append(cell.getBooleanCellValue() + "\t");
break;

case Cell.CELL_TYPE_NUMERIC:
bytesRead++;
currentString.append(cell.getNumericCellValue() + "\t");
break;

case Cell.CELL_TYPE_STRING:
bytesRead++;
currentString.append(cell.getStringCellValue() + "\t");
break;
}
}
currentString.append("\n");
}
is.close();
} catch (IOException e)
{
LOG.error("IO Exception : File not found " + e);
}
return currentString.toString();
}

public long getBytesRead()
{
return bytesRead;
}
}

package zhouls.bigdata.myMapReduce.ExcelContactCount;

import java.io.DataOutputStream;

import java.io.IOException;
import java.io.UnsupportedEncodingException;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;

public class MailRecordWriter< K, V > extends RecordWriter< K, V >
{
private static final String utf8 = "UTF-8";
private static final byte[] newline;
static
{
try
{
newline = "\n".getBytes(utf8);//换行符 "/n"不对
} catch (UnsupportedEncodingException uee)
{
throw new IllegalArgumentException("can‘t find " + utf8 + " encoding");
}
}
protected DataOutputStream out;
private final byte[] keyValueSeparator;
public MailRecordWriter(DataOutputStream out, String keyValueSeparator)
{
this.out = out;
try
{
this.keyValueSeparator = keyValueSeparator.getBytes(utf8);
} catch (UnsupportedEncodingException uee)
{
throw new IllegalArgumentException("can‘t find " + utf8 + " encoding");
}
}
public MailRecordWriter(DataOutputStream out)
{
this(out, "/t");
}
private void writeObject(Object o) throws IOException
{
if (o instanceof Text)
{
Text to = (Text) o;
out.write(to.getBytes(), 0, to.getLength());
} else
{
out.write(o.toString().getBytes(utf8));
}
}
public synchronized void write(K key, V value) throws IOException
{
boolean nullKey = key == null || key instanceof NullWritable;
boolean nullValue = value == null || value instanceof NullWritable;
if (nullKey && nullValue)
{
return;
}
if (!nullKey)
{
writeObject(key);
}
if (!(nullKey || nullValue))
{
out.write(keyValueSeparator);
}
if (!nullValue)
{
writeObject(value);
}
out.write(newline);
}
public synchronized void close(TaskAttemptContext context) throws IOException
{
out.close();
}
}

package zhouls.bigdata.myMapReduce.ExcelContactCount;

import java.io.DataOutputStream;
import java.io.IOException;
import java.util.HashMap;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapreduce.OutputCommitter;
import org.apache.hadoop.mapreduce.RecordWriter;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.ReflectionUtils;

public abstract class MailMultipleOutputFormat<K extends WritableComparable<?>, V extends Writable>
extends FileOutputFormat<K, V>
{
private MultiRecordWriter writer = null;
public RecordWriter<K, V> getRecordWriter(TaskAttemptContext job) throws IOException,
InterruptedException
{
if (writer == null)
{
writer = new MultiRecordWriter(job, getTaskOutputPath(job));
}
return writer;
}
private Path getTaskOutputPath(TaskAttemptContext conf) throws IOException
{
Path workPath = null;
OutputCommitter committer = super.getOutputCommitter(conf);
if (committer instanceof FileOutputCommitter)
{
workPath = ((FileOutputCommitter) committer).getWorkPath();
} else
{
Path outputPath = super.getOutputPath(conf);
if (outputPath == null)
{
throw new IOException("Undefined job output-path");
}
workPath = outputPath;
}
return workPath;
}
/**通过key, value, conf来确定输出文件名（含扩展名）*/
protected abstract String generateFileNameForKeyValue(K key, V value, Configuration conf);

public class MultiRecordWriter extends RecordWriter<K, V>
{
/**RecordWriter的缓存*/
private HashMap<String, RecordWriter<K, V>> recordWriters = null;
private TaskAttemptContext job = null;
/**输出目录*/
private Path workPath = null;
public MultiRecordWriter(TaskAttemptContext job, Path workPath)
{
super();
this.job = job;
this.workPath = workPath;
recordWriters = new HashMap<String, RecordWriter<K, V>>();
}
@Override
public void close(TaskAttemptContext context) throws IOException, InterruptedException
{
Iterator<RecordWriter<K, V>> values = this.recordWriters.values().iterator();
while (values.hasNext())
{
values.next().close(context);
}
this.recordWriters.clear();
}
@Override
public void write(K key, V value) throws IOException, InterruptedException
{
//得到输出文件名
String baseName = generateFileNameForKeyValue(key, value, job.getConfiguration());
RecordWriter<K, V> rw = this.recordWriters.get(baseName);
if (rw == null)
{
rw = getBaseRecordWriter(job, baseName);
this.recordWriters.put(baseName, rw);
}
rw.write(key, value);
}
// ${mapred.out.dir}/_temporary/_${taskid}/${nameWithExtension}
private RecordWriter<K, V> getBaseRecordWriter(TaskAttemptContext job, String baseName)
throws IOException, InterruptedException
{
Configuration conf = job.getConfiguration();
boolean isCompressed = getCompressOutput(job);
String keyValueSeparator = "\t";//key value 分隔符
RecordWriter<K, V> recordWriter = null;
if (isCompressed)
{
Class<? extends CompressionCodec> codecClass = getOutputCompressorClass(job,
GzipCodec.class);
CompressionCodec codec = ReflectionUtils.newInstance(codecClass, conf);
Path file = new Path(workPath, baseName + codec.getDefaultExtension());
FSDataOutputStream fileOut = file.getFileSystem(conf).create(file, false);
recordWriter = new MailRecordWriter<K, V>(new DataOutputStream(codec
.createOutputStream(fileOut)), keyValueSeparator);
} else
{
Path file = new Path(workPath, baseName);
FSDataOutputStream fileOut = file.getFileSystem(conf).create(file, false);
recordWriter = new MailRecordWriter<K, V>(fileOut, keyValueSeparator);
}
return recordWriter;
}
}
}

package zhouls.bigdata.myMapReduce.ExcelContactCount;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
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.Reducer.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import zhouls.bigdata.myMapReduce.ExcelContactCount.MailMultipleOutputFormat;

public class ExcelContactCount extends Configured implements Tool
{
private static Logger logger = LoggerFactory.getLogger(ExcelContactCount.class);

public static class ExcelMapper extends
Mapper<LongWritable, Text, Text, Text>
{

private static Logger LOG = LoggerFactory.getLogger(ExcelMapper.class);
private Text pkey = new Text();
private Text pvalue = new Text();

/**
* Excel Spreadsheet is supplied in string form to the mapper. We are
* simply emitting them for viewing on HDFS.
*/
public void map(LongWritable key, Text value, Context context)
throws InterruptedException, IOException
{
//1.0, 老爸, 13999123786, 2014-12-20
String line = value.toString();
String[] records = line.split("\\s+");
String[] months = records[3].split("-");//获取月份
pkey.set(records[1] + "\t" + months[1]);//昵称+月份
pvalue.set(records[2]);//手机号
context.write(pkey, pvalue);
LOG.info("Map processing finished");
}
}

public static class PhoneReducer extends Reducer<Text, Text, Text, Text>
{
private Text pvalue = new Text();

protected void reduce(Text Key, Iterable<Text> Values, Context context)
throws IOException, InterruptedException
{
int sum = 0;
Text outKey = Values.iterator().next();
for (Text value : Values)
{
sum++;
}
pvalue.set(outKey+"\t"+sum);
context.write(Key, pvalue);
}
}

public static class PhoneOutputFormat extends
MailMultipleOutputFormat<Text, Text>
{

protected String generateFileNameForKeyValue(Text key,
Text value, Configuration conf)
{
//name+month
String[] records = key.toString().split("\t");
return records[1] + ".txt";
}

}

public int run(String[] arg0) throws Exception {
Configuration conf = new Configuration();// 配置文件对象
Path mypath = new Path(arg0[1]);//下标为1，即是输出路径
FileSystem hdfs = mypath.getFileSystem(conf);// 创建输出路径
if (hdfs.isDirectory(mypath))
{
hdfs.delete(mypath, true);
}
logger.info("Driver started");

Job job = new Job();
job.setJarByClass(ExcelContactCount.class);
job.setJobName("Excel Record Reader");
job.setMapperClass(ExcelMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setInputFormatClass(ExcelInputFormat.class);//自定义输入格式

job.setReducerClass(PhoneReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setOutputFormatClass(PhoneOutputFormat.class);//自定义输出格式

FileInputFormat.addInputPath(job, new Path(arg0[0]));// 文件输入路径
FileOutputFormat.setOutputPath(job, new Path(arg0[1]));// 文件输出路径
job.waitForCompletion(true);

return 0;
}

public static void main(String[] args) throws Exception
{
// String[] args0 = { "hdfs://HadoopMaster:9000/phone/phone.xls",
// "hdfs://HadoopMaster:9000/out/phone" };



//本地路径
String[] args0 = { "./data/phone/phone.xls",
"out/phone"};

int ec = ToolRunner.run( new Configuration(), new ExcelContactCount(), args0);
System. exit(ec);
}

}

Hadoop MapReduce编程 API入门系列之处理Excel通话记录（二十）

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原文地址：http://www.cnblogs.com/zlslch/p/6165817.html