Java Code Examples for org.apache.crunch.Pipeline

The following examples show how to use org.apache.crunch.Pipeline. 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: tutorials   Source File: MemPipelineUnitTest.java    License: MIT License 6 votes vote down vote up
@Test
@Ignore("Requires Hadoop binaries")
public void givenCollection_whenWriteCalled_fileWrittenSuccessfully()
    throws IOException {
    PCollection<String> inputStrings = MemPipeline.collectionOf("Hello",
        "Apache", "Crunch", Calendar.getInstance()
            .toString());
    final String outputFilePath = createOutputPath();
    Target target = To.textFile(outputFilePath);

    inputStrings.write(target);

    Pipeline pipeline = MemPipeline.getInstance();
    PCollection<String> lines = pipeline.readTextFile(outputFilePath);
    assertIterableEquals(inputStrings.materialize(), lines.materialize());
}
 
Example 2
Source Project: kite   Source File: TestCrunchDatasetsHBase.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testGeneric() throws IOException {
  String datasetName = tableName + ".TestGenericEntity";

  DatasetDescriptor descriptor = new DatasetDescriptor.Builder()
      .schemaLiteral(testGenericEntity)
      .build();

  Dataset<GenericRecord> inputDataset = repo.create("default", "in", descriptor);
  Dataset<GenericRecord> outputDataset = repo.create("default", datasetName, descriptor);

  writeRecords(inputDataset, 10);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasetsHBase.class, HBaseTestUtils.getConf());
  PCollection<GenericRecord> data = pipeline.read(
      CrunchDatasets.asSource(inputDataset));
  pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  checkRecords(outputDataset, 10, 0);
}
 
Example 3
Source Project: kite   Source File: TestCrunchDatasetsHBase.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testSourceView() throws IOException {
  String datasetName = tableName + ".TestGenericEntity";

  DatasetDescriptor descriptor = new DatasetDescriptor.Builder()
      .schemaLiteral(testGenericEntity)
      .build();

  Dataset<GenericRecord> inputDataset = repo.create("default", "in", descriptor);
  Dataset<GenericRecord> outputDataset = repo.create("default", datasetName, descriptor);

  writeRecords(inputDataset, 10);

  View<GenericRecord> inputView = inputDataset
      .from("part1", new Utf8("part1_2")).to("part1", new Utf8("part1_7"))
      .from("part2", new Utf8("part2_2")).to("part2", new Utf8("part2_7"));
  Assert.assertEquals(6, datasetSize(inputView));

  Pipeline pipeline = new MRPipeline(TestCrunchDatasetsHBase.class, HBaseTestUtils.getConf());
  PCollection<GenericRecord> data = pipeline.read(
      CrunchDatasets.asSource(inputView));
  pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  checkRecords(outputDataset, 6, 2);
}
 
Example 4
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testGeneric() throws IOException {
  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());

  // write two files, each of 5 records
  writeTestUsers(inputDataset, 5, 0);
  writeTestUsers(inputDataset, 5, 5);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputDataset));
  pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  checkTestUsers(outputDataset, 10);
}
 
Example 5
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testGenericParquet() throws IOException {
  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).format(Formats.PARQUET).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).format(Formats.PARQUET).build());

  // write two files, each of 5 records
  writeTestUsers(inputDataset, 5, 0);
  writeTestUsers(inputDataset, 5, 5);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputDataset));
  pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  checkTestUsers(outputDataset, 10);
}
 
Example 6
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testPartitionedSource() throws IOException {
  PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().hash(
      "username", 2).build();

  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).format(Formats.PARQUET).build());

  writeTestUsers(inputDataset, 10);

  PartitionKey key = new PartitionKey(0);
  Dataset<Record> inputPart0 =
      ((PartitionedDataset<Record>) inputDataset).getPartition(key, false);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputPart0));
  pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(5, datasetSize(outputDataset));
}
 
Example 7
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testPartitionedSourceAndTarget() throws IOException {
  PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().hash(
      "username", 2).build();

  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());

  writeTestUsers(inputDataset, 10);

  PartitionKey key = new PartitionKey(0);
  Dataset<Record> inputPart0 =
      ((PartitionedDataset<Record>) inputDataset).getPartition(key, false);
  Dataset<Record> outputPart0 =
      ((PartitionedDataset<Record>) outputDataset).getPartition(key, true);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputPart0));
  pipeline.write(data, CrunchDatasets.asTarget(outputPart0), Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(5, datasetSize(outputPart0));
}
 
Example 8
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testSourceView() throws IOException {
  PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().hash(
      "username", 2).build();

  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).format(Formats.PARQUET).build());

  writeTestUsers(inputDataset, 10);

  View<Record> inputView = inputDataset.with("username", "test-0");
  Assert.assertEquals(1, datasetSize(inputView));

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputView));
  pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(1, datasetSize(outputDataset));
}
 
Example 9
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testTargetView() throws IOException {
  PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().hash(
      "username", 2).build();

  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());

  writeTestUsers(inputDataset, 10);

  View<Record> inputView = inputDataset.with("username", "test-0");
  Assert.assertEquals(1, datasetSize(inputView));
  View<Record> outputView = outputDataset.with("username", "test-0");

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputView));
  pipeline.write(data, CrunchDatasets.asTarget(outputView), Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(1, datasetSize(outputDataset));
}
 
Example 10
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testTargetViewProvidedPartition() throws IOException {
    PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().provided("version").build();

    Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
            .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
    Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
            .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());

    View<Record> inputView = inputDataset.with("version", "test-version-0");

    writeTestUsers(inputView, 1);

    Assert.assertEquals(1, datasetSize(inputView));
    View<Record> outputView = outputDataset.with("version", "test-version-0");

    Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
    PCollection<GenericData.Record> data = pipeline.read(
            CrunchDatasets.asSource(inputView));
    pipeline.write(data, CrunchDatasets.asTarget(outputView), Target.WriteMode.APPEND);
    pipeline.run();

    Assert.assertEquals(1, datasetSize(outputDataset));
}
 
Example 11
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testDatasetUris() throws IOException {
  PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().hash(
      "username", 2).build();

  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());

  writeTestUsers(inputDataset, 10);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(new URIBuilder(repo.getUri(), "ns", "in").build(),
          GenericData.Record.class));
  pipeline.write(data, CrunchDatasets.asTarget(
      new URIBuilder(repo.getUri(), "ns", "out").build()), Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(10, datasetSize(outputDataset));
}
 
Example 12
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testWriteModeOverwrite() throws IOException {
  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());

  writeTestUsers(inputDataset, 1, 0);
  writeTestUsers(outputDataset, 1, 1);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputDataset));
  pipeline.write(data, CrunchDatasets.asTarget((View<Record>) outputDataset),
      Target.WriteMode.OVERWRITE);

  pipeline.run();

  checkTestUsers(outputDataset, 1);
}
 
Example 13
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 6 votes vote down vote up
@Test
public void testMultipleFileReadingFromCrunch() throws IOException {
  Dataset<Record> inputDatasetA = repo.create("ns", "inA", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());
  Dataset<Record> inputDatasetB = repo.create("ns", "inB", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());

  // write two files, each of 5 records
  writeTestUsers(inputDatasetA, 5, 0);
  writeTestUsers(inputDatasetB, 5, 5);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> dataA = pipeline.read(
      CrunchDatasets.asSource(inputDatasetA));
  PCollection<GenericData.Record> dataB = pipeline.read(
      CrunchDatasets.asSource(inputDatasetB));
  pipeline.write(dataA.union(dataB), CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  checkTestUsers(outputDataset, 10);
}
 
Example 14
Source Project: hadoop-arch-book   Source File: JoinFilterExampleCrunch.java    License: Apache License 2.0 5 votes vote down vote up
public int run(String[] args) throws Exception {

    String fooInputPath = args[0];
    String barInputPath = args[1];
    String outputPath = args[2];
    int fooValMax = Integer.parseInt(args[3]);
    int joinValMax = Integer.parseInt(args[4]);
    int numberOfReducers = Integer.parseInt(args[5]);

    Pipeline pipeline = new MRPipeline(JoinFilterExampleCrunch.class, getConf()); //<1>
    
    PCollection<String> fooLines = pipeline.readTextFile(fooInputPath);  //<2>
    PCollection<String> barLines = pipeline.readTextFile(barInputPath);

    PTable<Long, Pair<Long, Integer>> fooTable = fooLines.parallelDo(  //<3>
        new FooIndicatorFn(),
        Avros.tableOf(Avros.longs(),
        Avros.pairs(Avros.longs(), Avros.ints())));

    fooTable = fooTable.filter(new FooFilter(fooValMax));  //<4>

    PTable<Long, Integer> barTable = barLines.parallelDo(new BarIndicatorFn(),
        Avros.tableOf(Avros.longs(), Avros.ints()));

    DefaultJoinStrategy<Long, Pair<Long, Integer>, Integer> joinStrategy =   //<5>
        new DefaultJoinStrategy
          <Long, Pair<Long, Integer>, Integer>
          (numberOfReducers);

    PTable<Long, Pair<Pair<Long, Integer>, Integer>> joinedTable = joinStrategy //<6>
        .join(fooTable, barTable, JoinType.INNER_JOIN);

    PTable<Long, Pair<Pair<Long, Integer>, Integer>> filteredTable = joinedTable.filter(new JoinFilter(joinValMax));

    filteredTable.write(At.textFile(outputPath), WriteMode.OVERWRITE); //<7>

    PipelineResult result = pipeline.done();

    return result.succeeded() ? 0 : 1;
  }
 
Example 15
Source Project: tutorials   Source File: WordCount.java    License: MIT License 5 votes vote down vote up
public int run(String[] args) throws Exception {

        if (args.length != 2) {
            System.err.println("Usage: hadoop jar crunch-1.0.0-SNAPSHOT-job.jar" + " [generic options] input output");
            System.err.println();
            GenericOptionsParser.printGenericCommandUsage(System.err);
            return 1;
        }

        String inputPath = args[0];
        String outputPath = args[1];

        // Create an object to coordinate pipeline creation and execution.
        Pipeline pipeline = new MRPipeline(WordCount.class, getConf());

        // Reference a given text file as a collection of Strings.
        PCollection<String> lines = pipeline.readTextFile(inputPath);

        // Define a function that splits each line in a PCollection of Strings into
        // a PCollection made up of the individual words in the file.
        // The second argument sets the serialization format.
        PCollection<String> words = lines.parallelDo(new Tokenizer(), Writables.strings());

        // Take the collection of words and remove known stop words.
        PCollection<String> noStopWords = words.filter(new StopWordFilter());

        // The count method applies a series of Crunch primitives and returns
        // a map of the unique words in the input PCollection to their counts.
        PTable<String, Long> counts = noStopWords.count();

        // Instruct the pipeline to write the resulting counts to a text file.
        pipeline.writeTextFile(counts, outputPath);

        // Execute the pipeline as a MapReduce.
        PipelineResult result = pipeline.done();

        return result.succeeded() ? 0 : 1;
    }
 
Example 16
Source Project: tutorials   Source File: MemPipelineUnitTest.java    License: MIT License 5 votes vote down vote up
@Test
public void givenPipeLineAndSource_whenSourceRead_thenExpectedNumberOfRecordsRead() {
    Pipeline pipeline = MemPipeline.getInstance();
    Source<String> source = From.textFile(INPUT_FILE_PATH);

    PCollection<String> lines = pipeline.read(source);

    assertEquals(21, lines.asCollection()
        .getValue()
        .size());
}
 
Example 17
Source Project: tutorials   Source File: MemPipelineUnitTest.java    License: MIT License 5 votes vote down vote up
@Test
public void givenPipeLine_whenTextFileRead_thenExpectedNumberOfRecordsRead() {
    Pipeline pipeline = MemPipeline.getInstance();

    PCollection<String> lines = pipeline.readTextFile(INPUT_FILE_PATH);

    assertEquals(21, lines.asCollection()
        .getValue()
        .size());
}
 
Example 18
Source Project: tutorials   Source File: MemPipelineUnitTest.java    License: MIT License 5 votes vote down vote up
@Test
@Ignore("Requires Hadoop binaries")
public void givenPipeLine_whenWriteTextFileCalled_fileWrittenSuccessfully()
    throws IOException {
    Pipeline pipeline = MemPipeline.getInstance();
    PCollection<String> inputStrings = MemPipeline.collectionOf("Hello",
        "Apache", "Crunch", Calendar.getInstance()
            .toString());
    final String outputFilePath = createOutputPath();

    pipeline.writeTextFile(inputStrings, outputFilePath);

    PCollection<String> lines = pipeline.readTextFile(outputFilePath);
    assertIterableEquals(inputStrings.materialize(), lines.materialize());
}
 
Example 19
Source Project: hdfs2cass   Source File: LegacyHdfs2Cass.java    License: Apache License 2.0 5 votes vote down vote up
@Override
public int run(String[] args) throws Exception {

  new JCommander(this, args);

  URI outputUri = URI.create(output);

  // Our crunch job is a MapReduce job
  Pipeline pipeline = new MRPipeline(LegacyHdfs2Cass.class, getConf());

  // Parse & fetch info about target Cassandra cluster
  CassandraParams params = CassandraParams.parse(outputUri);

  // Read records from Avro files in inputFolder
  PCollection<ByteBuffer> records =
      pipeline.read(From.avroFile(inputList(input), Avros.records(ByteBuffer.class)));

  // Transform the input
  String protocol = outputUri.getScheme();
  if (protocol.equalsIgnoreCase("thrift")) {
    records
        // First convert ByteBuffers to ThriftRecords
        .parallelDo(new LegacyHdfsToThrift(), ThriftRecord.PTYPE)
        // Then group the ThriftRecords in preparation for writing them
        .parallelDo(new ThriftRecord.AsPair(), ThriftRecord.AsPair.PTYPE)
        .groupByKey(params.createGroupingOptions())
        // Finally write the ThriftRecords to Cassandra
        .write(new ThriftTarget(outputUri, params));
  }
  else if (protocol.equalsIgnoreCase("cql")) {
    records
        // In case of CQL, convert ByteBuffers to CQLRecords
        .parallelDo(new LegacyHdfsToCQL(), CQLRecord.PTYPE)
        .by(params.getKeyFn(), Avros.bytes())
        .groupByKey(params.createGroupingOptions())
        .write(new CQLTarget(outputUri, params));
  }

  // Execute the pipeline
  PipelineResult result = pipeline.done();
  return result.succeeded() ? 0 : 1;
}
 
Example 20
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 5 votes vote down vote up
@Test
public void testPartitionedSourceAndTargetWritingToTopLevel() throws IOException {
  PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().hash(
      "username", 2).build();

  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());

  writeTestUsers(inputDataset, 10);

  PartitionKey key = new PartitionKey(0);
  Dataset<Record> inputPart0 =
      ((PartitionedDataset<Record>) inputDataset).getPartition(key, false);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputPart0));
  pipeline.write(data, CrunchDatasets.asTarget(outputDataset), Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(5, datasetSize(outputDataset));

  // check all records are in the correct partition
  Dataset<Record> outputPart0 =
      ((PartitionedDataset<Record>) outputDataset).getPartition(key, false);
  Assert.assertNotNull(outputPart0);
  Assert.assertEquals(5, datasetSize(outputPart0));
}
 
Example 21
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 5 votes vote down vote up
@Test
public void testViewUris() throws IOException {
  PartitionStrategy partitionStrategy = new PartitionStrategy.Builder().hash(
      "username", 2).build();

  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).partitionStrategy(partitionStrategy).build());

  writeTestUsers(inputDataset, 10);

  URI sourceViewUri = new URIBuilder(repo.getUri(), "ns", "in").with("username",
      "test-0").build();
  View<Record> inputView = Datasets.<Record, Dataset<Record>> load(sourceViewUri,
      Record.class);
  Assert.assertEquals(1, datasetSize(inputView));

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(CrunchDatasets
      .asSource(sourceViewUri, GenericData.Record.class));
  URI targetViewUri = new URIBuilder(repo.getUri(), "ns", "out").with(
      "email", "email-0").build();
  pipeline.write(data, CrunchDatasets.asTarget(targetViewUri),
      Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(1, datasetSize(outputDataset));
}
 
Example 22
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 5 votes vote down vote up
@Test(expected = CrunchRuntimeException.class)
public void testWriteModeDefaultFailsWithExisting() throws IOException {
  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());
  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());

  writeTestUsers(inputDataset, 1, 0);
  writeTestUsers(outputDataset, 1, 0);

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputDataset));
  pipeline.write(data, CrunchDatasets.asTarget((View<Record>) outputDataset));
}
 
Example 23
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 5 votes vote down vote up
@Test
public void testSignalReadyOutputView() {
  Assume.assumeTrue(!Hadoop.isHadoop1());
  Dataset<Record> inputDataset = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());

  Dataset<Record> outputDataset = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(USER_SCHEMA).build());

  writeTestUsers(inputDataset, 10);

  View<Record> inputView = inputDataset.with("username", "test-8", "test-9");
  View<Record> outputView = outputDataset.with("username", "test-8", "test-9");
  Assert.assertEquals(2, datasetSize(inputView));

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputView));
  pipeline.write(data, CrunchDatasets.asTarget(outputView), Target.WriteMode.APPEND);
  pipeline.run();

  Assert.assertEquals(2, datasetSize(outputView));

  Assert.assertFalse("Output dataset should not be signaled ready",
      ((Signalable)outputDataset).isReady());
  Assert.assertTrue("Output view should be signaled ready",
      ((Signalable)outputView).isReady());
}
 
Example 24
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 5 votes vote down vote up
private void runCheckpointPipeline(View<Record> inputView,
    View<Record> outputView) {
  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);
  PCollection<GenericData.Record> data = pipeline.read(
      CrunchDatasets.asSource(inputView));
  pipeline.write(data, CrunchDatasets.asTarget(outputView),
      Target.WriteMode.CHECKPOINT);
  pipeline.done();
}
 
Example 25
Source Project: hdfs2cass   Source File: Hdfs2Cass.java    License: Apache License 2.0 4 votes vote down vote up
@Override
public int run(String[] args) throws Exception {

  new JCommander(this, args);

  URI outputUri = URI.create(output);

  // Our crunch job is a MapReduce job
  Configuration conf = getConf();
  conf.setBoolean(MRJobConfig.MAP_SPECULATIVE, Boolean.FALSE);
  conf.setBoolean(MRJobConfig.REDUCE_SPECULATIVE, Boolean.FALSE);
  Pipeline pipeline = new MRPipeline(Hdfs2Cass.class, conf);

  // Parse & fetch info about target Cassandra cluster
  CassandraParams params = CassandraParams.parse(outputUri);

  PCollection<GenericRecord> records =
      ((PCollection<GenericRecord>)(PCollection) pipeline.read(From.avroFile(inputList(input))));

  String protocol = outputUri.getScheme();
  if (protocol.equalsIgnoreCase("thrift")) {
    records
        // First convert ByteBuffers to ThriftRecords
        .parallelDo(new AvroToThrift(rowkey, timestamp, ttl, ignore), ThriftRecord.PTYPE)
        // Then group the ThriftRecords in preparation for writing them
        .parallelDo(new ThriftRecord.AsPair(), ThriftRecord.AsPair.PTYPE)
        .groupByKey(params.createGroupingOptions())
         // Finally write the ThriftRecords to Cassandra
        .write(new ThriftTarget(outputUri, params));
  }
  else if (protocol.equalsIgnoreCase("cql")) {
    records
        // In case of CQL, convert ByteBuffers to CQLRecords
        .parallelDo(new AvroToCQL(rowkey, timestamp, ttl, ignore), CQLRecord.PTYPE)
        .by(params.getKeyFn(), Avros.bytes())
        .groupByKey(params.createGroupingOptions())
        .write(new CQLTarget(outputUri, params));
  }

  // Execute the pipeline
  PipelineResult result = pipeline.done();
  return result.succeeded() ? 0 : 1;
}
 
Example 26
Source Project: kite   Source File: TransformTask.java    License: Apache License 2.0 4 votes vote down vote up
public PipelineResult run() throws IOException {
  boolean isLocal = (isLocal(from.getDataset()) || isLocal(to.getDataset()));
  if (isLocal) {
    // copy to avoid making changes to the caller's configuration
    Configuration conf = new Configuration(getConf());
    conf.set("mapreduce.framework.name", "local");
    setConf(conf);
  }

  if (isHive(from) || isHive(to)) {
    setConf(addHiveDelegationToken(getConf()));

    // add jars needed for metastore interaction to the classpath
    if (!isLocal) {
      Class<?> fb303Class, thriftClass;
      try {
        // attempt to use libfb303 and libthrift 0.9.2 when async was added
        fb303Class = Class.forName(
            "com.facebook.fb303.FacebookService.AsyncProcessor");
        thriftClass = Class.forName(
            "org.apache.thrift.TBaseAsyncProcessor");
      } catch (ClassNotFoundException e) {
        try {
          // fallback to 0.9.0 or earlier
          fb303Class = Class.forName(
              "com.facebook.fb303.FacebookBase");
          thriftClass = Class.forName(
              "org.apache.thrift.TBase");
        } catch (ClassNotFoundException real) {
          throw new DatasetOperationException(
              "Cannot find thrift dependencies", real);
        }
      }

      TaskUtil.configure(getConf())
          .addJarForClass(Encoder.class) // commons-codec
          .addJarForClass(Log.class) // commons-logging
          .addJarForClass(CompressorInputStream.class) // commons-compress
          .addJarForClass(ApiAdapter.class) // datanucleus-core
          .addJarForClass(JDOAdapter.class) // datanucleus-api-jdo
          .addJarForClass(SQLQuery.class) // datanucleus-rdbms
          .addJarForClass(JDOHelper.class) // jdo-api
          .addJarForClass(Transaction.class) // jta
          .addJarForClass(fb303Class) // libfb303
          .addJarForClass(thriftClass) // libthrift
          .addJarForClass(HiveMetaStore.class) // hive-metastore
          .addJarForClass(HiveConf.class); // hive-exec
    }
  }

  PType<T> toPType = ptype(to);
  MapFn<T, T> validate = new CheckEntityClass<T>(to.getType());

  Pipeline pipeline = new MRPipeline(getClass(), getConf());

  PCollection<T> collection = pipeline.read(CrunchDatasets.asSource(from))
      .parallelDo(transform, toPType).parallelDo(validate, toPType);

  if (compact) {
    // the transform must be run before partitioning
    collection = CrunchDatasets.partition(collection, to, numWriters, numPartitionWriters);
  }

  pipeline.write(collection, CrunchDatasets.asTarget(to), mode);

  PipelineResult result = pipeline.done();

  StageResult sr = Iterables.getFirst(result.getStageResults(), null);
  if (sr != null && MAP_INPUT_RECORDS != null) {
    this.count = sr.getCounterValue(MAP_INPUT_RECORDS);
  }

  return result;
}
 
Example 27
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 4 votes vote down vote up
@Test
public void testUseReaderSchema() throws IOException {

  // Create a schema with only a username, so we can test reading it
  // with an enhanced record structure.
  Schema oldRecordSchema = SchemaBuilder.record("org.kitesdk.data.user.OldUserRecord")
      .fields()
      .requiredString("username")
      .endRecord();

  // create the dataset
  Dataset<Record> in = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .schema(oldRecordSchema).build());
  Dataset<Record> out = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .schema(oldRecordSchema).build());
  Record oldUser = new Record(oldRecordSchema);
  oldUser.put("username", "user");

  DatasetWriter<Record> writer = in.newWriter();

  try {

    writer.write(oldUser);

  } finally {
    writer.close();
  }

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);

  // read data from updated dataset that has the new schema.
  // At this point, User class has the old schema
  PCollection<NewUserRecord> data = pipeline.read(CrunchDatasets.asSource(in.getUri(),
      NewUserRecord.class));

  PCollection<NewUserRecord> processed = data.parallelDo(new UserRecordIdentityFn(),
      Avros.records(NewUserRecord.class));

  pipeline.write(processed, CrunchDatasets.asTarget(out));

  DatasetReader reader = out.newReader();

  Assert.assertTrue("Pipeline failed.", pipeline.run().succeeded());

  try {

    // there should be one record that is equal to our old user generic record.
    Assert.assertEquals(oldUser, reader.next());
    Assert.assertFalse(reader.hasNext());

  } finally {
    reader.close();
  }
}
 
Example 28
Source Project: kite   Source File: TestCrunchDatasets.java    License: Apache License 2.0 4 votes vote down vote up
@Test
public void testUseReaderSchemaParquet() throws IOException {

  // Create a schema with only a username, so we can test reading it
  // with an enhanced record structure.
  Schema oldRecordSchema = SchemaBuilder.record("org.kitesdk.data.user.OldUserRecord")
      .fields()
      .requiredString("username")
      .endRecord();

  // create the dataset
  Dataset<Record> in = repo.create("ns", "in", new DatasetDescriptor.Builder()
      .format(Formats.PARQUET).schema(oldRecordSchema).build());

  Dataset<Record> out = repo.create("ns", "out", new DatasetDescriptor.Builder()
      .format(Formats.PARQUET).schema(oldRecordSchema).build());
  Record oldUser = new Record(oldRecordSchema);
  oldUser.put("username", "user");

  DatasetWriter<Record> writer = in.newWriter();

  try {

    writer.write(oldUser);

  } finally {
    writer.close();
  }

  Pipeline pipeline = new MRPipeline(TestCrunchDatasets.class);

  // read data from updated dataset that has the new schema.
  // At this point, User class has the old schema
  PCollection<NewUserRecord> data = pipeline.read(CrunchDatasets.asSource(in.getUri(),
      NewUserRecord.class));

  PCollection<NewUserRecord> processed = data.parallelDo(new UserRecordIdentityFn(),
      Avros.records(NewUserRecord.class));

  pipeline.write(processed, CrunchDatasets.asTarget(out));

  DatasetReader reader = out.newReader();

  Assert.assertTrue("Pipeline failed.", pipeline.run().succeeded());

  try {

    // there should be one record that is equal to our old user generic record.
    Assert.assertEquals(oldUser, reader.next());
    Assert.assertFalse(reader.hasNext());

  } finally {
    reader.close();
  }
}