Examples of org.apache.hadoop.mapreduce.InputFormat

org.apache.hadoop.mapreduce.InputFormat
InputFormat describes the input-specification for a Map-Reduce job.
The Map-Reduce framework relies on the InputFormat of the job to:
1. Validate the input-specification of the job.
2. Split-up the input file(s) into logical {@link InputSplit}s, each of which is then assigned to an individual {@link Mapper}.
3. Provide the {@link RecordReader} implementation to be used to gleaninput records from the logical InputSplit for processing by the {@link Mapper}.
The default behavior of file-based {@link InputFormat}s, typically sub-classes of {@link FileInputFormat}, is to split the input into logical {@link InputSplit}s based on the total size, in bytes, of the input files. However, the {@link FileSystem} blocksize of the input files is treated as an upper bound for input splits. A lower bound on the split size can be set via mapred.min.split.size.

Clearly, logical splits based on input-size is insufficient for many applications since record boundaries are to respected. In such cases, the application has to also implement a {@link RecordReader} on whom lies theresponsibility to respect record-boundaries and present a record-oriented view of the logical InputSplit to the individual task. @see InputSplit @see RecordReader @see FileInputFormat

    conf1.set(DUMMY_KEY, "STATE1");
    TaskAttemptContext context1 = new TaskAttemptContextImpl(conf1, taskId);


    // This will create a CombineFileRecordReader that itself contains a
    // DummyRecordReader.
    InputFormat inputFormat = new ChildRRInputFormat();


    Path [] files = { new Path("file1") };
    long [] lengths = { 1 };


    CombineFileSplit split = new CombineFileSplit(files, lengths);


    RecordReader rr = inputFormat.createRecordReader(split, context1);
    assertTrue("Unexpected RR type!", rr instanceof CombineFileRecordReader);


    // Verify that the initial configuration is the one being used.
    // Right after construction the dummy key should have value "STATE1"
    assertEquals("Invalid initial dummy key value", "STATE1",

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    Configuration conf = new Configuration();
    TaskAttemptContext context = new TaskAttemptContextImpl(conf, taskId);


    // This will create a CombineFileRecordReader that itself contains a
    // DummyRecordReader.
    InputFormat inputFormat = new ChildRRInputFormat();


    Path [] files = { new Path("file1"), new Path("file2") };
    long [] lengths = { 1, 1 };


    CombineFileSplit split = new CombineFileSplit(files, lengths);
    RecordReader rr = inputFormat.createRecordReader(split, context);
    assertTrue("Unexpected RR type!", rr instanceof CombineFileRecordReader);


    // first initialize() call comes from MapTask. We'll do it here.
    rr.initialize(split, context);

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   */
  @SuppressWarnings("unchecked") // getInputFormat, getOutputKeyComparator
  public static <K,V> void writePartitionFile(Job job, Sampler<K,V> sampler) 
      throws IOException, ClassNotFoundException, InterruptedException {
    Configuration conf = job.getConfiguration();
    final InputFormat inf = 
        ReflectionUtils.newInstance(job.getInputFormatClass(), conf);
    int numPartitions = job.getNumReduceTasks();
    K[] samples = sampler.getSample(inf, job);
    LOG.info("Using " + samples.length + " samples");
    RawComparator<K> comparator =

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   */
  @SuppressWarnings("unchecked") // getInputFormat, getOutputKeyComparator
  public static <K,V> void writePartitionFile(Job job, Sampler<K,V> sampler) 
      throws IOException, ClassNotFoundException, InterruptedException {
    Configuration conf = job.getConfiguration();
    final InputFormat inf = 
        ReflectionUtils.newInstance(job.getInputFormatClass(), conf);
    int numPartitions = job.getNumReduceTasks();
    K[] samples = sampler.getSample(inf, job);
    LOG.info("Using " + samples.length + " samples");
    RawComparator<K> comparator =

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    @SuppressWarnings("rawtypes")
    @Override
    public InputFormat getInputFormat() throws IOException {
        AvroStorageLog.funcCall("getInputFormat");
        InputFormat result = null;
        if(inputAvroSchema != null) {
            result = new PigAvroInputFormat(
            inputAvroSchema, ignoreBadFiles, schemaToMergedSchemaMap, useMultipleSchemas);
        } else {
            result = new TextInputFormat();

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     * IndexableLoadFunc interface implementation
     */
    @Override
    public void initialize(Configuration conf) throws IOException {
        try {
            InputFormat inputFormat = this.getInputFormat();
            TaskAttemptID id = HadoopShims.getNewTaskAttemptID();


            if (System.getenv("HADOOP_TOKEN_FILE_LOCATION") != null) {
                        conf.set(MRConfiguration.JOB_CREDENTIALS_BINARY, System.getenv("HADOOP_TOKEN_FILE_LOCATION"));
            }
            List<FileSplit> fileSplits = inputFormat.getSplits(HadoopShims.createJobContext(conf, null));
            this.readers = new IndexedStorageRecordReader[fileSplits.size()];


            int idx = 0;
            Iterator<FileSplit> it = fileSplits.iterator();
            while (it.hasNext()) {
                FileSplit fileSplit = it.next();
                TaskAttemptContext context = HadoopShims.createTaskAttemptContext(conf, id);
                IndexedStorageRecordReader r = (IndexedStorageRecordReader) inputFormat.createRecordReader(fileSplit, context);
                r.initialize(fileSplit, context);
                this.readers[idx] = r;
                idx++;
            }

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        PigInputFormat.mergeSplitSpecificConf(loadFunc, pigSplit, conf);


        // for backward compatibility
        PigInputFormat.sJob = conf;


        InputFormat inputFormat = loadFunc.getInputFormat();


        List<Long> inpLimitLists =
                (ArrayList<Long>)ObjectSerializer.deserialize(
                        conf.get("pig.inpLimits"));

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                        inputSpecificJob);
                // The above setLocation call could write to the conf within
                // the inputSpecificJob - use this updated conf


                // get the InputFormat from it and ask for splits
                InputFormat inpFormat = loadFunc.getInputFormat();
                List<InputSplit> oneInputSplits = inpFormat.getSplits(
                        HadoopShims.createJobContext(inputSpecificJob.getConfiguration(),
                                jobcontext.getJobID()));
                List<InputSplit> oneInputPigSplits = getPigSplits(
                        oneInputSplits, i, inpTargets.get(i),
                        HadoopShims.getDefaultBlockSize(fs, isFsPath? path: fs.getWorkingDirectory()),

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    conf1.set(DUMMY_KEY, "STATE1");
    TaskAttemptContext context1 = new TaskAttemptContextImpl(conf1, taskId);


    // This will create a CombineFileRecordReader that itself contains a
    // DummyRecordReader.
    InputFormat inputFormat = new ChildRRInputFormat();


    Path [] files = { new Path("file1") };
    long [] lengths = { 1 };


    CombineFileSplit split = new CombineFileSplit(files, lengths);


    RecordReader rr = inputFormat.createRecordReader(split, context1);
    assertTrue("Unexpected RR type!", rr instanceof CombineFileRecordReader);


    // Verify that the initial configuration is the one being used.
    // Right after construction the dummy key should have value "STATE1"
    assertEquals("Invalid initial dummy key value", "STATE1",

View Full Code Here

    Configuration conf = new Configuration();
    TaskAttemptContext context = new TaskAttemptContextImpl(conf, taskId);


    // This will create a CombineFileRecordReader that itself contains a
    // DummyRecordReader.
    InputFormat inputFormat = new ChildRRInputFormat();


    Path [] files = { new Path("file1"), new Path("file2") };
    long [] lengths = { 1, 1 };


    CombineFileSplit split = new CombineFileSplit(files, lengths);
    RecordReader rr = inputFormat.createRecordReader(split, context);
    assertTrue("Unexpected RR type!", rr instanceof CombineFileRecordReader);


    // first initialize() call comes from MapTask. We'll do it here.
    rr.initialize(split, context);

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