MapReduce编程场景之小文件合并
（一）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
（二）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 CombineFileInputFormat 提高效率
（三）实现
在此，我们采用第二种方式使用 MapReduce 程序来对小文件进行合并。注意： 并不是说编写一个 MR 程序来实现对这小文件的计算，只是做合并
核心实现思路：
1、编写自定义的 InputFormat
2、改写 RecordReader，实现一次 maptask 读取一个小文件的完整内容封装了一个 KV 对
3、在 Driver 类中一定要设置使用自定义的 InputFormat：
job.setInputFormatClass(WholeFileInputFormat.class)
/**
*第一步，编写自定义的 InputFormat
*@author lv_hulk
*/
import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
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;
public class WholeFileInputFormat extends FileInputFormat<NullWritable, Text> {
// 设置每个小文件不可分片,保证一个小文件生成一个 key-value 键值对
@Override
protected boolean isSplitable(JobContext context, Path file) {
return false;
}
@Override
public RecordReader<NullWritable, Text> createRecordReader(InputSplit split,
TaskAttemptContext context) throws IOException, InterruptedException {
WholeFileRecordReader reader = new WholeFileRecordReader();
reader.initialize(split, context);
return reader;
}
}
/**
*第二步，编写自定义的 RecordReader
*@author lv_hulk
*/
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.IOUtils;
import org.apache.hadoop.io.NullWritable;
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.FileSplit;
class WholeFileRecordReader extends RecordReader<NullWritable, Text> {
private FileSplit fileSplit;
private Configuration conf;
private Text value = new Text();
private boolean processed = false;
@Override
public void initialize(InputSplit split, TaskAttemptContext context)
throws IOException, InterruptedException {
this.fileSplit = (FileSplit) split;
this.conf = context.getConfiguration();
}
@Override
public boolean nextKeyValue() throws IOException, InterruptedException {
if (!processed) {
byte[] contents = new byte[(int) fileSplit.getLength()];
Path file = fileSplit.getPath();
FileSystem fs = file.getFileSystem(conf);
FSDataInputStream in = null;
try {
in = fs.open(file);
// 把输入流上的数据全部读取到 contents 字节数组里
IOUtils.readFully(in, contents, 0, contents.length);
// 把读取到的数据设置到 value 里
value.set(contents, 0, contents.length);
} finally {
IOUtils.closeStream(in);
}
processed = true;
return true;
}
return false;
}
@Override
public NullWritable getCurrentKey() throws IOException, InterruptedException {
return NullWritable.get();
}
@Override
public Text getCurrentValue() throws IOException, InterruptedException {
return value;
}
@Override
public float getProgress() throws IOException {
return processed ? 1.0f : 0.0f;
}
@Override
public void close() throws IOException {
// do nothing
}
}
/**
*第三步，编写 MapReduce 程序
*@author lv_hulk
*/
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.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
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.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class SmallFilesConvertToBigMR extends Configured implements Tool {
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new SmallFilesConvertToBigMR(), args);
System.exit(exitCode);
}
static class SmallFilesConvertToBigMRMapper extends Mapper<NullWritable, Text, Text, Text> {
private Text filenameKey;
@Override
protected void setup(Context context) throws IOException, InterruptedException {
InputSplit split = context.getInputSplit();
Path path = ((FileSplit) split).getPath();
filenameKey = new Text(path.toString());
}
@Override
protected void map(NullWritable key, Text value, Context context)
throws IOException, InterruptedException {
context.write(filenameKey, value);
}
}
static class SmallFilesConvertToBigMRReducer extends Reducer<Text, Text, NullWritable, Text> {
@Override
protected void reduce(Text filename, Iterable<Text> bytes,
Context context) throws IOException, InterruptedException {
context.write(NullWritable.get(), bytes.iterator().next());
}
}
@Override
public int run(String[] args) throws Exception {
Configuration conf = new Configuration();
conf.set("fs.defaultFS", "hdfs://hadoop02:9000");
System.setProperty("HADOOP_USER_NAME", "hadoop");
Job job = Job.getInstance(conf, "combine small files to bigfile");
job.setJarByClass(SmallFilesConvertToBigMR.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setMapperClass(SmallFilesConvertToBigMRMapper.class);
job.setReducerClass(SmallFilesConvertToBigMRReducer.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(WholeFileInputFormat.class);
// job.setOutputFormatClass(SequenceFileOutputFormat.class);
Path input = new Path("/smallfiles");
Path output = new Path("/bigfile");
FileInputFormat.setInputPaths(job, input);
FileSystem fs = FileSystem.get(conf);
if (fs.exists(output)) {
fs.delete(output, true);
}
FileOutputFormat.setOutputPath(job, output);
int status = job.waitForCompletion(true) ? 0 : 1;
return status;
}
}