标签：自定义 InputFormat mapreduce hadoop MapReduce io org apache import
小文件处理案例（自定义InputFormat）
1）需求
无论hdfs还是mapreduce，对于小文件都有损效率，实践中，又难免面临处理大量小文件的场景，此时，就需要有相应解决方案。将多个小文件合并成一个文件SequenceFile，SequenceFile里面存储着多个文件，存储的形式为文件路径+名称为key，文件内容为value。
2）输入数据
1:
yongpeng weidong weinan
sanfeng luozong xiaoming
2:
longlong fanfan
mazong kailun yuhang yixin
longlong fanfan
mazong kailun yuhang yixin
3:
shuaige changmo zhenqiang
dongli lingu xuanxuan
最终预期文件格式：
SEQorg.apache.hadoop.io.Text"org.apache.hadoop.io.BytesWritable ã;ŠCW
uÊÚX@ù½ü˜í W "!file:/e:/inputinputformat/one.txt 1yongpeng weidong weinan
sanfeng luozong xiaoming Y $#file:/e:/inputinputformat/three.txt 1shuaige changmo zhenqiang
dongli lingu xuanxuan € "!file:/e:/inputinputformat/two.txt Zlonglong fanfan
mazong kailun yuhang yixin
longlong fanfan
mazong kailun yuhang yixin
3）分析
小文件的优化无非以下几种方式：
（1）在数据采集的时候，就将小文件或小批数据合成大文件再上传HDFS
（2）在业务处理之前，在HDFS上使用mapreduce程序对小文件进行合并
（3）在mapreduce处理时，可采用CombineTextInputFormat提高效率
4）具体实现
本节采用自定义InputFormat的方式，处理输入小文件的问题。
（1）自定义一个类继承FileInputFormat
（2）改写RecordReader，实现一次读取一个完整文件封装为KV
（3）在输出时使用SequenceFileOutPutFormat输出合并文件
5）程序实现：
（1）自定义InputFromat
package com.atguigu.mapreduce.inputformat;
import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
// 定义类继承FileInputFormat
public class WholeFileInputformat extends FileInputFormat<NullWritable, BytesWritable>{
@Override
protected boolean isSplitable(JobContext context, Path filename) {
return false;
}
@Override
public RecordReader<NullWritable, BytesWritable> createRecordReader(InputSplit split, TaskAttemptContext context)
throws IOException, InterruptedException {
WholeRecordReader recordReader = new WholeRecordReader();
recordReader.initialize(split, context);
return recordReader;
}
} |
（2）自定义RecordReader
package com.atguigu.mapreduce.inputformat;
import java.io.IOException;
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.BytesWritable;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.NullWritable;
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.FileSplit;
public class WholeRecordReader extends RecordReader<NullWritable, BytesWritable>{
private Configuration configuration;
private FileSplit split;
private boolean processed = false;
private BytesWritable value = new BytesWritable();
@Override
public void initialize(InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException {
this.split = (FileSplit)split;
configuration = context.getConfiguration();
}
@Override
public boolean nextKeyValue() throws IOException, InterruptedException {
if (!processed) {
// 1 定义缓存区
byte[] contents = new byte[(int)split.getLength()];
FileSystem fs = null;
FSDataInputStream fis = null;
try {
// 2 获取文件系统
Path path = split.getPath();
fs = path.getFileSystem(configuration);
// 3 读取数据
fis = fs.open(path);
// 4 读取文件内容
IOUtils.readFully(fis, contents, 0, contents.length);
// 5 输出文件内容
value.set(contents, 0, contents.length);
} catch (Exception e) {
}finally {
IOUtils.closeStream(fis);
}
processed = true;
return true;
}
return false;
}
@Override
public NullWritable getCurrentKey() throws IOException, InterruptedException {
return NullWritable.get();
}
@Override
public BytesWritable getCurrentValue() throws IOException, InterruptedException {
return value;
}
@Override
public float getProgress() throws IOException, InterruptedException {
return processed? 1:0;
}
@Override
public void close() throws IOException {
}
} |
（3）SequenceFileMapper处理流程
package com.atguigu.mapreduce.inputformat;
import java.io.IOException;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
public class SequenceFileMapper extends Mapper<NullWritable, BytesWritable, Text, BytesWritable>{
Text k = new Text();
@Override
protected void setup(Mapper<NullWritable, BytesWritable, Text, BytesWritable>.Context context)
throws IOException, InterruptedException {
// 1 获取文件切片信息
FileSplit inputSplit = (FileSplit) context.getInputSplit();
// 2 获取切片名称
String name = inputSplit.getPath().toString();
// 3 设置key的输出
k.set(name);
}
@Override
protected void map(NullWritable key, BytesWritable value,
Context context)
throws IOException, InterruptedException {
context.write(k, value);
}
} |
（4）SequenceFileReducer处理流程
package com.atguigu.mapreduce.inputformat;
import java.io.IOException;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class SequenceFileReducer extends Reducer<Text, BytesWritable, Text, BytesWritable> {
@Override
protected void reduce(Text key, Iterable<BytesWritable> values, Context context)
throws IOException, InterruptedException {
context.write(key, values.iterator().next());
}
} |
（5）SequenceFileDriver处理流程
package com.atguigu.mapreduce.inputformat;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
public class SequenceFileDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
args = new String[] { "e:/input/inputinputformat", "e:/output1" };
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(SequenceFileDriver.class);
job.setMapperClass(SequenceFileMapper.class);
job.setReducerClass(SequenceFileReducer.class);
// 设置输入的inputFormat
job.setInputFormatClass(WholeFileInputformat.class);
// 设置输出的outputFormat
job.setOutputFormatClass(SequenceFileOutputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(BytesWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(BytesWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
} |
标签：自定义,
InputFormat,
mapreduce,
hadoop,
MapReduce,
io,
org,
apache,
import
From： https://blog.51cto.com/u_12654321/5843247