org.apache.spark.sql.catalyst.util.GenericArrayData Java Examples

@Override
public MapData nonNullRead(ColumnVector vector, int row) {
  MapColumnVector mapVector = (MapColumnVector) vector;
  int offset = (int) mapVector.offsets[row];
  long length = mapVector.lengths[row];
  List<Object> keys = Lists.newArrayListWithExpectedSize((int) length);
  List<Object> values = Lists.newArrayListWithExpectedSize((int) length);
  for (int c = 0; c < length; c++) {
    keys.add(keyReader.read(mapVector.keys, offset + c));
    values.add(valueReader.read(mapVector.values, offset + c));
  }

  return new ArrayBasedMapData(
      new GenericArrayData(keys.toArray()),
      new GenericArrayData(values.toArray()));
}
 

@Override
public GenericArrayData read(Decoder decoder, Object reuse) throws IOException {
  reusedList.clear();
  long chunkLength = decoder.readArrayStart();

  while (chunkLength > 0) {
    for (int i = 0; i < chunkLength; i += 1) {
      reusedList.add(elementReader.read(decoder, null));
    }

    chunkLength = decoder.arrayNext();
  }

  // this will convert the list to an array so it is okay to reuse the list
  return new GenericArrayData(reusedList.toArray());
}
 

@Override
public ArrayBasedMapData read(Decoder decoder, Object reuse) throws IOException {
  reusedKeyList.clear();
  reusedValueList.clear();

  long chunkLength = decoder.readArrayStart();

  while (chunkLength > 0) {
    for (int i = 0; i < chunkLength; i += 1) {
      reusedKeyList.add(keyReader.read(decoder, null));
      reusedValueList.add(valueReader.read(decoder, null));
    }

    chunkLength = decoder.arrayNext();
  }

  return new ArrayBasedMapData(
      new GenericArrayData(reusedKeyList.toArray()),
      new GenericArrayData(reusedValueList.toArray()));
}
 

@Override
public ArrayBasedMapData read(Decoder decoder, Object reuse) throws IOException {
  reusedKeyList.clear();
  reusedValueList.clear();

  long chunkLength = decoder.readMapStart();

  while (chunkLength > 0) {
    for (int i = 0; i < chunkLength; i += 1) {
      reusedKeyList.add(keyReader.read(decoder, null));
      reusedValueList.add(valueReader.read(decoder, null));
    }

    chunkLength = decoder.mapNext();
  }

  return new ArrayBasedMapData(
      new GenericArrayData(reusedKeyList.toArray()),
      new GenericArrayData(reusedValueList.toArray()));
}
 

@SuppressWarnings("unchecked")
private <T> GenericArrayData fillArray(Collection<?> values, Function<Object[], BiConsumer<Integer, T>> makeSetter) {
  Object[] array = new Object[values.size()];
  BiConsumer<Integer, T> setter = makeSetter.apply(array);

  int index = 0;
  for (Object value : values) {
    if (value == null) {
      array[index] = null;
    } else {
      setter.accept(index, (T) value);
    }

    index += 1;
  }

  return new GenericArrayData(array);
}
 

@Override
public GenericArrayData read(Decoder decoder, Object reuse) throws IOException {
  reusedList.clear();
  long chunkLength = decoder.readArrayStart();

  while (chunkLength > 0) {
    for (int i = 0; i < chunkLength; i += 1) {
      reusedList.add(elementReader.read(decoder, null));
    }

    chunkLength = decoder.arrayNext();
  }

  // this will convert the list to an array so it is okay to reuse the list
  return new GenericArrayData(reusedList.toArray());
}
 

@Override
public ArrayBasedMapData read(Decoder decoder, Object reuse) throws IOException {
  reusedKeyList.clear();
  reusedValueList.clear();

  long chunkLength = decoder.readArrayStart();

  while (chunkLength > 0) {
    for (int i = 0; i < chunkLength; i += 1) {
      reusedKeyList.add(keyReader.read(decoder, null));
      reusedValueList.add(valueReader.read(decoder, null));
    }

    chunkLength = decoder.arrayNext();
  }

  return new ArrayBasedMapData(
      new GenericArrayData(reusedKeyList.toArray()),
      new GenericArrayData(reusedValueList.toArray()));
}
 

@Override
public ArrayBasedMapData read(Decoder decoder, Object reuse) throws IOException {
  reusedKeyList.clear();
  reusedValueList.clear();

  long chunkLength = decoder.readMapStart();

  while (chunkLength > 0) {
    for (int i = 0; i < chunkLength; i += 1) {
      reusedKeyList.add(keyReader.read(decoder, null));
      reusedValueList.add(valueReader.read(decoder, null));
    }

    chunkLength = decoder.mapNext();
  }

  return new ArrayBasedMapData(
      new GenericArrayData(reusedKeyList.toArray()),
      new GenericArrayData(reusedValueList.toArray()));
}
 

@Override
public ArrayData nonNullRead(ColumnVector vector, int row) {
  ListColumnVector listVector = (ListColumnVector) vector;
  int offset = (int) listVector.offsets[row];
  int length = (int) listVector.lengths[row];
  List<Object> elements = Lists.newArrayListWithExpectedSize(length);
  for (int c = 0; c < length; ++c) {
    elements.add(elementReader.read(listVector.child, offset + c));
  }
  return new GenericArrayData(elements.toArray());
}
 

static Object convert(Field field, Object value) {
    if (value == null) {
        return null;
    }

    if (field.getMode() == Field.Mode.REPEATED) {
        // rather than recurring down we strip off the repeated mode
        // Due to serialization issues, reconstruct the type using reflection:
        // See: https://github.com/googleapis/google-cloud-java/issues/3942
        LegacySQLTypeName fType = LegacySQLTypeName.valueOfStrict(field.getType().name());
        Field nestedField = Field.newBuilder(field.getName(), fType, field.getSubFields())
                // As long as this is not repeated it works, but technically arrays cannot contain
                // nulls, so select required instead of nullable.
                .setMode(Field.Mode.REQUIRED)
                .build();

        List<Object> valueList = (List<Object>) value;

        return new GenericArrayData(valueList.stream().map(v -> convert(nestedField, v)).collect(Collectors.toList()));
    }

    if (LegacySQLTypeName.INTEGER.equals(field.getType()) ||
            LegacySQLTypeName.FLOAT.equals(field.getType()) ||
            LegacySQLTypeName.BOOLEAN.equals(field.getType()) ||
            LegacySQLTypeName.DATE.equals(field.getType()) ||
            LegacySQLTypeName.TIME.equals(field.getType()) ||
            LegacySQLTypeName.TIMESTAMP.equals(field.getType())) {
        return value;
    }

    if (LegacySQLTypeName.STRING.equals(field.getType()) ||
            LegacySQLTypeName.DATETIME.equals(field.getType()) ||
            LegacySQLTypeName.GEOGRAPHY.equals(field.getType())) {
        return UTF8String.fromBytes(((Utf8) value).getBytes());
    }

    if (LegacySQLTypeName.BYTES.equals(field.getType())) {
        return getBytes((ByteBuffer) value);
    }

    if (LegacySQLTypeName.NUMERIC.equals(field.getType())) {
        byte[] bytes = getBytes((ByteBuffer) value);
        BigDecimal b = new BigDecimal(new BigInteger(bytes), BQ_NUMERIC_SCALE);
        Decimal d = Decimal.apply(b, BQ_NUMERIC_PRECISION, BQ_NUMERIC_SCALE);

        return d;
    }

    if (LegacySQLTypeName.RECORD.equals(field.getType())) {
        return convertAll(field.getSubFields(),
                (GenericRecord) value,
                field.getSubFields().stream().map(f -> f.getName()).collect(Collectors.toList()));
    }

    throw new IllegalStateException("Unexpected type: " + field.getType());
}
 

@Override
protected int run() throws Exception {

  SparkConf sparkConf = new SparkConf()
      .setAppName("Data2CoNLL")
      .set("spark.hadoop.validateOutputSpecs", "false")
      .set("spark.yarn.executor.memoryOverhead", "3072")
      .set("spark.rdd.compress", "true")
      .set("spark.core.connection.ack.wait.timeout", "600")
      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
      //.set("spark.kryo.registrationRequired", "true")
      .registerKryoClasses(new Class[] {SCAS.class, LabeledPoint.class, SparseVector.class, int[].class, double[].class,
          InternalRow[].class, GenericInternalRow.class, Object[].class, GenericArrayData.class,
          VectorIndexer.class})
      ;//.setMaster("local[4]"); //Remove this if you run it on the server.


  JavaSparkContext sc = new JavaSparkContext(sparkConf);
  int totalCores = Integer.parseInt(sc.getConf().get("spark.executor.instances"))
      * Integer.parseInt(sc.getConf().get("spark.executor.cores"));

  FileSystem fs = FileSystem.get(new Configuration());

  int partitionNumber = 3 * totalCores;
  if(partitions != null) {
    partitionNumber = partitions;
  }

  //Read training documents serialized as SCAS
  JavaRDD<SCAS> documents = sc.sequenceFile(input, Text.class, SCAS.class, partitionNumber).values();

  JavaRDD<String> docStrings = documents.map( s -> {
    JCas jCas = s.getJCas();
    NYTArticleMetaData metadata = JCasUtil.selectSingle(jCas, NYTArticleMetaData.class);

    StringJoiner docBuilder = new StringJoiner("\n");

    docBuilder.add("-DOCSTART- (" +  metadata.getGuid() + ")");
    docBuilder.add("");

    Collection<Sentence> sentences = JCasUtil.select(jCas, Sentence.class);
    for(Sentence sentence: sentences) {
      List<Token> tokens = JCasUtil.selectCovered(jCas, Token.class, sentence);
      for(Token token: tokens) {
        CoreLabel taggedWord = CoreNlpUtils.tokenToWord(token);
        StringJoiner lineBuilder = new StringJoiner("\t");
        lineBuilder.add(taggedWord.word().toLowerCase());
        docBuilder.add(lineBuilder.toString());
      }
      docBuilder.add("");
    }
    return docBuilder.toString();
  });

  docStrings.saveAsTextFile(output);
  sc.stop();
  return 0;
}
 

@Override
    protected int run() throws Exception {

        SparkConf sparkConf = new SparkConf()
                .setAppName("EntitySalienceTrainingSparkRunner")
                .set("spark.hadoop.validateOutputSpecs", "false")
                .set("spark.yarn.executor.memoryOverhead", "3072")
                .set("spark.rdd.compress", "true")
                .set("spark.core.connection.ack.wait.timeout", "600")
                .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
                //.set("spark.kryo.registrationRequired", "true")
                .registerKryoClasses(new Class[] {SCAS.class, LabeledPoint.class, SparseVector.class, int[].class, double[].class,
                        InternalRow[].class, GenericInternalRow.class, Object[].class, GenericArrayData.class,
                        VectorIndexer.class})
                ;//.setMaster("local[4]"); //Remove this if you run it on the server.

        TrainingSettings trainingSettings = new TrainingSettings();

        if(folds != null) {
            trainingSettings.setNumFolds(folds);
        }
        if(method != null) {
            trainingSettings.setClassificationMethod(TrainingSettings.ClassificationMethod.valueOf(method));
        }
        if(defaultConf != null) {
            trainingSettings.setAidaDefaultConf(defaultConf);
        }

        if(scalingFactor != null) {
            trainingSettings.setPositiveInstanceScalingFactor(scalingFactor);
        }

        JavaSparkContext sc = new JavaSparkContext(sparkConf);
        int totalCores = Integer.parseInt(sc.getConf().get("spark.executor.instances"))
                * Integer.parseInt(sc.getConf().get("spark.executor.cores"));

//        int totalCores = 4;
////        trainingSettings.setFeatureExtractor(TrainingSettings.FeatureExtractor.ANNOTATE_AND_ENTITY_SALIENCE);
////        trainingSettings.setAidaDefaultConf("db");
//        //trainingSettings.setClassificationMethod(TrainingSettings.ClassificationMethod.LOG_REG);
//        trainingSettings.setPositiveInstanceScalingFactor(1);

        //Add the cache files to each node only if annotation is required.
        //The input documents could already be annotated, and in this case no caches are needed.
        if(trainingSettings.getFeatureExtractor().equals(TrainingSettings.FeatureExtractor.ANNOTATE_AND_ENTITY_SALIENCE)) {
            sc.addFile(trainingSettings.getBigramCountCache());
            sc.addFile(trainingSettings.getKeywordCountCache());
            sc.addFile(trainingSettings.getWordContractionsCache());
            sc.addFile(trainingSettings.getWordExpansionsCache());
            if (trainingSettings.getAidaDefaultConf().equals("db")) {
                sc.addFile(trainingSettings.getDatabaseAida());
            } else {
                sc.addFile(trainingSettings.getCassandraConfig());
            }
        }

        SQLContext sqlContext = new SQLContext(sc);


        FileSystem fs = FileSystem.get(new Configuration());

        int partitionNumber = 3 * totalCores;
        if(partitions != null) {
            partitionNumber = partitions;
        }

        //Read training documents serialized as SCAS
        JavaRDD<SCAS> documents = sc.sequenceFile(input, Text.class, SCAS.class, partitionNumber).values();

        //Instanciate a training spark runner
        TrainingSparkRunner trainingSparkRunner = new TrainingSparkRunner();

        //Train a model
        CrossValidatorModel model = trainingSparkRunner.crossValidate(sc, sqlContext, documents, trainingSettings);


        //Create the model path
        String modelPath = output+"/"+sc.getConf().getAppId()+"/model_"+trainingSettings.getClassificationMethod();

        //Delete the old model if there is one
        fs.delete(new Path(modelPath), true);

        //Save the new model model
        List<Model> models = new ArrayList<>();
        models.add(model.bestModel());
        sc.parallelize(models, 1).saveAsObjectFile(modelPath);

        //Save the model stats
        SparkClassificationModel.saveStats(model, trainingSettings, output+"/"+sc.getConf().getAppId()+"/");


        return 0;
    }
 

@Override
    protected int run() throws Exception {

        SparkConf sparkConf = new SparkConf()
                .setAppName("EntitySalienceTrainingSparkRunner")
                .set("spark.hadoop.validateOutputSpecs", "false")
                //.set("spark.yarn.executor.memoryOverhead", "4096")
                .set("spark.rdd.compress", "true")
                .set("spark.core.connection.ack.wait.timeout", "600")
                .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
                //.set("spark.kryo.registrationRequired", "true")
                .registerKryoClasses(new Class[] {SCAS.class, LabeledPoint.class, SparseVector.class, int[].class, double[].class,
                        InternalRow[].class, GenericInternalRow.class, Object[].class, GenericArrayData.class,
                        VectorIndexer.class})
                ;//setMaster("local"); //Remove this if you run it on the server.

        TrainingSettings trainingSettings = new TrainingSettings();

        if(defaultConf != null) {
            trainingSettings.setAidaDefaultConf(defaultConf);
        }


        JavaSparkContext sc = new JavaSparkContext(sparkConf);

        int totalCores = Integer.parseInt(sc.getConf().get("spark.executor.instances"))
                * Integer.parseInt(sc.getConf().get("spark.executor.cores"));

//        int totalCores = 2;

        //trainingSettings.setClassificationMethod(TrainingSettings.ClassificationMethod.LOG_REG);

        trainingSettings.setPositiveInstanceScalingFactor(1);
        if(trainingSettings.getFeatureExtractor().equals(TrainingSettings.FeatureExtractor.ANNOTATE_AND_ENTITY_SALIENCE)) {
            sc.addFile(trainingSettings.getBigramCountCache());
            sc.addFile(trainingSettings.getKeywordCountCache());
            sc.addFile(trainingSettings.getWordContractionsCache());
            sc.addFile(trainingSettings.getWordExpansionsCache());
            if (trainingSettings.getAidaDefaultConf().equals("db")) {
                sc.addFile(trainingSettings.getDatabaseAida());
            } else {
                sc.addFile(trainingSettings.getCassandraConfig());
            }
        }

        SQLContext sqlContext = new SQLContext(sc);


        int partitionNumber = 3 * totalCores;
        //Read training documents serialized as SCAS
        JavaPairRDD<Text, SCAS> documents = sc.sequenceFile(input, Text.class, SCAS.class, partitionNumber);

        //Instanciate a training spark runner
        TrainingSparkRunner trainingSparkRunner = new TrainingSparkRunner();


        PipelineModel trainingModel = (PipelineModel) sc.objectFile(model).first();

        //Evaluate the model and write down the evaluation metrics.
        trainingSparkRunner.evaluate(sc, sqlContext, documents, trainingModel, trainingSettings, output+"/"+sc.getConf().getAppId()+"/");

        return 0;
    }
 

@Override
public ArrayData copy() {
  return new GenericArrayData(array());
}
 

@Override
public ArrayData copy() {
  return new GenericArrayData(array());
}