org.apache.spark.sql.catalyst.expressions.GenericInternalRow Java Examples

PartitionRowConverter(Schema partitionSchema, PartitionSpec spec) {
  StructType partitionType = SparkSchemaUtil.convert(partitionSchema);
  StructField[] fields = partitionType.fields();

  this.types = new DataType[fields.length];
  this.positions = new int[types.length];
  this.javaTypes = new Class<?>[types.length];
  this.reusedRow = new GenericInternalRow(types.length);

  List<PartitionField> partitionFields = spec.fields();
  for (int rowIndex = 0; rowIndex < fields.length; rowIndex += 1) {
    this.types[rowIndex] = fields[rowIndex].dataType();

    int sourceId = partitionSchema.columns().get(rowIndex).fieldId();
    for (int specIndex = 0; specIndex < partitionFields.size(); specIndex += 1) {
      PartitionField field = spec.fields().get(specIndex);
      if (field.sourceId() == sourceId && "identity".equals(field.transform().toString())) {
        positions[rowIndex] = specIndex;
        javaTypes[rowIndex] = spec.javaClasses()[specIndex];
        break;
      }
    }
  }
}
 

static GenericInternalRow convertAll(FieldList fieldList,
                                     GenericRecord record,
                                     List<String> namesInOrder) {

    Map<String, Object> fieldMap = new HashMap<>();

    fieldList.stream().forEach(field ->
            fieldMap.put(field.getName(), convert(field, record.get(field.getName()))));

    Object[] values = new Object[namesInOrder.size()];
    for (int i = 0; i < namesInOrder.size(); i++) {
        values[i] = fieldMap.get(namesInOrder.get(i));
    }

    return new GenericInternalRow(values);
}
 

@Override
public InternalRow serialize(final T obj) {
  final byte[] bytes = new TWKBWriter().write(obj);
  final InternalRow returnRow = new GenericInternalRow(bytes.length);
  returnRow.update(0, bytes);
  return returnRow;
}
 

@Override
protected GenericInternalRow newStructData(InternalRow reuse) {
  if (reuse instanceof GenericInternalRow) {
    return (GenericInternalRow) reuse;
  } else {
    return new GenericInternalRow(numFields);
  }
}
 

@Override
protected InternalRow reuseOrCreate(Object reuse) {
  if (reuse instanceof GenericInternalRow && ((GenericInternalRow) reuse).numFields() == numFields) {
    return (InternalRow) reuse;
  }
  return new GenericInternalRow(numFields);
}
 

@Override
protected GenericInternalRow newStructData(InternalRow reuse) {
  if (reuse instanceof GenericInternalRow) {
    return (GenericInternalRow) reuse;
  } else {
    return new GenericInternalRow(numFields);
  }
}
 

public static Class[] getSerializableClasses() {
    return new Class[]{
            Instance.class, Predicate.class, RelationPredicate.class, RelationRow.class,
            TypeID.class, HashMap.class, HashSet.class, LiteralType.class, Object[].class,
            InternalRow[].class, GenericInternalRow.class, IndexMap.class, Quad.class
    };
}
 

@Override
protected void setDouble(GenericInternalRow row, int pos, double value) {
  row.setDouble(pos, value);
}
 

@Override
protected void setFloat(GenericInternalRow row, int pos, float value) {
  row.setFloat(pos, value);
}
 

@Override
protected void setLong(GenericInternalRow row, int pos, long value) {
  row.setLong(pos, value);
}
 

@Override
protected void setInteger(GenericInternalRow row, int pos, int value) {
  row.setInt(pos, value);
}
 

@Override
protected void setBoolean(GenericInternalRow row, int pos, boolean value) {
  row.setBoolean(pos, value);
}
 

@Override
protected void setNull(GenericInternalRow row, int pos) {
  row.setNullAt(pos);
}
 

@Override
protected void set(GenericInternalRow row, int pos, Object value) {
  row.update(pos, value);
}
 

@Override
protected InternalRow buildStruct(GenericInternalRow struct) {
  return struct;
}
 

@Override
protected Object getField(GenericInternalRow intermediate, int pos) {
  return intermediate.genericGet(pos);
}
 

@Override
protected InternalRow create() {
  return new GenericInternalRow(numFields);
}
 

@Override
protected void setDouble(GenericInternalRow row, int pos, double value) {
  row.setDouble(pos, value);
}
 

@Override
protected void setFloat(GenericInternalRow row, int pos, float value) {
  row.setFloat(pos, value);
}
 

@Override
protected void setLong(GenericInternalRow row, int pos, long value) {
  row.setLong(pos, value);
}
 

@Override
protected void setInteger(GenericInternalRow row, int pos, int value) {
  row.setInt(pos, value);
}
 

@Override
protected void setBoolean(GenericInternalRow row, int pos, boolean value) {
  row.setBoolean(pos, value);
}
 

@Override
protected void setNull(GenericInternalRow row, int pos) {
  row.setNullAt(pos);
}
 

@Override
protected void set(GenericInternalRow row, int pos, Object value) {
  row.update(pos, value);
}
 

@Override
protected InternalRow buildStruct(GenericInternalRow struct) {
  return struct;
}
 

@Override
protected Object getField(GenericInternalRow intermediate, int pos) {
  return intermediate.genericGet(pos);
}
 

@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
    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("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;
}