Java Code Examples for org.apache.hadoop.mapreduce.lib.output.MapFileOutputFormat

The following examples show how to use org.apache.hadoop.mapreduce.lib.output.MapFileOutputFormat. These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source Project: hbase   Source File: HashTable.java    License: Apache License 2.0 6 votes vote down vote up
public Job createSubmittableJob(String[] args) throws IOException {
  Path partitionsPath = new Path(destPath, PARTITIONS_FILE_NAME);
  generatePartitions(partitionsPath);

  Job job = Job.getInstance(getConf(),
        getConf().get("mapreduce.job.name", "hashTable_" + tableHash.tableName));
  Configuration jobConf = job.getConfiguration();
  jobConf.setLong(HASH_BATCH_SIZE_CONF_KEY, tableHash.batchSize);
  jobConf.setBoolean(IGNORE_TIMESTAMPS, tableHash.ignoreTimestamps);
  job.setJarByClass(HashTable.class);

  TableMapReduceUtil.initTableMapperJob(tableHash.tableName, tableHash.initScan(),
      HashMapper.class, ImmutableBytesWritable.class, ImmutableBytesWritable.class, job);

  // use a TotalOrderPartitioner and reducers to group region output into hash files
  job.setPartitionerClass(TotalOrderPartitioner.class);
  TotalOrderPartitioner.setPartitionFile(jobConf, partitionsPath);
  job.setReducerClass(Reducer.class);  // identity reducer
  job.setNumReduceTasks(tableHash.numHashFiles);
  job.setOutputKeyClass(ImmutableBytesWritable.class);
  job.setOutputValueClass(ImmutableBytesWritable.class);
  job.setOutputFormatClass(MapFileOutputFormat.class);
  FileOutputFormat.setOutputPath(job, new Path(destPath, HASH_DATA_DIR));

  return job;
}
 
Example 2
Source Project: datawave   Source File: SafeFileOutputCommitterTest.java    License: Apache License 2.0 5 votes vote down vote up
private void testMapFileOutputCommitterInternal(int version) throws Exception {
    Job job = Job.getInstance();
    FileOutputFormat.setOutputPath(job, outDir);
    Configuration conf = job.getConfiguration();
    conf.set(MRJobConfig.TASK_ATTEMPT_ID, attempt);
    conf.setInt(FILEOUTPUTCOMMITTER_ALGORITHM_VERSION, version);
    JobContext jContext = new JobContextImpl(conf, taskID.getJobID());
    TaskAttemptContext tContext = new TaskAttemptContextImpl(conf, taskID);
    FileOutputCommitter committer = new SafeFileOutputCommitter(outDir, tContext);
    
    // setup
    committer.setupJob(jContext);
    committer.setupTask(tContext);
    
    // write output
    MapFileOutputFormat theOutputFormat = new MapFileOutputFormat();
    RecordWriter theRecordWriter = theOutputFormat.getRecordWriter(tContext);
    writeMapFileOutput(theRecordWriter, tContext);
    
    // do commit
    committer.commitTask(tContext);
    committer.commitJob(jContext);
    
    // validate output
    validateMapFileOutputContent(FileSystem.get(job.getConfiguration()), outDir);
    FileUtil.fullyDelete(new File(outDir.toString()));
}
 
Example 3
Source Project: MapReduce-Demo   Source File: MultiInOutput.java    License: MIT License 4 votes vote down vote up
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
	// 1.设置HDFS配置信息
	String namenode_ip = "192.168.17.10";
	String hdfs = "hdfs://" + namenode_ip + ":9000";
	Configuration conf = new Configuration();
	conf.set("fs.defaultFS", hdfs);
	conf.set("mapreduce.app-submission.cross-platform", "true");

	// 2.设置MapReduce作业配置信息
	String jobName = "MultInputOutput"; // 作业名称
	Job job = Job.getInstance(conf, jobName);
	job.setJarByClass(MultiInOutput.class); // 指定运行时作业类
	job.setJar("export\\MultiInOutput.jar"); // 指定本地jar包
	job.setMapOutputKeyClass(Text.class); // 设置Mapper输出Key类型
	job.setMapOutputValueClass(IntWritable.class); // 设置Mapper输出Value类型
	job.setReducerClass(MultOutputReducer.class); // 指定Reducer类
	// job.setOutputKeyClass(Text.class); //设置Reduce输出Key类型
	// job.setOutputValueClass(IntWritable.class); //设置Reduce输出Value类型
	
	// 3.指定作业多输入路径,及Map所使用的类
	MultipleInputs.addInputPath(job, new Path(hdfs+"/expr/multiinoutput/data/txt"), TextInputFormat.class, TxtFileMapper.class);
	MultipleInputs.addInputPath(job, new Path(hdfs+"/expr/multiinoutput/data/csv"), TextInputFormat.class, CsvFileMapper.class);
	
	// 定义多文件输出的文件名、输出格式、Reduce输出键类型,值类型
	MultipleOutputs.addNamedOutput(job, "f2015", TextOutputFormat.class, Text.class, IntWritable.class);
	MultipleOutputs.addNamedOutput(job, "f2016", SequenceFileOutputFormat.class, Text.class, IntWritable.class);
	MultipleOutputs.addNamedOutput(job, "f2017", MapFileOutputFormat.class, Text.class, IntWritable.class);

	// 设置作业输出路径
	String outputDir = "/expr/multiinoutput/output"; // 实验输出目录
	Path outPath = new Path(hdfs + outputDir);
	FileOutputFormat.setOutputPath(job, outPath);
	FileSystem fs = FileSystem.get(conf);
	if (fs.exists(outPath)) {
		fs.delete(outPath, true);
	}

	// 4.运行作业
	System.out.println("Job: " + jobName + " is running...");
	if (job.waitForCompletion(true)) {
		System.out.println("success!");
		System.exit(0);
	} else {
		System.out.println("failed!");
		System.exit(1);
	}
}
 
Example 4
Source Project: MapReduce-Demo   Source File: MultOutput.java    License: MIT License 4 votes vote down vote up
public static void main(String[] args) throws Exception {		
	//1.设置HDFS配置信息
	String namenode_ip = "192.168.17.10";
	String hdfs = "hdfs://" + namenode_ip + ":9000";			
	Configuration conf = new Configuration();
	conf.set("fs.defaultFS", hdfs);
	conf.set("mapreduce.app-submission.cross-platform", "true");

	//2.设置MapReduce作业配置信息
	String jobName = "MultOutput";					//作业名称
	Job job = Job.getInstance(conf, jobName);
	job.setJarByClass(MultOutput.class);			//指定运行时作业类
	job.setJar("export\\MultOutput.jar");			//指定本地jar包
	job.setMapperClass(MultOutputMapper.class);		//指定Mapper类
	job.setMapOutputKeyClass(Text.class);			//设置Mapper输出Key类型
	job.setMapOutputValueClass(IntWritable.class);	//设置Mapper输出Value类型
	job.setReducerClass(MultOutputReducer.class);	//指定Reducer类
	//job.setOutputKeyClass(Text.class);			//设置Reduce输出Key类型
	//job.setOutputValueClass(IntWritable.class); 	//设置Reduce输出Value类型
	
	//定义多文件输出的文件名、输出格式、键类型、值类型
	MultipleOutputs.addNamedOutput(job, "f2015", TextOutputFormat.class, Text.class, IntWritable.class);
	MultipleOutputs.addNamedOutput(job, "f2016", SequenceFileOutputFormat.class, Text.class, IntWritable.class);
	MultipleOutputs.addNamedOutput(job, "f2017", MapFileOutputFormat.class, Text.class, IntWritable.class);
	
	//3.设置作业输入和输出路径
	String dataDir = "/expr/multoutput/data";			//实验数据目录	
	String outputDir = "/expr/multoutput/output";		//实验输出目录
	Path inPath = new Path(hdfs + dataDir);
	Path outPath = new Path(hdfs + outputDir);
	FileInputFormat.addInputPath(job, inPath);
	FileOutputFormat.setOutputPath(job, outPath);
	FileSystem fs = FileSystem.get(conf);
	if(fs.exists(outPath)) {
		fs.delete(outPath, true);
	}
	
	//4.运行作业
	System.out.println("Job: " + jobName + " is running...");
	if(job.waitForCompletion(true)) {
		System.out.println("success!");
		System.exit(0);
	} else {
		System.out.println("failed!");
		System.exit(1);
	}
}
 
Example 5
Source Project: DataVec   Source File: SparkStorageUtils.java    License: Apache License 2.0 3 votes vote down vote up
/**
 * Save a {@code JavaRDD<List<Writable>>} to a Hadoop {@link org.apache.hadoop.io.MapFile}. Each record is
 * given a <i>unique and contiguous</i> {@link LongWritable} key, and values are stored as
 * {@link RecordWritable} instances.<br>
 * <b>Note</b>: If contiguous keys are not required, using a sequence file instead is preferable from a performance
 * point of view. Contiguous keys are often only required for non-Spark use cases, such as with
 * {@link org.datavec.hadoop.records.reader.mapfile.MapFileRecordReader}
 * <p>
 * Use {@link #restoreMapFileSequences(String, JavaSparkContext)} to restore values saved with this method.
 *
 * @param path           Path to save the MapFile
 * @param rdd            RDD to save
 * @param c              Configuration object, used to customise options for the map file
 * @param maxOutputFiles Nullable. If non-null: first coalesce the RDD to the specified size (number of partitions)
 *                       to limit the maximum number of output map files
 * @see #saveMapFileSequences(String, JavaRDD)
 * @see #saveSequenceFile(String, JavaRDD)
 */
public static void saveMapFile(String path, JavaRDD<List<Writable>> rdd, Configuration c,
                 Integer maxOutputFiles) {
    path = FilenameUtils.normalize(path, true);
    if (maxOutputFiles != null) {
        rdd = rdd.coalesce(maxOutputFiles);
    }
    JavaPairRDD<List<Writable>, Long> dataIndexPairs = rdd.zipWithIndex(); //Note: Long values are unique + contiguous, but requires a count
    JavaPairRDD<LongWritable, RecordWritable> keyedByIndex =
                    dataIndexPairs.mapToPair(new RecordSavePrepPairFunction());

    keyedByIndex.saveAsNewAPIHadoopFile(path, LongWritable.class, RecordWritable.class, MapFileOutputFormat.class,
                    c);
}
 
Example 6
Source Project: DataVec   Source File: SparkStorageUtils.java    License: Apache License 2.0 3 votes vote down vote up
/**
 * Save a {@code JavaRDD<List<List<Writable>>>} to a Hadoop {@link org.apache.hadoop.io.MapFile}. Each record is
 * given a <i>unique and contiguous</i> {@link LongWritable} key, and values are stored as
 * {@link SequenceRecordWritable} instances.<br>
 * <b>Note</b>: If contiguous keys are not required, using a sequence file instead is preferable from a performance
 * point of view. Contiguous keys are often only required for non-Spark use cases, such as with
 * {@link org.datavec.hadoop.records.reader.mapfile.MapFileSequenceRecordReader}<br>
 * <p>
 * Use {@link #restoreMapFileSequences(String, JavaSparkContext)} to restore values saved with this method.
 *
 * @param path Path to save the MapFile
 * @param rdd  RDD to save
 * @param c    Configuration object, used to customise options for the map file
 * @see #saveMapFileSequences(String, JavaRDD)
 * @see #saveSequenceFile(String, JavaRDD)
 */
public static void saveMapFileSequences(String path, JavaRDD<List<List<Writable>>> rdd, Configuration c,
                 Integer maxOutputFiles) {
    path = FilenameUtils.normalize(path, true);
    if (maxOutputFiles != null) {
        rdd = rdd.coalesce(maxOutputFiles);
    }
    JavaPairRDD<List<List<Writable>>, Long> dataIndexPairs = rdd.zipWithIndex();
    JavaPairRDD<LongWritable, SequenceRecordWritable> keyedByIndex =
                    dataIndexPairs.mapToPair(new SequenceRecordSavePrepPairFunction());

    keyedByIndex.saveAsNewAPIHadoopFile(path, LongWritable.class, SequenceRecordWritable.class,
                    MapFileOutputFormat.class, c);
}
 
Example 7
Source Project: deeplearning4j   Source File: SparkStorageUtils.java    License: Apache License 2.0 3 votes vote down vote up
/**
 * Save a {@code JavaRDD<List<Writable>>} to a Hadoop {@link org.apache.hadoop.io.MapFile}. Each record is
 * given a <i>unique and contiguous</i> {@link LongWritable} key, and values are stored as
 * {@link RecordWritable} instances.<br>
 * <b>Note</b>: If contiguous keys are not required, using a sequence file instead is preferable from a performance
 * point of view. Contiguous keys are often only required for non-Spark use cases, such as with
 * {@link org.datavec.hadoop.records.reader.mapfile.MapFileRecordReader}
 * <p>
 * Use {@link #restoreMapFileSequences(String, JavaSparkContext)} to restore values saved with this method.
 *
 * @param path           Path to save the MapFile
 * @param rdd            RDD to save
 * @param c              Configuration object, used to customise options for the map file
 * @param maxOutputFiles Nullable. If non-null: first coalesce the RDD to the specified size (number of partitions)
 *                       to limit the maximum number of output map files
 * @see #saveMapFileSequences(String, JavaRDD)
 * @see #saveSequenceFile(String, JavaRDD)
 */
public static void saveMapFile(String path, JavaRDD<List<Writable>> rdd, Configuration c,
                 Integer maxOutputFiles) {
    path = FilenameUtils.normalize(path, true);
    if (maxOutputFiles != null) {
        rdd = rdd.coalesce(maxOutputFiles);
    }
    JavaPairRDD<List<Writable>, Long> dataIndexPairs = rdd.zipWithIndex(); //Note: Long values are unique + contiguous, but requires a count
    JavaPairRDD<LongWritable, RecordWritable> keyedByIndex =
                    dataIndexPairs.mapToPair(new RecordSavePrepPairFunction());

    keyedByIndex.saveAsNewAPIHadoopFile(path, LongWritable.class, RecordWritable.class, MapFileOutputFormat.class,
                    c);
}
 
Example 8
Source Project: deeplearning4j   Source File: SparkStorageUtils.java    License: Apache License 2.0 3 votes vote down vote up
/**
 * Save a {@code JavaRDD<List<List<Writable>>>} to a Hadoop {@link org.apache.hadoop.io.MapFile}. Each record is
 * given a <i>unique and contiguous</i> {@link LongWritable} key, and values are stored as
 * {@link SequenceRecordWritable} instances.<br>
 * <b>Note</b>: If contiguous keys are not required, using a sequence file instead is preferable from a performance
 * point of view. Contiguous keys are often only required for non-Spark use cases, such as with
 * {@link org.datavec.hadoop.records.reader.mapfile.MapFileSequenceRecordReader}<br>
 * <p>
 * Use {@link #restoreMapFileSequences(String, JavaSparkContext)} to restore values saved with this method.
 *
 * @param path Path to save the MapFile
 * @param rdd  RDD to save
 * @param c    Configuration object, used to customise options for the map file
 * @see #saveMapFileSequences(String, JavaRDD)
 * @see #saveSequenceFile(String, JavaRDD)
 */
public static void saveMapFileSequences(String path, JavaRDD<List<List<Writable>>> rdd, Configuration c,
                 Integer maxOutputFiles) {
    path = FilenameUtils.normalize(path, true);
    if (maxOutputFiles != null) {
        rdd = rdd.coalesce(maxOutputFiles);
    }
    JavaPairRDD<List<List<Writable>>, Long> dataIndexPairs = rdd.zipWithIndex();
    JavaPairRDD<LongWritable, SequenceRecordWritable> keyedByIndex =
                    dataIndexPairs.mapToPair(new SequenceRecordSavePrepPairFunction());

    keyedByIndex.saveAsNewAPIHadoopFile(path, LongWritable.class, SequenceRecordWritable.class,
                    MapFileOutputFormat.class, c);
}