Example usage for org.apache.hadoop.mapreduce Job setJobName

List of usage examples for org.apache.hadoop.mapreduce Job setJobName

  1. HOME
  2. Java
  3. org.apache.hadoop
  4. org.apache.hadoop.mapreduce.*
  5. Job
  6. setJobName

Introduction

In this page you can find the example usage for org.apache.hadoop.mapreduce Job setJobName.

Prototype

public void setJobName(String name) throws IllegalStateException

Source Link

Document

Set the user-specified job name.

Usage

From source file:com.cotdp.hadoop.ZipFileTest.java

License:Apache License

/**
 * This test explicitly tries to read a file containing random noise as a ZIP file,
 * the expected result is a quiet failure. The Job shouldn't fail if non-ZIP data is
 * encountered.//  w w  w . j ava  2 s.  c  o  m
 * 
 * Expected result: (quiet) failure
 * 
 * @throws IOException 
 * @throws InterruptedException 
 * @throws ClassNotFoundException 
 */
public void testNonZipData() throws IOException, ClassNotFoundException, InterruptedException

{
    LOG.info("============================================================");
    LOG.info("==                Running testNonZipData()                ==");
    LOG.info("============================================================");

    // Standard stuff
    Job job = new Job(conf);
    job.setJobName(this.getClass().getSimpleName());
    job.setJarByClass(this.getClass());
    job.setMapperClass(MyMapper.class);
    job.setReducerClass(MyReducer.class);

    // 
    job.setInputFormatClass(ZipFileInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);

    // The output files will contain "Word [TAB] Count"
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);

    //
    ZipFileInputFormat.setInputPaths(job, new Path(inputPath, "random.dat"));
    TextOutputFormat.setOutputPath(job, new Path(workingPath, "Output_NonZipData"));

    //
    assertTrue(job.waitForCompletion(true));
}

From source file:com.cotdp.hadoop.ZipFileTest.java

License:Apache License

/**
 * This test refers to a corrupt (truncated) ZIP file, upon reaching the corruption
 * the Job will fail and no output will be written through the Reducer.
 * /*w w  w  .j a  v a  2 s .  c  om*/
 * Expected result: failure
 * 
 * @throws IOException 
 * @throws InterruptedException 
 * @throws ClassNotFoundException 
 */
public void testCorruptZip() throws IOException, ClassNotFoundException, InterruptedException

{
    LOG.info("============================================================");
    LOG.info("==                Running testCorruptZip()                ==");
    LOG.info("============================================================");

    // Standard stuff
    Job job = new Job(conf);
    job.setJobName(this.getClass().getSimpleName());
    job.setJarByClass(this.getClass());
    job.setMapperClass(MyMapper.class);
    job.setReducerClass(MyReducer.class);

    // 
    job.setInputFormatClass(ZipFileInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);

    // The output files will contain "Word [TAB] Count"
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);

    //
    ZipFileInputFormat.setInputPaths(job, new Path(inputPath, "corrupt.zip"));
    TextOutputFormat.setOutputPath(job, new Path(workingPath, "Output_Corrupt"));

    //
    assertFalse(job.waitForCompletion(true));
}

From source file:com.cotdp.hadoop.ZipFileTest.java

License:Apache License

/**
 * This test refers to a corrupt (truncated) ZIP file, upon reaching the corruption
 * the Mapper will ignore the corrupt entry and close the ZIP file. All previous
 * output will be treated as normal and passed through the Reducer. 
 * //from w ww . j  a va 2s . com
 * Expected result: success
 * 
 * @throws IOException 
 * @throws InterruptedException 
 * @throws ClassNotFoundException 
 */
public void testCorruptZipLenient() throws IOException, ClassNotFoundException, InterruptedException

{
    LOG.info("============================================================");
    LOG.info("==                Running testCorruptZipLenient()         ==");
    LOG.info("============================================================");

    // Standard stuff
    Job job = new Job(conf);
    job.setJobName(this.getClass().getSimpleName());
    job.setJarByClass(this.getClass());
    job.setMapperClass(MyMapper.class);
    job.setReducerClass(MyReducer.class);

    // 
    job.setInputFormatClass(ZipFileInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);

    // The output files will contain "Word [TAB] Count"
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);

    //
    ZipFileInputFormat.setLenient(true);
    ZipFileInputFormat.setInputPaths(job, new Path(inputPath, "corrupt.zip"));
    TextOutputFormat.setOutputPath(job, new Path(workingPath, "Output_CorruptLenient"));

    //
    assertTrue(job.waitForCompletion(true));
}

From source file:com.declum.squzer.example.hbase.table2file.Export.java

License:Apache License

/**
 * Sets up the actual job./* w w  w. j a  v a 2s. c o m*/
 * 
 * @param conf
 *            The current configuration.
 * @param args
 *            The command line parameters.
 * @return The newly created job.
 * @throws IOException
 *             When setting up the job fails.
 */
public static Job createSubmittableJob(Configuration conf, String[] args) throws IOException {
    String tableName = args[0];
    Path outputDir = new Path(args[1]);

    Job job = Job.getInstance(conf);
    job.setJobName(tableName);
    job.setJobName(NAME + "_" + tableName);
    job.setJarByClass(Exporter.class);
    // TODO: Allow passing filter and subset of rows/columns.
    Scan s = new Scan();
    // Optional arguments.
    int versions = args.length > 2 ? Integer.parseInt(args[2]) : 1;
    s.setMaxVersions(versions);
    long startTime = args.length > 3 ? Long.parseLong(args[3]) : 0L;
    long endTime = args.length > 4 ? Long.parseLong(args[4]) : Long.MAX_VALUE;
    s.setTimeRange(startTime, endTime);
    s.setCacheBlocks(false);
    if (conf.get(TableInputFormat.SCAN_COLUMN_FAMILY) != null) {
        s.addFamily(Bytes.toBytes(conf.get(TableInputFormat.SCAN_COLUMN_FAMILY)));
    }
    LOG.info("verisons=" + versions + ", starttime=" + startTime + ", endtime=" + endTime);
    TableMapReduceUtil.initTableMapperJob(tableName, s, Exporter.class, null, null, job);
    // No reducers. Just write straight to output files.
    job.setNumReduceTasks(0);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    job.setOutputKeyClass(ImmutableBytesWritable.class);
    job.setOutputValueClass(Result.class);
    FileOutputFormat.setOutputPath(job, outputDir);
    return job;
}

From source file:com.digitalpebble.behemoth.io.sequencefile.SequenceFileConverterJob.java

License:Apache License

public int run(String[] args) throws Exception {
    int result = 0;
    addInputOption();/*from  w w  w  . j  ava 2 s .  co  m*/
    addOutputOption();
    if (parseArguments(args) == null) {
        return -1;
    }
    Path input = getInputPath();
    Path output = getOutputPath();

    Job job = prepareJob(input, output, SequenceFileInputFormat.class, SequenceFileConverterMapper.class,
            Text.class, BehemothDocument.class, SequenceFileOutputFormat.class);
    job.setJobName("Convert Sequence File: " + input);
    job.waitForCompletion(true);

    if (log.isInfoEnabled()) {
        log.info("Conversion: done");
    }
    return result;
}

From source file:com.digitalpebble.behemoth.mahout.BehemothDocumentProcessor.java

License:Apache License

/**
 * Convert the input documents into token array using the
 * {@link StringTuple} The input documents has to be in the
 * {@link org.apache.hadoop.io.SequenceFile} format
 * //from   w w  w  .  j av  a  2 s. c om
 * @param input
 *            input directory of the documents in
 *            {@link org.apache.hadoop.io.SequenceFile} format
 * @param output
 *            output directory were the {@link StringTuple} token array of
 *            each document has to be created
 * @param type
 *            The annotation type representing the tokens
 * @param feature
 *            The name of the features holding the token value
 * @throws IOException
 * @throws ClassNotFoundException
 * @throws InterruptedException
 */
public static void tokenizeDocuments(Path input, String type, String feature, Path output)
        throws IOException, InterruptedException, ClassNotFoundException {
    Configuration conf = new Configuration();
    // this conf parameter needs to be set enable serialisation of conf
    // values
    conf.set("io.serializations", "org.apache.hadoop.io.serializer.JavaSerialization,"
            + "org.apache.hadoop.io.serializer.WritableSerialization");
    conf.set(TOKEN_TYPE, type);
    conf.set(FEATURE_NAME, feature);

    Job job = new Job(conf);
    job.setJobName("DocumentProcessor::BehemothTokenizer: input-folder: " + input);
    job.setJarByClass(BehemothDocumentProcessor.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(StringTuple.class);
    FileInputFormat.setInputPaths(job, input);
    FileOutputFormat.setOutputPath(job, output);

    job.setMapperClass(BehemothTokenizerMapper.class);
    job.setInputFormatClass(SequenceFileInputFormat.class);
    job.setNumReduceTasks(0);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    HadoopUtil.delete(conf, output);

    boolean succeeded = job.waitForCompletion(true);
    if (!succeeded)
        throw new IllegalStateException("Job failed!");
}

From source file:com.digitalpebble.behemoth.mahout.BehemothDocumentProcessor.java

License:Apache License

/**
 * Convert the input documents into token array using the
 * {@link StringTuple} The input documents has to be in the
 * {@link org.apache.hadoop.io.SequenceFile} format
 * //  w w  w .  ja va  2s.  c om
 * @param input
 *            input directory of the documents in
 *            {@link org.apache.hadoop.io.SequenceFile} format
 * @param output
 *            output directory were the {@link StringTuple} token array of
 *            each document has to be created
 * @param analyzerClass
 *            The Lucene {@link Analyzer} for tokenizing the UTF-8 text
 */
public static void tokenizeDocuments(Path input, Class<? extends Analyzer> analyzerClass, Path output,
        Configuration baseConf) throws IOException, InterruptedException, ClassNotFoundException {
    Configuration conf = new Configuration(baseConf);
    // this conf parameter needs to be set enable serialisation of conf
    // values
    conf.set("io.serializations", "org.apache.hadoop.io.serializer.JavaSerialization,"
            + "org.apache.hadoop.io.serializer.WritableSerialization");
    conf.set(ANALYZER_CLASS, analyzerClass.getName());

    Job job = new Job(conf);
    job.setJobName("DocumentProcessor::LuceneTokenizer: input-folder: " + input);
    job.setJarByClass(BehemothDocumentProcessor.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(StringTuple.class);
    FileInputFormat.setInputPaths(job, input);
    FileOutputFormat.setOutputPath(job, output);

    job.setMapperClass(LuceneTokenizerMapper.class);
    job.setInputFormatClass(SequenceFileInputFormat.class);
    job.setNumReduceTasks(0);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    HadoopUtil.delete(conf, output);

    boolean succeeded = job.waitForCompletion(true);
    if (!succeeded)
        throw new IllegalStateException("Job failed!");

}

From source file:com.digitalpebble.behemoth.mahout.BehemothDocumentProcessor.java

License:Apache License

public static void dumpLabels(Path input, Path output, Configuration baseConf)
        throws IOException, InterruptedException, ClassNotFoundException {
    Configuration conf = new Configuration(baseConf);
    // this conf parameter needs to be set enable serialisation of conf
    // values/*from w  w w  . j a  va2  s. com*/
    conf.set("io.serializations", "org.apache.hadoop.io.serializer.JavaSerialization,"
            + "org.apache.hadoop.io.serializer.WritableSerialization");

    Job job = new Job(conf);
    job.setJobName("DocumentProcessor::LabelDumper: input-folder: " + input);
    job.setJarByClass(BehemothDocumentProcessor.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(Text.class);
    FileInputFormat.setInputPaths(job, input);
    FileOutputFormat.setOutputPath(job, output);

    job.setMapperClass(BehemothLabelMapper.class);
    job.setInputFormatClass(SequenceFileInputFormat.class);
    job.setNumReduceTasks(0);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    HadoopUtil.delete(conf, output);

    boolean succeeded = job.waitForCompletion(true);
    if (!succeeded)
        throw new IllegalStateException("Job failed!");

}

From source file:com.digitalpebble.behemoth.mahout.DocumentProcessor.java

License:Apache License

/**
 * Convert the input documents into token array using the
 * {@link StringTuple} The input documents has to be in the
 * {@link org.apache.hadoop.io.SequenceFile} format
 * //from   w w w  .j  av a2  s.co m
 * @param input
 *            input directory of the documents in
 *            {@link org.apache.hadoop.io.SequenceFile} format
 * @param output
 *            output directory were the {@link StringTuple} token array of
 *            each document has to be created
 * @param type
 *            The annotation type representing the tokens
 * @param feature
 *            The name of the features holding the token value
 * @throws IOException
 * @throws ClassNotFoundException
 * @throws InterruptedException
 */
public static void tokenizeDocuments(Path input, String type, String feature, Path output)
        throws IOException, InterruptedException, ClassNotFoundException {
    Configuration conf = new Configuration();
    // this conf parameter needs to be set enable serialisation of conf
    // values
    conf.set("io.serializations", "org.apache.hadoop.io.serializer.JavaSerialization,"
            + "org.apache.hadoop.io.serializer.WritableSerialization");
    conf.set(TOKEN_TYPE, type);
    conf.set(FEATURE_NAME, feature);

    Job job = new Job(conf);
    job.setJobName("DocumentProcessor::DocumentTokenizer: input-folder: " + input);
    job.setJarByClass(DocumentProcessor.class);

    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(StringTuple.class);
    FileInputFormat.setInputPaths(job, input);
    FileOutputFormat.setOutputPath(job, output);

    job.setMapperClass(SequenceFileTokenizerMapper.class);
    job.setInputFormatClass(SequenceFileInputFormat.class);
    job.setNumReduceTasks(0);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    HadoopUtil.delete(conf, output);

    job.waitForCompletion(true);
}

From source file:com.elex.dmp.vectorizer.DictionaryVectorizer.java

License:Apache License

/**
 * Create a partial vector using a chunk of features from the input documents. The input documents has to be
 * in the {@link SequenceFile} format//from  w w  w.j a  va2 s. c  o  m
 * 
 * @param input
 *          input directory of the documents in {@link SequenceFile} format
 * @param baseConf
 *          job configuration
 * @param maxNGramSize
 *          maximum size of ngrams to generate
 * @param dictionaryFilePath
 *          location of the chunk of features and the id's
 * @param output
 *          output directory were the partial vectors have to be created
 * @param dimension
 * @param sequentialAccess
 *          output vectors should be optimized for sequential access
 * @param namedVectors
 *          output vectors should be named, retaining key (doc id) as a label
 * @param numReducers 
 *          the desired number of reducer tasks
 */
private static void makePartialVectors(Path input, Configuration baseConf, int maxNGramSize,
        Path dictionaryFilePath, Path output, int dimension, boolean sequentialAccess, boolean namedVectors,
        int numReducers) throws IOException, InterruptedException, ClassNotFoundException {

    Configuration conf = new Configuration(baseConf);
    // this conf parameter needs to be set enable serialisation of conf values
    conf.set("io.serializations", "org.apache.hadoop.io.serializer.JavaSerialization,"
            + "org.apache.hadoop.io.serializer.WritableSerialization");
    conf.setInt(PartialVectorMerger.DIMENSION, dimension);
    conf.setBoolean(PartialVectorMerger.SEQUENTIAL_ACCESS, sequentialAccess);
    conf.setBoolean(PartialVectorMerger.NAMED_VECTOR, namedVectors);
    conf.setInt(MAX_NGRAMS, maxNGramSize);
    DistributedCache.setCacheFiles(new URI[] { dictionaryFilePath.toUri() }, conf);

    Job job = new Job(conf);
    job.setJobName("DictionaryVectorizer::MakePartialVectors: input-folder: " + input + ", dictionary-file: "
            + dictionaryFilePath);
    job.setJarByClass(DictionaryVectorizer.class);

    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(StringTuple.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(VectorWritable.class);
    FileInputFormat.setInputPaths(job, input);

    FileOutputFormat.setOutputPath(job, output);

    job.setMapperClass(Mapper.class);
    job.setInputFormatClass(SequenceFileInputFormat.class);
    job.setReducerClass(TFPartialVectorReducer.class);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    job.setNumReduceTasks(numReducers);

    HadoopUtil.delete(conf, output);

    boolean succeeded = job.waitForCompletion(true);
    if (!succeeded)
        throw new IllegalStateException("Job failed!");
}