本文主要是介绍hadoop CombineFileInputFormat的使用，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
CombineFileInputFormat作用：将多个小文件打包成一个InputSplit提供给一个Map处理，避免因为大量小文件问题，启动大量任务
CombineFileInputFormat是一种新的inputformat，用于将多个文件合并成一个单独的split，另外，它会考虑数据的存储位置。旧版本的MultiFileInputFormat是按文件单位切分，可能造成split不均匀，如果有一个大文件则会单独由一个map处理,严重偏慢
CombineFileInputFormat是个抽象类，需要手工实现
1、Hive中可以设置:
set mapred.max.split.size=256000000; //合并的每个map大小
Set mapred.min.split.size.per.node=256000000 //控制一个节点上split的至少的大小,按mapred.max.split.size大小切分文件后，剩余大小如果超过mapred.min.split.size.per.node则作为一个分片，否则保留等待rack层处理
Set Mapred.min.split.size.per.rack=256000000 // 控制一个交换机下split至少的大小，合并碎片文件，按mapred.max.split.size分割，最后若剩余大小超过 Mapred.min.split.size.per.rack则作为单独的一分片
最后合并不同rack下的碎片，按mapred.max.split.size分割,剩下的碎片无论大小作为一个split
set hive.input.format=org.apache.hadoop.hive.ql.io.CombineHiveInputFormat
2、Mapreduce中使用
自定义类MyMultiFileInputFormat,代码参考其他博客
import java.io.IOException;
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.CombineFileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.CombineFileRecordReader;
import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit;
public class MyMultiFileInputFormat extends CombineFileInputFormat<MultiFileInputWritableComparable, Text>
{
public RecordReader<MultiFileInputWritableComparable,Text> createRecordReader(InputSplit split,TaskAttemptContext context) throws IOException
{
return new org.apache.hadoop.mapreduce.lib.input.CombineFileRecordReader<MultiFileInputWritableComparable, Text>
((CombineFileSplit)split, context, CombineFileLineRecordReader.class);
//CombineFileLineRecordReader.class为自定义类，一个split可能对应多个path则系统自带类..input.CombineFileRecordReader会通过java反射,针对不同的path分别构建自定义的CombineFileLineRecordReader去读key,value数据,具体看input.CombineFileRecordReader类源码}
}
自定义CombineFileLineRecordReader类:
import java.io.IOException;
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.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit;
import org.apache.hadoop.util.LineReader;
@SuppressWarnings("deprecation")
public class CombineFileLineRecordReader extends RecordReader<MultiFileInputWritableComparable, Text> {
private long startOffset; // offset of the chunk;
private long end; // end of the chunk;
private long pos; // current pos
private FileSystem fs;
private Path path; // path of hdfs
private MultiFileInputWritableComparable key;
private Text value; // value should be string(hadoop Text)
private FSDataInputStream fileIn;
private LineReader reader;
public CombineFileLineRecordReader(CombineFileSplit split, TaskAttemptContext context, Integer index) throws IOException
{
fs = FileSystem.get(context.getConfiguration());
this.path = split.getPath(index);
this.startOffset = split.getOffset(index);
this.end = startOffset + split.getLength(index);
boolean skipFirstLine = false;
fileIn = fs.open(path); // open the file
if (startOffset != 0) {
skipFirstLine = true;
--startOffset;
fileIn.seek(startOffset);
}
reader = new LineReader(fileIn);
if (skipFirstLine) // skip first line and re-establish "startOffset".
{
int readNum = reader.readLine(new Text(),0,(int) Math.min((long) Integer.MAX_VALUE, end - startOffset));
startOffset += readNum;
}
this.pos = startOffset;
}
public void initialize(InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException
{}
public void close() throws IOException
{
reader.close();
}
public float getProgress() throws IOException
{
if (startOffset == end) {
return 0.0f;
} else {
return Math.min(1.0f, (pos - startOffset) / (float) (end - startOffset));
}
}
public boolean nextKeyValue() throws IOException
{
if (key == null) {
key = new MultiFileInputWritableComparable();
key.setFileName(path.getName());
}
key.setOffset(pos);
if (value == null) {
value = new Text();
}
int newSize = 0;
if (pos < end) {
newSize = reader.readLine(value);
pos += newSize;
}
if (newSize == 0) {
key = null;
value = null;
return false;
} else {
return true;
}
}
public MultiFileInputWritableComparable getCurrentKey() throws IOException, InterruptedException
{
return key;
}
public Text getCurrentValue() throws IOException, InterruptedException
{
return value;
}
}
MultiFileInputWritableComparable类
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
@SuppressWarnings("rawtypes")
public class MultiFileInputWritableComparable implements WritableComparable {
private long offset; //offset of this file block
private String fileName; //filename of this block
public long getOffset() {
return offset;
}
public void setOffset(long offset) {
this.offset = offset;
}
public String getFileName() {
return fileName;
}
public void setFileName(String fileName) {
this.fileName = fileName;
}
public void readFields(DataInput in) throws IOException {
this.offset = in.readLong();
this.fileName = Text.readString(in);
}
public void write(DataOutput out) throws IOException {
out.writeLong(offset);
Text.writeString(out, fileName);
}
public int compareTo(Object object) {
MultiFileInputWritableComparable that = (MultiFileInputWritableComparable)object;
int compare = this.fileName.compareTo(that.fileName);
if(compare == 0) {
return (int)Math.signum((double)(this.offset - that.offset));
}
return compare;
}
@Override
public boolean equals(Object object) {
if(object instanceof MultiFileInputWritableComparable)
return this.compareTo(object) == 0;
return false;
}
@Override
public int hashCode() {
assert false : "hashCode not designed";
return 42; //an arbitrary constant
}
}
测试
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.reduce.IntSumReducer;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
@SuppressWarnings("deprecation")
public class MultiFileWordCount extends Configured implements Tool {
public static class MapClass extends
Mapper<MultiFileInputWritableComparable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(MultiFileInputWritableComparable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
StringTo这篇关于hadoop CombineFileInputFormat的使用的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
