Java Code Examples for org.apache.flink.api.java.DataSet#count()

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Example 1

Source File: CountCollectITCase.java From Flink-CEPplus with Apache License 2.0

6 votes

@Test
public void testSimple() throws Exception {
	ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

	Integer[] input = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

	DataSet<Integer> data = env.fromElements(input);

	// count
	long numEntries = data.count();
	assertEquals(10, numEntries);

	// collect
	ArrayList<Integer> list = (ArrayList<Integer>) data.collect();
	assertArrayEquals(input, list.toArray());
}

Example 2

Source File: CountCollectITCase.java From flink with Apache License 2.0

6 votes

@Test
public void testSimple() throws Exception {
	ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

	Integer[] input = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

	DataSet<Integer> data = env.fromElements(input);

	// count
	long numEntries = data.count();
	assertEquals(10, numEntries);

	// collect
	ArrayList<Integer> list = (ArrayList<Integer>) data.collect();
	assertArrayEquals(input, list.toArray());
}

Example 3

Source File: CountCollectITCase.java From flink with Apache License 2.0

6 votes

@Test
public void testSimple() throws Exception {
	ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

	Integer[] input = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

	DataSet<Integer> data = env.fromElements(input);

	// count
	long numEntries = data.count();
	assertEquals(10, numEntries);

	// collect
	ArrayList<Integer> list = (ArrayList<Integer>) data.collect();
	assertArrayEquals(input, list.toArray());
}

Example 4

Source File: CountCollectITCase.java From Flink-CEPplus with Apache License 2.0

5 votes

@Test
public void testAdvanced() throws Exception {
	ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
	env.getConfig().disableObjectReuse();

	DataSet<Integer> data = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
	DataSet<Integer> data2 = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

	DataSet<Tuple2<Integer, Integer>> data3 = data.cross(data2);

	// count
	long numEntries = data3.count();
	assertEquals(100, numEntries);

	// collect
	ArrayList<Tuple2<Integer, Integer>> list = (ArrayList<Tuple2<Integer, Integer>>) data3.collect();

	// set expected entries in a hash map to true
	HashMap<Tuple2<Integer, Integer>, Boolean> expected = new HashMap<Tuple2<Integer, Integer>, Boolean>();
	for (int i = 1; i <= 10; i++) {
		for (int j = 1; j <= 10; j++) {
			expected.put(new Tuple2<Integer, Integer>(i, j), true);
		}
	}

	// check if all entries are contained in the hash map
	for (int i = 0; i < 100; i++) {
		Tuple2<Integer, Integer> element = list.get(i);
		assertEquals(expected.get(element), true);
		expected.remove(element);
	}
}

Example 5

Source File: CountCollectITCase.java From flink with Apache License 2.0

5 votes

@Test
public void testAdvanced() throws Exception {
	ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
	env.getConfig().disableObjectReuse();

	DataSet<Integer> data = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
	DataSet<Integer> data2 = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

	DataSet<Tuple2<Integer, Integer>> data3 = data.cross(data2);

	// count
	long numEntries = data3.count();
	assertEquals(100, numEntries);

	// collect
	ArrayList<Tuple2<Integer, Integer>> list = (ArrayList<Tuple2<Integer, Integer>>) data3.collect();

	// set expected entries in a hash map to true
	HashMap<Tuple2<Integer, Integer>, Boolean> expected = new HashMap<Tuple2<Integer, Integer>, Boolean>();
	for (int i = 1; i <= 10; i++) {
		for (int j = 1; j <= 10; j++) {
			expected.put(new Tuple2<Integer, Integer>(i, j), true);
		}
	}

	// check if all entries are contained in the hash map
	for (int i = 0; i < 100; i++) {
		Tuple2<Integer, Integer> element = list.get(i);
		assertEquals(expected.get(element), true);
		expected.remove(element);
	}
}

Example 6

Source File: CountCollectITCase.java From flink with Apache License 2.0

5 votes

@Test
public void testAdvanced() throws Exception {
	ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
	env.getConfig().disableObjectReuse();

	DataSet<Integer> data = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
	DataSet<Integer> data2 = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

	DataSet<Tuple2<Integer, Integer>> data3 = data.cross(data2);

	// count
	long numEntries = data3.count();
	assertEquals(100, numEntries);

	// collect
	ArrayList<Tuple2<Integer, Integer>> list = (ArrayList<Tuple2<Integer, Integer>>) data3.collect();

	// set expected entries in a hash map to true
	HashMap<Tuple2<Integer, Integer>, Boolean> expected = new HashMap<Tuple2<Integer, Integer>, Boolean>();
	for (int i = 1; i <= 10; i++) {
		for (int j = 1; j <= 10; j++) {
			expected.put(new Tuple2<Integer, Integer>(i, j), true);
		}
	}

	// check if all entries are contained in the hash map
	for (int i = 0; i < 100; i++) {
		Tuple2<Integer, Integer> element = list.get(i);
		assertEquals(expected.get(element), true);
		expected.remove(element);
	}
}

Example 7

Source File: FlinkCubingByLayer.java From kylin-on-parquet-v2 with Apache License 2.0

4 votes

@Override
protected void execute(OptionsHelper optionsHelper) throws Exception {
    String metaUrl = optionsHelper.getOptionValue(OPTION_META_URL);
    String hiveTable = optionsHelper.getOptionValue(OPTION_INPUT_TABLE);
    String inputPath = optionsHelper.getOptionValue(OPTION_INPUT_PATH);
    String cubeName = optionsHelper.getOptionValue(OPTION_CUBE_NAME);
    String segmentId = optionsHelper.getOptionValue(OPTION_SEGMENT_ID);
    String outputPath = optionsHelper.getOptionValue(OPTION_OUTPUT_PATH);
    String enableObjectReuseOptValue = optionsHelper.getOptionValue(OPTION_ENABLE_OBJECT_REUSE);

    boolean enableObjectReuse = false;
    if (enableObjectReuseOptValue != null && !enableObjectReuseOptValue.isEmpty()) {
        enableObjectReuse = true;
    }

    Job job = Job.getInstance();
    FileSystem fs = HadoopUtil.getWorkingFileSystem();
    HadoopUtil.deletePath(job.getConfiguration(), new Path(outputPath));

    final SerializableConfiguration sConf = new SerializableConfiguration(job.getConfiguration());
    KylinConfig envConfig = AbstractHadoopJob.loadKylinConfigFromHdfs(sConf, metaUrl);

    final CubeInstance cubeInstance = CubeManager.getInstance(envConfig).getCube(cubeName);
    final CubeDesc cubeDesc = cubeInstance.getDescriptor();
    final CubeSegment cubeSegment = cubeInstance.getSegmentById(segmentId);

    logger.info("DataSet input path : {}", inputPath);
    logger.info("DataSet output path : {}", outputPath);

    int countMeasureIndex = 0;
    for (MeasureDesc measureDesc : cubeDesc.getMeasures()) {
        if (measureDesc.getFunction().isCount() == true) {
            break;
        } else {
            countMeasureIndex++;
        }
    }

    final CubeStatsReader cubeStatsReader = new CubeStatsReader(cubeSegment, envConfig);
    boolean[] needAggr = new boolean[cubeDesc.getMeasures().size()];
    boolean allNormalMeasure = true;
    for (int i = 0; i < cubeDesc.getMeasures().size(); i++) {
        needAggr[i] = !cubeDesc.getMeasures().get(i).getFunction().getMeasureType().onlyAggrInBaseCuboid();
        allNormalMeasure = allNormalMeasure && needAggr[i];
    }

    logger.info("All measure are normal (agg on all cuboids) ? : " + allNormalMeasure);

    boolean isSequenceFile = JoinedFlatTable.SEQUENCEFILE.equalsIgnoreCase(envConfig.getFlatTableStorageFormat());

    ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    if (enableObjectReuse) {
        env.getConfig().enableObjectReuse();
    }
    env.getConfig().registerKryoType(PercentileCounter.class);
    env.getConfig().registerTypeWithKryoSerializer(PercentileCounter.class, PercentileCounterSerializer.class);

    DataSet<String[]> hiveDataSet = FlinkUtil.readHiveRecords(isSequenceFile, env, inputPath, hiveTable, job);

    DataSet<Tuple2<ByteArray, Object[]>> encodedBaseDataSet = hiveDataSet.mapPartition(
            new EncodeBaseCuboidMapPartitionFunction(cubeName, segmentId, metaUrl, sConf));

    Long totalCount = 0L;
    if (envConfig.isFlinkSanityCheckEnabled()) {
        totalCount = encodedBaseDataSet.count();
    }

    final BaseCuboidReduceGroupFunction baseCuboidReducerFunction = new BaseCuboidReduceGroupFunction(cubeName, metaUrl, sConf);

    BaseCuboidReduceGroupFunction reducerFunction = baseCuboidReducerFunction;
    if (!allNormalMeasure) {
        reducerFunction = new CuboidReduceGroupFunction(cubeName, metaUrl, sConf, needAggr);
    }

    final int totalLevels = cubeSegment.getCuboidScheduler().getBuildLevel();
    DataSet<Tuple2<ByteArray, Object[]>>[] allDataSets = new DataSet[totalLevels + 1];
    int level = 0;

    // aggregate to calculate base cuboid
    allDataSets[0] = encodedBaseDataSet.groupBy(0).reduceGroup(baseCuboidReducerFunction);

    sinkToHDFS(allDataSets[0], metaUrl, cubeName, cubeSegment, outputPath, 0, Job.getInstance(), envConfig);

    CuboidMapPartitionFunction mapPartitionFunction = new CuboidMapPartitionFunction(cubeName, segmentId, metaUrl, sConf);

    for (level = 1; level <= totalLevels; level++) {
        allDataSets[level] = allDataSets[level - 1].mapPartition(mapPartitionFunction).groupBy(0).reduceGroup(reducerFunction);
        if (envConfig.isFlinkSanityCheckEnabled()) {
            sanityCheck(allDataSets[level], totalCount, level, cubeStatsReader, countMeasureIndex);
        }
        sinkToHDFS(allDataSets[level], metaUrl, cubeName, cubeSegment, outputPath, level, Job.getInstance(), envConfig);
    }

    env.execute("Cubing for : " + cubeName + " segment " + segmentId);
    logger.info("Finished on calculating all level cuboids.");
    logger.info("HDFS: Number of bytes written=" + FlinkBatchCubingJobBuilder2.getFileSize(outputPath, fs));
}

Example 8

Source File: FlinkCubingByLayer.java From kylin with Apache License 2.0

4 votes

@Override
protected void execute(OptionsHelper optionsHelper) throws Exception {
    String metaUrl = optionsHelper.getOptionValue(OPTION_META_URL);
    String hiveTable = optionsHelper.getOptionValue(OPTION_INPUT_TABLE);
    String inputPath = optionsHelper.getOptionValue(OPTION_INPUT_PATH);
    String cubeName = optionsHelper.getOptionValue(OPTION_CUBE_NAME);
    String segmentId = optionsHelper.getOptionValue(OPTION_SEGMENT_ID);
    String outputPath = optionsHelper.getOptionValue(OPTION_OUTPUT_PATH);
    String enableObjectReuseOptValue = optionsHelper.getOptionValue(OPTION_ENABLE_OBJECT_REUSE);

    boolean enableObjectReuse = false;
    if (enableObjectReuseOptValue != null && !enableObjectReuseOptValue.isEmpty()) {
        enableObjectReuse = true;
    }

    Job job = Job.getInstance();
    FileSystem fs = HadoopUtil.getWorkingFileSystem();
    HadoopUtil.deletePath(job.getConfiguration(), new Path(outputPath));

    final SerializableConfiguration sConf = new SerializableConfiguration(job.getConfiguration());
    KylinConfig envConfig = AbstractHadoopJob.loadKylinConfigFromHdfs(sConf, metaUrl);

    final CubeInstance cubeInstance = CubeManager.getInstance(envConfig).getCube(cubeName);
    final CubeDesc cubeDesc = cubeInstance.getDescriptor();
    final CubeSegment cubeSegment = cubeInstance.getSegmentById(segmentId);

    logger.info("DataSet input path : {}", inputPath);
    logger.info("DataSet output path : {}", outputPath);

    int countMeasureIndex = 0;
    for (MeasureDesc measureDesc : cubeDesc.getMeasures()) {
        if (measureDesc.getFunction().isCount() == true) {
            break;
        } else {
            countMeasureIndex++;
        }
    }

    final CubeStatsReader cubeStatsReader = new CubeStatsReader(cubeSegment, envConfig);
    boolean[] needAggr = new boolean[cubeDesc.getMeasures().size()];
    boolean allNormalMeasure = true;
    for (int i = 0; i < cubeDesc.getMeasures().size(); i++) {
        needAggr[i] = !cubeDesc.getMeasures().get(i).getFunction().getMeasureType().onlyAggrInBaseCuboid();
        allNormalMeasure = allNormalMeasure && needAggr[i];
    }

    logger.info("All measure are normal (agg on all cuboids) ? : " + allNormalMeasure);

    boolean isSequenceFile = JoinedFlatTable.SEQUENCEFILE.equalsIgnoreCase(envConfig.getFlatTableStorageFormat());

    ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    if (enableObjectReuse) {
        env.getConfig().enableObjectReuse();
    }
    env.getConfig().registerKryoType(PercentileCounter.class);
    env.getConfig().registerTypeWithKryoSerializer(PercentileCounter.class, PercentileCounterSerializer.class);

    DataSet<String[]> hiveDataSet = FlinkUtil.readHiveRecords(isSequenceFile, env, inputPath, hiveTable, job);

    DataSet<Tuple2<ByteArray, Object[]>> encodedBaseDataSet = hiveDataSet.mapPartition(
            new EncodeBaseCuboidMapPartitionFunction(cubeName, segmentId, metaUrl, sConf));

    Long totalCount = 0L;
    if (envConfig.isFlinkSanityCheckEnabled()) {
        totalCount = encodedBaseDataSet.count();
    }

    final BaseCuboidReduceGroupFunction baseCuboidReducerFunction = new BaseCuboidReduceGroupFunction(cubeName, metaUrl, sConf);

    BaseCuboidReduceGroupFunction reducerFunction = baseCuboidReducerFunction;
    if (!allNormalMeasure) {
        reducerFunction = new CuboidReduceGroupFunction(cubeName, metaUrl, sConf, needAggr);
    }

    final int totalLevels = cubeSegment.getCuboidScheduler().getBuildLevel();
    DataSet<Tuple2<ByteArray, Object[]>>[] allDataSets = new DataSet[totalLevels + 1];
    int level = 0;

    // aggregate to calculate base cuboid
    allDataSets[0] = encodedBaseDataSet.groupBy(0).reduceGroup(baseCuboidReducerFunction);

    sinkToHDFS(allDataSets[0], metaUrl, cubeName, cubeSegment, outputPath, 0, Job.getInstance(), envConfig);

    CuboidMapPartitionFunction mapPartitionFunction = new CuboidMapPartitionFunction(cubeName, segmentId, metaUrl, sConf);

    for (level = 1; level <= totalLevels; level++) {
        allDataSets[level] = allDataSets[level - 1].mapPartition(mapPartitionFunction).groupBy(0).reduceGroup(reducerFunction);
        if (envConfig.isFlinkSanityCheckEnabled()) {
            sanityCheck(allDataSets[level], totalCount, level, cubeStatsReader, countMeasureIndex);
        }
        sinkToHDFS(allDataSets[level], metaUrl, cubeName, cubeSegment, outputPath, level, Job.getInstance(), envConfig);
    }

    env.execute("Cubing for : " + cubeName + " segment " + segmentId);
    logger.info("Finished on calculating all level cuboids.");
    logger.info("HDFS: Number of bytes written=" + FlinkBatchCubingJobBuilder2.getFileSize(outputPath, fs));
}