Hadoop的FileInputFormat解析

在运行MapReduce程序时，输入的文件格式有：日志文件，二进制文件，数据库表等，那么针对不同的数据类型，MapReduce提供了相应的读取数据接口实现类

TextInputFormat，KeyValueTextInputFormat，NLineInputFormat，CombineTextInputFormat和自定义InputFormat

1、TextInputFormat（Text类型）

TextInputFormat是默认的FileInputFormat实现类，按行读取每条记录。

键是存储该行在整个文件中的起始字节偏移量，LongWritable类型，值是该行内容，不包含任何终止符（换行符和回车符）

2、KeyValueTextInputFormat

每一行均为一条记录，被分隔符分割成key，value，在驱动类中设定分隔符，默认分隔符是tab（\t）

//获取配置信息，job对象实例
Configuration configuration=new Configuration();
configuration.set(KeyValueLineRecordReader.KEY_VALUE_SEPERATOR,"\t");

（3）NLineInputFormat

每个map进程处理的InputSplit不再按照Block块划分，而是按照NLineInputFormat指定的行数N来划分，即输入文件的总行数/N=切片数。如果不能够整除，切片数=商+1

（4）自定义InputFormat

Hadoop自带的InputFormat类型不能满足所有应用场景时，需要自定义InputFormat来解决

自定义InputFormat步骤

（1）自定义InputFormat类继承FileInputFormat

        i）重写isSplitable方法，返回false不可切割

        ii）重写RecordReader方法，创建自定义的RecordReader对象并初始化

public class WholeInputFormat extends FileInputFormat<NullWritable, BytesWritable>{


   @Override
   protected boolean isSplitable(JobContext context, Path filename) {
      return false;
   }
   @Override
   public RecordReader<NullWritable, BytesWritable> createRecordReader(
         InputSplit inputSplit, TaskAttemptContext context)
         throws IOException, InterruptedException {
      WholeRecordReader recordReader=new WholeRecordReader();
      recordReader.initialize(null, context);
      return recordReader;
   }


}

（2）改写RecordReader，实现一次读取一个完整文件封装为KV

i）采用IO流一次读取一个文件输出到value中，因为设置了不可切片，最终把所有文件都封装在value中

ii）获取文件路径信息+名称，并设置key

public class WholeRecordReader extends RecordReader<Text,BytesWritable>{

   private BytesWritable value=new BytesWritable();
   private Text key=new Text();
    private boolean isProcess=false;
    private FileSplit fileSplit;
    private Configuration configuration;
   @Override
   public void close() throws IOException {
   }

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

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

   @Override
   public float getProgress() throws IOException, InterruptedException {
      return isProcess?1:0;
   }

   @Override
   public void initialize(InputSplit inputSplit, TaskAttemptContext context) throws IOException, InterruptedException {
      fileSplit=(FileSplit) inputSplit;
      configuration = context.getConfiguration();
   }

   @Override
   public boolean nextKeyValue() throws IOException, InterruptedException {
      
      if(!isProcess){
         FSDataInputStream inputStream=null;
         FileSystem fileSystem=null;
         try {
            byte[] bs=new byte[(int) fileSplit.getLength()];
            //获取文件系统
            Path path = fileSplit.getPath();
            fileSystem = path.getFileSystem(configuration);
            //打开文件流
            inputStream = fileSystem.open(path);
            IOUtils.readFully(inputStream, bs, 0,bs.length);
            value.set(bs, 0, bs.length);
                key.set(path.toString());
         }catch(Exception e){
            e.printStackTrace();
         }finally {
            if(inputStream!=null){
               inputStream.close();
            }
            if(fileSystem!=null){
               fileSystem.close();
            }
         }
         isProcess=true;
         return true;
      }
      
      return false;
   }

   
}

（3）在输出时使用SequenceFileOutputFormat输出合并文件

job.setInputFormatClass(WholeInputFormat.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);

Driver端

public static void main(String[] args) throws Exception {
   System.setProperty("HADOOP_USER_NAME", "root");
   Configuration configuration=new Configuration();
   Job job = Job.getInstance(configuration);
   //设置输入的inputFormat
   job.setInputFormatClass(WholeInputFormat.class);
   //设置输出的outputFormat
   job.setOutputFormatClass(SequenceFileOutputFormat.class);
   job.setMapperClass(SequenceFileMapper.class);
   job.setMapOutputKeyClass(Text.class);
   job.setMapOutputValueClass(BytesWritable.class);
   job.setReducerClass(SequenceFileReduce.class);
   job.setOutputKeyClass(Text.class);
   job.setOutputValueClass(BytesWritable.class);
   FileInputFormat.setInputPaths(job, new Path("/mapreduce/inputformat/sequencefiles"));
    FileOutputFormat.setOutputPath(job, new Path("/mapreduce/inputformat/output"));
    boolean waitForCompletion = job.waitForCompletion(true);
    System.exit(waitForCompletion==true?0:1);
}