org.apache.spark.api.java.JavaPairRDD#join

Source File: TransformationRDD.java From hui-bigdata-spark with Apache License 2.0

6 votes

/**
 * 集合关联. 合并相同key的value
 * demo计算目的：今年和去年都获奖的同学，获奖项的科目都有哪些
 *
 * @since hui_project 1.0.0
 */
public void testJoin() {
    SparkConf sparkConf = new SparkConf().setMaster("local[4]").setAppName("test");
    JavaSparkContext sparkContext = new JavaSparkContext(sparkConf);
    //今年同学获奖的科目
    JavaPairRDD<Object, Object> rdd1 = sparkContext.parallelize(Arrays.asList(
            new Tuple2("xiaoming", "语文")
            , new Tuple2("xiaoming", "数学")
            , new Tuple2("lihua", "数学")
            , new Tuple2("xiaofeng", "艺术")
            , new Tuple2("test", "艺术")))
            .mapToPair(x -> new Tuple2<>(x._1, x._2));
    //去年同学获奖的科目
    JavaPairRDD<Object, Object> rdd2 = sparkContext.parallelize(Arrays.asList(
            new Tuple2("xiaoming", "艺术")
            , new Tuple2("lihua", "艺术")
            , new Tuple2("xiaofeng", "语文")))
            .mapToPair(x -> new Tuple2<>(x._1, x._2));
    JavaPairRDD<Object, Tuple2<Object, Object>> join = rdd1.join(rdd2);
    checkResult(join.collect());
}

Source File: TransformationRDDTest.java From hui-bigdata-spark with Apache License 2.0

6 votes

/**
 * 集合关联. 合并相同key的value
 * demo计算目的：今年和去年都获奖的同学，获奖项的科目都有哪些
 * @since hui_project 1.0.0
 */
@Test
public void testJoin() {
    //今年同学获奖的科目
    JavaPairRDD<Object, Object> rdd1 = sparkContext.parallelize(Arrays.asList(
            new Tuple2("xiaoming", "语文")
            , new Tuple2("xiaoming", "数学")
            , new Tuple2("lihua", "数学")
            , new Tuple2("xiaofeng", "艺术")
            , new Tuple2("test", "艺术")))
            .mapToPair(x -> new Tuple2<>(x._1, x._2));
    //去年同学获奖的科目
    JavaPairRDD<Object, Object> rdd2 = sparkContext.parallelize(Arrays.asList(
            new Tuple2("xiaoming", "艺术")
            , new Tuple2("lihua", "艺术")
            , new Tuple2("xiaofeng", "语文")))
            .mapToPair(x -> new Tuple2<>(x._1, x._2));
    JavaPairRDD<Object, Tuple2<Object, Object>> join = rdd1.join(rdd2);
    checkResult(join.collect());
}

Source File: SparkTableChecker.java From spliceengine with GNU Affero General Public License v3.0

6 votes

/**
 * Check for duplicate indexes
 * @param index
 * @return
 * @throws StandardException
 * @throws InterruptedException
 * @throws ExecutionException
 */
private List<String> checkDuplicateIndexes(PairDataSet table, PairDataSet index) throws StandardException {
    try {
        SpliceSpark.pushScope(String.format("Check duplicates in index %s.%s", schemaName, indexName));
        JavaPairRDD duplicateIndexRdd = ((SparkPairDataSet) index).rdd
                .combineByKey(new CreateCombiner(), new MergeValue(), new MergeCombiners())
                .filter(new DuplicateIndexFilter());

        JavaPairRDD joinedRdd = duplicateIndexRdd
                .join(((SparkPairDataSet) table).rdd);

        JavaRDD duplicateIndex = joinedRdd
                .mapPartitions(new SparkFlatMapFunction<>(new DeleteDuplicateIndexFunction<>(conglomerate, txn, tentativeIndex, baseColumnMap, fix)));

        Iterator it = duplicateIndex.toLocalIterator();
        long count = duplicateIndex.count();
        return reportDuplicateIndexes(it, count, fix);
    }catch (Exception e) {
        throw StandardException.plainWrapException(e);
    }
    finally {
        SpliceSpark.popScope();
    }
}

Source File: CumulativeOffsetSPInstruction.java From systemds with Apache License 2.0

5 votes

@Override
public void processInstruction(ExecutionContext ec) {
	SparkExecutionContext sec = (SparkExecutionContext)ec;
	DataCharacteristics mc1 = sec.getDataCharacteristics(input1.getName());
	DataCharacteristics mc2 = sec.getDataCharacteristics(input2.getName());
	long rlen = mc2.getRows();
	int blen = mc2.getBlocksize();
	
	//get and join inputs
	JavaPairRDD<MatrixIndexes,MatrixBlock> inData = sec.getBinaryMatrixBlockRDDHandleForVariable(input1.getName());
	JavaPairRDD<MatrixIndexes,Tuple2<MatrixBlock,MatrixBlock>> joined = null;
	boolean broadcast = _broadcast && !SparkUtils.isHashPartitioned(inData);
	
	if( broadcast ) {
		//broadcast offsets and broadcast join with data
		PartitionedBroadcast<MatrixBlock> inAgg = sec.getBroadcastForVariable(input2.getName());
		joined = inData.mapToPair(new RDDCumSplitLookupFunction(inAgg,_initValue, rlen, blen));
	}
	else {
		//prepare aggregates (cumsplit of offsets) and repartition join with data
		joined = inData.join(sec
			.getBinaryMatrixBlockRDDHandleForVariable(input2.getName())
			.flatMapToPair(new RDDCumSplitFunction(_initValue, rlen, blen)));
	}
	
	//execute cumulative offset (apply cumulative op w/ offsets)
	JavaPairRDD<MatrixIndexes,MatrixBlock> out = joined
		.mapValues(new RDDCumOffsetFunction(_uop, _cumsumprod));
	
	//put output handle in symbol table
	if( _cumsumprod )
		sec.getDataCharacteristics(output.getName())
			.set(mc1.getRows(), 1, mc1.getBlocksize(), mc1.getBlocksize());
	else //general case
		updateUnaryOutputDataCharacteristics(sec);
	sec.setRDDHandleForVariable(output.getName(), out);
	sec.addLineageRDD(output.getName(), input1.getName());
	sec.addLineage(output.getName(), input2.getName(), broadcast);
}

Source File: CumulativeOffsetSPInstruction.java From systemds with Apache License 2.0

5 votes

@Override
public void processInstruction(ExecutionContext ec) {
	SparkExecutionContext sec = (SparkExecutionContext)ec;
	DataCharacteristics mc1 = sec.getDataCharacteristics(input1.getName());
	DataCharacteristics mc2 = sec.getDataCharacteristics(input2.getName());
	long rlen = mc2.getRows();
	int blen = mc2.getBlocksize();
	
	//get and join inputs
	JavaPairRDD<MatrixIndexes,MatrixBlock> inData = sec.getBinaryMatrixBlockRDDHandleForVariable(input1.getName());
	JavaPairRDD<MatrixIndexes,Tuple2<MatrixBlock,MatrixBlock>> joined = null;
	boolean broadcast = _broadcast && !SparkUtils.isHashPartitioned(inData);
	
	if( broadcast ) {
		//broadcast offsets and broadcast join with data
		PartitionedBroadcast<MatrixBlock> inAgg = sec.getBroadcastForVariable(input2.getName());
		joined = inData.mapToPair(new RDDCumSplitLookupFunction(inAgg,_initValue, rlen, blen));
	}
	else {
		//prepare aggregates (cumsplit of offsets) and repartition join with data
		joined = inData.join(sec
			.getBinaryMatrixBlockRDDHandleForVariable(input2.getName())
			.flatMapToPair(new RDDCumSplitFunction(_initValue, rlen, blen)));
	}
	
	//execute cumulative offset (apply cumulative op w/ offsets)
	JavaPairRDD<MatrixIndexes,MatrixBlock> out = joined
		.mapValues(new RDDCumOffsetFunction(_uop, _cumsumprod));
	
	//put output handle in symbol table
	if( _cumsumprod )
		sec.getDataCharacteristics(output.getName())
			.set(mc1.getRows(), 1, mc1.getBlocksize(), mc1.getBlocksize());
	else //general case
		updateUnaryOutputDataCharacteristics(sec);
	sec.setRDDHandleForVariable(output.getName(), out);
	sec.addLineageRDD(output.getName(), input1.getName());
	sec.addLineage(output.getName(), input2.getName(), broadcast);
}

Source File: AreaTop3ProductSpark.java From BigDataPlatform with GNU General Public License v3.0

4 votes

/**
 * 生成点击商品基础信息临时表
 * @param sqlContext
 * @param cityid2clickActionRDD
 * @param cityid2cityInfoRDD
 */
private static void generateTempClickProductBasicTable(
		SQLContext sqlContext,
		JavaPairRDD<Long, Row> cityid2clickActionRDD,
		JavaPairRDD<Long, Row> cityid2cityInfoRDD) {
	// 执行join操作，进行点击行为数据和城市数据的关联
	JavaPairRDD<Long, Tuple2<Row, Row>> joinedRDD =
			cityid2clickActionRDD.join(cityid2cityInfoRDD);
	
	// 将上面的JavaPairRDD，转换成一个JavaRDD<Row>（才能将RDD转换为Dataset<Row>）
	JavaRDD<Row> mappedRDD = joinedRDD.map(
			
			new Function<Tuple2<Long,Tuple2<Row,Row>>, Row>() {

				private static final long serialVersionUID = 1L;

				@Override
				public Row call(Tuple2<Long, Tuple2<Row, Row>> tuple)
						throws Exception {
					Long cityid = tuple._1;
					Row clickAction = tuple._2._1;
					Row cityInfo = tuple._2._2;
					
					Long productid = clickAction.getLong(1);
					String cityName = cityInfo.getString(1);
					String area = cityInfo.getString(2);
					
					return RowFactory.create(cityid, cityName, area, productid);  
				}
				
			});
	
	// 基于JavaRDD<Row>的格式，就可以将其转换为Dataset<Row>
	List<StructField> structFields = new ArrayList<StructField>();
	structFields.add(DataTypes.createStructField("city_id", DataTypes.LongType, true));
	structFields.add(DataTypes.createStructField("city_name", DataTypes.StringType, true));
	structFields.add(DataTypes.createStructField("area", DataTypes.StringType, true));
	structFields.add(DataTypes.createStructField("product_id", DataTypes.LongType, true));
	
	// 1 北京
	// 2 上海
	// 1 北京
	// group by area,product_id
	// 1:北京,2:上海
	
	// 两个函数
	// UDF：concat2()，将两个字段拼接起来，用指定的分隔符
	// UDAF：group_concat_distinct()，将一个分组中的多个字段值，用逗号拼接起来，同时进行去重
	
	StructType schema = DataTypes.createStructType(structFields);

	Dataset<Row> df = sqlContext.createDataFrame(mappedRDD, schema);
	System.out.println("tmp_click_product_basic: " + df.count());  
	
	// 将Dataset<Row>中的数据，注册成临时表（tmp_click_product_basic）
	df.registerTempTable("tmp_click_product_basic");  
}

Source File: ExtractorEntityTest.java From deep-spark with Apache License 2.0

4 votes

/**
 * It tests if the extractor can join two data sets
 */
@Test
protected void testInnerJoin() {
    DeepSparkContext context = getDeepSparkContext();

    try {

        JavaPairRDD<Long, TeamEntity> teamsRDD = prepareTeamRDD(context);

        JavaPairRDD<Long, Iterable<PlayerEntity>> playersRDD = preparePlayerRDD(context).groupByKey();

        JavaPairRDD<Long, Tuple2<TeamEntity, Iterable<PlayerEntity>>> joinRDD = teamsRDD.join(playersRDD);

        assertEquals(joinRDD.count(), 4);

    } finally {
        context.stop();
    }
}

Source File: SkewedJoinConverter.java From spork with Apache License 2.0

4 votes

@Override
public RDD<Tuple> convert(List<RDD<Tuple>> predecessors,
                          POSkewedJoin poSkewedJoin) throws IOException {

    SparkUtil.assertPredecessorSize(predecessors, poSkewedJoin, 2);
    LRs = new POLocalRearrange[2];
    this.poSkewedJoin = poSkewedJoin;

    createJoinPlans(poSkewedJoin.getJoinPlans());

    // extract the two RDDs
    RDD<Tuple> rdd1 = predecessors.get(0);
    RDD<Tuple> rdd2 = predecessors.get(1);

    // make (key, value) pairs, key has type Object, value has type Tuple
    RDD<Tuple2<Object, Tuple>> rdd1Pair = rdd1.map(new ExtractKeyFunction(
            this, 0), SparkUtil.<Object, Tuple>getTuple2Manifest());
    RDD<Tuple2<Object, Tuple>> rdd2Pair = rdd2.map(new ExtractKeyFunction(
            this, 1), SparkUtil.<Object, Tuple>getTuple2Manifest());

    // join fn is present in JavaPairRDD class ..
    JavaPairRDD<Object, Tuple> rdd1Pair_javaRDD = new JavaPairRDD<Object, Tuple>(
            rdd1Pair, SparkUtil.getManifest(Object.class),
            SparkUtil.getManifest(Tuple.class));
    JavaPairRDD<Object, Tuple> rdd2Pair_javaRDD = new JavaPairRDD<Object, Tuple>(
            rdd2Pair, SparkUtil.getManifest(Object.class),
            SparkUtil.getManifest(Tuple.class));

    // do the join
    JavaPairRDD<Object, Tuple2<Tuple, Tuple>> result_KeyValue = rdd1Pair_javaRDD
            .join(rdd2Pair_javaRDD);

    // map to get RDD<Tuple> from RDD<Object, Tuple2<Tuple, Tuple>> by
    // ignoring the key (of type Object) and appending the values (the
    // Tuples)
    JavaRDD<Tuple> result = result_KeyValue
            .mapPartitions(new ToValueFunction());

    // return type is RDD<Tuple>, so take it from JavaRDD<Tuple>
    return result.rdd();
}

Source File: ParamservUtils.java From systemds with Apache License 2.0

3 votes

/**
 * Assemble the matrix of features and labels according to the rowID
 *
 * @param featuresRDD indexed features matrix block
 * @param labelsRDD indexed labels matrix block
 * @return Assembled rdd with rowID as key while matrix of features and labels as value (rowID {@literal ->} features, labels)
 */
public static JavaPairRDD<Long, Tuple2<MatrixBlock, MatrixBlock>> assembleTrainingData(JavaPairRDD<MatrixIndexes, MatrixBlock> featuresRDD, JavaPairRDD<MatrixIndexes, MatrixBlock> labelsRDD) {
	JavaPairRDD<Long, MatrixBlock> fRDD = groupMatrix(featuresRDD);
	JavaPairRDD<Long, MatrixBlock> lRDD = groupMatrix(labelsRDD);
	//TODO Add an additional physical operator which broadcasts the labels directly (broadcast join with features) if certain memory budgets are satisfied
	return fRDD.join(lRDD);
}

Source File: ParamservUtils.java From systemds with Apache License 2.0

3 votes

/**
 * Assemble the matrix of features and labels according to the rowID
 *
 * @param featuresRDD indexed features matrix block
 * @param labelsRDD indexed labels matrix block
 * @return Assembled rdd with rowID as key while matrix of features and labels as value (rowID {@literal ->} features, labels)
 */
public static JavaPairRDD<Long, Tuple2<MatrixBlock, MatrixBlock>> assembleTrainingData(JavaPairRDD<MatrixIndexes, MatrixBlock> featuresRDD, JavaPairRDD<MatrixIndexes, MatrixBlock> labelsRDD) {
	JavaPairRDD<Long, MatrixBlock> fRDD = groupMatrix(featuresRDD);
	JavaPairRDD<Long, MatrixBlock> lRDD = groupMatrix(labelsRDD);
	//TODO Add an additional physical operator which broadcasts the labels directly (broadcast join with features) if certain memory budgets are satisfied
	return fRDD.join(lRDD);
}

Java Code Examples for org.apache.spark.api.java.JavaPairRDD#join()