Examples with Partitioner org.apache.spark.Partitioner used on opensource projects

Example 1 with Partitioner

use of org.apache.spark.Partitioner in project beam by apache.

the class SparkGroupAlsoByWindowViaWindowSet method groupAlsoByWindow.

public static <K, InputT, W extends BoundedWindow> JavaDStream<WindowedValue<KV<K, Iterable<InputT>>>> groupAlsoByWindow(JavaDStream<WindowedValue<KV<K, Iterable<WindowedValue<InputT>>>>> inputDStream, final Coder<K> keyCoder, final Coder<WindowedValue<InputT>> wvCoder, final WindowingStrategy<?, W> windowingStrategy, final SparkRuntimeContext runtimeContext, final List<Integer> sourceIds) {
    final IterableCoder<WindowedValue<InputT>> itrWvCoder = IterableCoder.of(wvCoder);
    final Coder<InputT> iCoder = ((FullWindowedValueCoder<InputT>) wvCoder).getValueCoder();
    final Coder<? extends BoundedWindow> wCoder = ((FullWindowedValueCoder<InputT>) wvCoder).getWindowCoder();
    final Coder<WindowedValue<KV<K, Iterable<InputT>>>> wvKvIterCoder = FullWindowedValueCoder.of(KvCoder.of(keyCoder, IterableCoder.of(iCoder)), wCoder);
    final TimerInternals.TimerDataCoder timerDataCoder = TimerInternals.TimerDataCoder.of(windowingStrategy.getWindowFn().windowCoder());
    long checkpointDurationMillis = runtimeContext.getPipelineOptions().as(SparkPipelineOptions.class).getCheckpointDurationMillis();
    // we have to switch to Scala API to avoid Optional in the Java API, see: SPARK-4819.
    // we also have a broader API for Scala (access to the actual key and entire iterator).
    // we use coders to convert objects in the PCollection to byte arrays, so they
    // can be transferred over the network for the shuffle and be in serialized form
    // for checkpointing.
    // for readability, we add comments with actual type next to byte[].
    // to shorten line length, we use:
    //---- WV: WindowedValue
    //---- Iterable: Itr
    //---- AccumT: A
    //---- InputT: I
    DStream<Tuple2<ByteArray, byte[]>> /*Itr<WV<I>>*/
    pairDStream = inputDStream.transformToPair(new Function<JavaRDD<WindowedValue<KV<K, Iterable<WindowedValue<InputT>>>>>, JavaPairRDD<ByteArray, byte[]>>() {

        // we use mapPartitions with the RDD API because its the only available API
        // that allows to preserve partitioning.
        @Override
        public JavaPairRDD<ByteArray, byte[]> call(JavaRDD<WindowedValue<KV<K, Iterable<WindowedValue<InputT>>>>> rdd) throws Exception {
            return rdd.mapPartitions(TranslationUtils.functionToFlatMapFunction(WindowingHelpers.<KV<K, Iterable<WindowedValue<InputT>>>>unwindowFunction()), true).mapPartitionsToPair(TranslationUtils.<K, Iterable<WindowedValue<InputT>>>toPairFlatMapFunction(), true).mapPartitionsToPair(TranslationUtils.pairFunctionToPairFlatMapFunction(CoderHelpers.toByteFunction(keyCoder, itrWvCoder)), true);
        }
    }).dstream();
    PairDStreamFunctions<ByteArray, byte[]> pairDStreamFunctions = DStream.toPairDStreamFunctions(pairDStream, JavaSparkContext$.MODULE$.<ByteArray>fakeClassTag(), JavaSparkContext$.MODULE$.<byte[]>fakeClassTag(), null);
    int defaultNumPartitions = pairDStreamFunctions.defaultPartitioner$default$1();
    Partitioner partitioner = pairDStreamFunctions.defaultPartitioner(defaultNumPartitions);
    // use updateStateByKey to scan through the state and update elements and timers.
    DStream<Tuple2<ByteArray, Tuple2<StateAndTimers, List<byte[]>>>> /*WV<KV<K, Itr<I>>>*/
    firedStream = pairDStreamFunctions.updateStateByKey(new SerializableFunction1<scala.collection.Iterator<Tuple3</*K*/
    ByteArray, Seq<byte[]>, Option<Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
    List<byte[]>>>>>, scala.collection.Iterator<Tuple2</*K*/
    ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
    List<byte[]>>>>>() {

        @Override
        public scala.collection.Iterator<Tuple2</*K*/
        ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
        List<byte[]>>>> apply(final scala.collection.Iterator<Tuple3</*K*/
        ByteArray, Seq<byte[]>, Option<Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
        List<byte[]>>>>> iter) {
            //--- ACTUAL STATEFUL OPERATION:
            //
            // Input Iterator: the partition (~bundle) of a cogrouping of the input
            // and the previous state (if exists).
            //
            // Output Iterator: the output key, and the updated state.
            //
            // possible input scenarios for (K, Seq, Option<S>):
            // (1) Option<S>.isEmpty: new data with no previous state.
            // (2) Seq.isEmpty: no new data, but evaluating previous state (timer-like behaviour).
            // (3) Seq.nonEmpty && Option<S>.isDefined: new data with previous state.
            final SystemReduceFn<K, InputT, Iterable<InputT>, Iterable<InputT>, W> reduceFn = SystemReduceFn.buffering(((FullWindowedValueCoder<InputT>) wvCoder).getValueCoder());
            final OutputWindowedValueHolder<K, InputT> outputHolder = new OutputWindowedValueHolder<>();
            // use in memory Aggregators since Spark Accumulators are not resilient
            // in stateful operators, once done with this partition.
            final MetricsContainerImpl cellProvider = new MetricsContainerImpl("cellProvider");
            final CounterCell droppedDueToClosedWindow = cellProvider.getCounter(MetricName.named(SparkGroupAlsoByWindowViaWindowSet.class, GroupAlsoByWindowsAggregators.DROPPED_DUE_TO_CLOSED_WINDOW_COUNTER));
            final CounterCell droppedDueToLateness = cellProvider.getCounter(MetricName.named(SparkGroupAlsoByWindowViaWindowSet.class, GroupAlsoByWindowsAggregators.DROPPED_DUE_TO_LATENESS_COUNTER));
            AbstractIterator<Tuple2<ByteArray, Tuple2<StateAndTimers, List<byte[]>>>> /*WV<KV<K, Itr<I>>>*/
            outIter = new AbstractIterator<Tuple2</*K*/
            ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
            List<byte[]>>>>() {

                @Override
                protected Tuple2</*K*/
                ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
                List<byte[]>>> computeNext() {
                    // (possibly) previous-state and (possibly) new data.
                    while (iter.hasNext()) {
                        // for each element in the partition:
                        Tuple3<ByteArray, Seq<byte[]>, Option<Tuple2<StateAndTimers, List<byte[]>>>> next = iter.next();
                        ByteArray encodedKey = next._1();
                        K key = CoderHelpers.fromByteArray(encodedKey.getValue(), keyCoder);
                        Seq<byte[]> seq = next._2();
                        Option<Tuple2<StateAndTimers, List<byte[]>>> prevStateAndTimersOpt = next._3();
                        SparkStateInternals<K> stateInternals;
                        SparkTimerInternals timerInternals = SparkTimerInternals.forStreamFromSources(sourceIds, GlobalWatermarkHolder.get());
                        // get state(internals) per key.
                        if (prevStateAndTimersOpt.isEmpty()) {
                            // no previous state.
                            stateInternals = SparkStateInternals.forKey(key);
                        } else {
                            // with pre-existing state.
                            StateAndTimers prevStateAndTimers = prevStateAndTimersOpt.get()._1();
                            stateInternals = SparkStateInternals.forKeyAndState(key, prevStateAndTimers.getState());
                            Collection<byte[]> serTimers = prevStateAndTimers.getTimers();
                            timerInternals.addTimers(SparkTimerInternals.deserializeTimers(serTimers, timerDataCoder));
                        }
                        ReduceFnRunner<K, InputT, Iterable<InputT>, W> reduceFnRunner = new ReduceFnRunner<>(key, windowingStrategy, ExecutableTriggerStateMachine.create(TriggerStateMachines.stateMachineForTrigger(TriggerTranslation.toProto(windowingStrategy.getTrigger()))), stateInternals, timerInternals, outputHolder, new UnsupportedSideInputReader("GroupAlsoByWindow"), reduceFn, runtimeContext.getPipelineOptions());
                        // clear before potential use.
                        outputHolder.clear();
                        if (!seq.isEmpty()) {
                            // new input for key.
                            try {
                                Iterable<WindowedValue<InputT>> elementsIterable = CoderHelpers.fromByteArray(seq.head(), itrWvCoder);
                                Iterable<WindowedValue<InputT>> validElements = LateDataUtils.dropExpiredWindows(key, elementsIterable, timerInternals, windowingStrategy, droppedDueToLateness);
                                reduceFnRunner.processElements(validElements);
                            } catch (Exception e) {
                                throw new RuntimeException("Failed to process element with ReduceFnRunner", e);
                            }
                        } else if (stateInternals.getState().isEmpty()) {
                            // no input and no state -> GC evict now.
                            continue;
                        }
                        try {
                            // advance the watermark to HWM to fire by timers.
                            timerInternals.advanceWatermark();
                            // call on timers that are ready.
                            reduceFnRunner.onTimers(timerInternals.getTimersReadyToProcess());
                        } catch (Exception e) {
                            throw new RuntimeException("Failed to process ReduceFnRunner onTimer.", e);
                        }
                        // this is mostly symbolic since actual persist is done by emitting output.
                        reduceFnRunner.persist();
                        // obtain output, if fired.
                        List<WindowedValue<KV<K, Iterable<InputT>>>> outputs = outputHolder.get();
                        if (!outputs.isEmpty() || !stateInternals.getState().isEmpty()) {
                            StateAndTimers updated = new StateAndTimers(stateInternals.getState(), SparkTimerInternals.serializeTimers(timerInternals.getTimers(), timerDataCoder));
                            // persist Spark's state by outputting.
                            List<byte[]> serOutput = CoderHelpers.toByteArrays(outputs, wvKvIterCoder);
                            return new Tuple2<>(encodedKey, new Tuple2<>(updated, serOutput));
                        }
                    // an empty state with no output, can be evicted completely - do nothing.
                    }
                    return endOfData();
                }
            };
            // log if there's something to log.
            long lateDropped = droppedDueToLateness.getCumulative();
            if (lateDropped > 0) {
                LOG.info(String.format("Dropped %d elements due to lateness.", lateDropped));
                droppedDueToLateness.inc(-droppedDueToLateness.getCumulative());
            }
            long closedWindowDropped = droppedDueToClosedWindow.getCumulative();
            if (closedWindowDropped > 0) {
                LOG.info(String.format("Dropped %d elements due to closed window.", closedWindowDropped));
                droppedDueToClosedWindow.inc(-droppedDueToClosedWindow.getCumulative());
            }
            return scala.collection.JavaConversions.asScalaIterator(outIter);
        }
    }, partitioner, true, JavaSparkContext$.MODULE$.<Tuple2<StateAndTimers, List<byte[]>>>fakeClassTag());
    if (checkpointDurationMillis > 0) {
        firedStream.checkpoint(new Duration(checkpointDurationMillis));
    }
    // go back to Java now.
    JavaPairDStream<ByteArray, Tuple2<StateAndTimers, List<byte[]>>> /*WV<KV<K, Itr<I>>>*/
    javaFiredStream = JavaPairDStream.fromPairDStream(firedStream, JavaSparkContext$.MODULE$.<ByteArray>fakeClassTag(), JavaSparkContext$.MODULE$.<Tuple2<StateAndTimers, List<byte[]>>>fakeClassTag());
    // filter state-only output (nothing to fire) and remove the state from the output.
    return javaFiredStream.filter(new Function<Tuple2</*K*/
    ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
    List<byte[]>>>, Boolean>() {

        @Override
        public Boolean call(Tuple2</*K*/
        ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
        List<byte[]>>> t2) throws Exception {
            // filter output if defined.
            return !t2._2()._2().isEmpty();
        }
    }).flatMap(new FlatMapFunction<Tuple2</*K*/
    ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
    List<byte[]>>>, WindowedValue<KV<K, Iterable<InputT>>>>() {

        @Override
        public Iterable<WindowedValue<KV<K, Iterable<InputT>>>> call(Tuple2</*K*/
        ByteArray, Tuple2<StateAndTimers, /*WV<KV<K, Itr<I>>>*/
        List<byte[]>>> t2) throws Exception {
            // return in serialized form.
            return CoderHelpers.fromByteArrays(t2._2()._2(), wvKvIterCoder);
        }
    });
}

Example 2 with Partitioner

use of org.apache.spark.Partitioner in project incubator-systemml by apache.

the class MatrixIndexingSPInstruction method createPartitionPruningRDD.

/**
	 * Wraps the input RDD into a PartitionPruningRDD, which acts as a filter
	 * of required partitions. The distinct set of required partitions is determined
	 * via the partitioner of the input RDD.
	 * 
	 * @param in input matrix as {@code JavaPairRDD<MatrixIndexes,MatrixBlock>}
	 * @param filter partition filter
	 * @return matrix as {@code JavaPairRDD<MatrixIndexes,MatrixBlock>}
	 */
private static JavaPairRDD<MatrixIndexes, MatrixBlock> createPartitionPruningRDD(JavaPairRDD<MatrixIndexes, MatrixBlock> in, List<MatrixIndexes> filter) {
    //build hashset of required partition ids
    HashSet<Integer> flags = new HashSet<Integer>();
    Partitioner partitioner = in.rdd().partitioner().get();
    for (MatrixIndexes key : filter) flags.add(partitioner.getPartition(key));
    //create partition pruning rdd
    Function1<Object, Object> f = new PartitionPruningFunction(flags);
    PartitionPruningRDD<Tuple2<MatrixIndexes, MatrixBlock>> ppRDD = PartitionPruningRDD.create(in.rdd(), f);
    //wrap output into java pair rdd
    return new JavaPairRDD<MatrixIndexes, MatrixBlock>(ppRDD, ClassManifestFactory.fromClass(MatrixIndexes.class), ClassManifestFactory.fromClass(MatrixBlock.class));
}

Example 3 with Partitioner

use of org.apache.spark.Partitioner in project hive by apache.

the class SortByShuffler method shuffle.

@Override
public JavaPairRDD<HiveKey, BytesWritable> shuffle(JavaPairRDD<HiveKey, BytesWritable> input, int numPartitions) {
    JavaPairRDD<HiveKey, BytesWritable> rdd;
    if (totalOrder) {
        if (numPartitions > 0) {
            if (numPartitions > 1 && input.getStorageLevel() == StorageLevel.NONE()) {
                input.persist(StorageLevel.DISK_ONLY());
                sparkPlan.addCachedRDDId(input.id());
            }
            rdd = input.sortByKey(true, numPartitions);
        } else {
            rdd = input.sortByKey(true);
        }
    } else {
        Partitioner partitioner = new HashPartitioner(numPartitions);
        rdd = input.repartitionAndSortWithinPartitions(partitioner);
    }
    return rdd;
}

Example 4 with Partitioner

use of org.apache.spark.Partitioner in project beam by apache.

the class GroupCombineFunctions method groupByKeyOnly.

/**
   * An implementation of
   * {@link org.apache.beam.runners.core.GroupByKeyViaGroupByKeyOnly.GroupByKeyOnly}
   * for the Spark runner.
   */
public static <K, V> JavaRDD<WindowedValue<KV<K, Iterable<WindowedValue<V>>>>> groupByKeyOnly(JavaRDD<WindowedValue<KV<K, V>>> rdd, Coder<K> keyCoder, WindowedValueCoder<V> wvCoder) {
    // we use coders to convert objects in the PCollection to byte arrays, so they
    // can be transferred over the network for the shuffle.
    JavaPairRDD<ByteArray, byte[]> pairRDD = rdd.map(new ReifyTimestampsAndWindowsFunction<K, V>()).map(WindowingHelpers.<KV<K, WindowedValue<V>>>unwindowFunction()).mapToPair(TranslationUtils.<K, WindowedValue<V>>toPairFunction()).mapToPair(CoderHelpers.toByteFunction(keyCoder, wvCoder));
    // use a default parallelism HashPartitioner.
    Partitioner partitioner = new HashPartitioner(rdd.rdd().sparkContext().defaultParallelism());
    // and avoid unnecessary shuffle downstream.
    return pairRDD.groupByKey(partitioner).mapPartitionsToPair(TranslationUtils.pairFunctionToPairFlatMapFunction(CoderHelpers.fromByteFunctionIterable(keyCoder, wvCoder)), true).mapPartitions(TranslationUtils.<K, Iterable<WindowedValue<V>>>fromPairFlatMapFunction(), true).mapPartitions(TranslationUtils.functionToFlatMapFunction(WindowingHelpers.<KV<K, Iterable<WindowedValue<V>>>>windowFunction()), true);
}