MapReduce任务的输入文件一般是存储在HDFS里面。输入的文件格式包括：基于行的日志文件、二进制格式文件等。这些文件一般会很大，达到数十GB，甚至更大。那么MapReduce是如何读取这些数据的呢？下面我们首先学习InputFormat接口。
InputFormat常见的接口实现类包括：TextInputFormat、KeyValueTextInputFormat、NLineInputFormat、CombineTextInputFormat和自定义InputFormat等。
1）TextInputFormat
TextInputFormat是默认的InputFormat。每条记录是一行输入。键K是LongWritable类型，存储该行在整个文件中的字节偏移量。值是这行的内容，不包括任何行终止符（换行符和回车符）。
以下是一个示例，比如，一个分片包含了如下4条文本记录。
Rich learning form Intelligent learning engine Learning more convenient From the real demand for more close to the enterprise
每条记录表示为以下键/值对：
(0,Rich learning form) (20,Intelligent learning engine) (49,Learning more convenient) (75,From the real demand for more close to the enterprise)
很明显，键并不是行号。一般情况下，很难取得行号，因为文件按字节而不是按行切分为分片。
计算公式：字符个数+空格+偏移量（自占一位）1
2）KeyValueTextInputFormat
每一行均为一条记录，被分隔符分割为key，value。可以通过在驱动类中设置conf.set(KeyValueLineRecordReader.KEY_VALUE_SEPERATOR, " ");来设定分隔符。默认分隔符是tab（\t）。
job.setInputFormatClass(KeyValueTextInputFormat.class);
以下是一个示例，输入是一个包含4条记录的分片。其中——>表示一个（水平方向的）制表符。
line1 ——>Rich，learning form line2 ——>Intelligent，learning engine line3 ——>Learning，more convenient line4 ——>From，the real demand for more close to the enterprise
每条记录表示为以下键/值对：
(Rich，learning form) (Intelligent，learning engine) (Learning，more convenient) (From the，real demand for more close to the enterprise)
此时的键是每行排在制表符之前的Text序列。
3）NLineInputFormat
如果使用NlineInputFormat，代表每个map进程处理的InputSplit不再按block块去划分，而是按NlineInputFormat指定的行数N来划分。即输入文件的总行数/N=切片数(20)，如果不整除，切片数=商+1。
以下是一个示例，仍然以上面的4行输入为例。
Rich learning form Intelligent learning engine Learning more convenient From the real demand for more close to the enterprise
例如，如果N是2，则每个输入分片包含两行。开启2个maptask。
(0,Rich learning form) (19,Intelligent learning engine)
另一个 mapper 则收到后两行：
(47,Learning more convenient) (72,From the real demand for more close to the enterprise)
这里的键和值与TextInputFormat生成的一样。
4）自定义InputFormat
1）概述
（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。
2）案例实操
小文件处理（自定义InputFormat）。
目标
3个小文件通过mapreduce输出到一个文件中：
准备3个文件a.txt b.txt c.txt
代码
MyInPutFormat.java package com.hfl.input1; 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; import java.io.IOException; public class MyInPutFormat extends FileInputFormat<NullWritable,BytesWritable> { /** * 不切分 * */ @Override protected boolean isSplitable(JobContext context, Path filename) { return false; } @Override public RecordReader<NullWritable, BytesWritable> createRecordReader(InputSplit inputSplit, TaskAttemptContext context) throws IOException, InterruptedException { MyRecordRead recordRead = new MyRecordRead(); recordRead.initialize(inputSplit,context); return recordRead; } }
MyRecordRead.java 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; import java.io.IOException; public class MyRecordRead extends RecordReader<NullWritable, BytesWritable> { private Configuration configuration; private FileSplit split; //是否数据加工 private boolean processed = false; private BytesWritable value = new BytesWritable(); /** * 初始化 * */ @Override public void initialize(InputSplit inputSplit, TaskAttemptContext context) throws IOException, InterruptedException { configuration = context.getConfiguration(); split = (FileSplit) inputSplit; } @Override public boolean nextKeyValue() throws IOException, InterruptedException { if (!processed){ //定义存储数据的缓冲区 byte[] contents = new byte[(int) split.getLength()]; FileSystem fs = null; FSDataInputStream fis = null; try { //获取文件系统 Path path = split.getPath(); fs = path.getFileSystem(configuration); //创建读数据的流 fis = fs.open(path); //读取文件 IOUtils.readFully(fis,contents,0,contents.length); //写文件 value.set(contents,0,contents.length); }catch (Exception e){ }finally { IOUtils.closeStream(fis); } //不重复读数据 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 { } }
SequenceMap.java 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; import java.io.IOException; public class SequenceMap extends Mapper<NullWritable,BytesWritable,Text,BytesWritable> { Text k = new Text(); @Override protected void setup(Context context) throws IOException, InterruptedException { //获取文件的路径 FileSplit fileSplit = (FileSplit) context.getInputSplit(); String name = fileSplit.getPath().toString(); k.set(name); } @Override protected void map(NullWritable key, BytesWritable value, Context context) throws IOException, InterruptedException { context.write(k,value); } }
SequenceReduce.java import org.apache.hadoop.io.BytesWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; import java.io.IOException; public class SequenceReduce extends Reducer<Text, BytesWritable, Text, BytesWritable> { @Override protected void reduce(Text key, Iterable<BytesWritable> values, Context context) throws IOException, InterruptedException { for (BytesWritable b : values ) { context.write(key, b); } } }
SequenceDriver.java 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; import java.io.IOException; public class SequenceDriver { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { args = new String[]{"F:\\input\\inpuformat","F:\\output\\inputformat"}; Configuration conf = new Configuration(); Job job = Job.getInstance(conf); job.setJarByClass(SequenceDriver.class); job.setMapperClass(SequenceMap.class); job.setReducerClass(SequenceReduce.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(BytesWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(BytesWritable.class); //设置自定义的inputformat job.setInputFormatClass(MyInPutFormat.class); //设置输出的二进制 job.setOutputFormatClass(SequenceFileOutputFormat.class); //输入输出路径 FileInputFormat.setInputPaths(job,new Path(args[0])); FileOutputFormat.setOutputPath(job,new Path(args[1])); job.waitForCompletion(true); } }
运行结果:
大功告成，收工!!
