Examples with BoundedDataset org.apache.beam.runners.spark.translation.BoundedDataset used on opensource projects

Example 1 with BoundedDataset

use of org.apache.beam.runners.spark.translation.BoundedDataset in project beam by apache.

the class StreamingTransformTranslator method flattenPColl.

private static <T> TransformEvaluator<Flatten.PCollections<T>> flattenPColl() {
    return new TransformEvaluator<Flatten.PCollections<T>>() {

        @SuppressWarnings("unchecked")
        @Override
        public void evaluate(Flatten.PCollections<T> transform, EvaluationContext context) {
            Map<TupleTag<?>, PCollection<?>> pcs = context.getInputs(transform);
            // since this is a streaming pipeline, at least one of the PCollections to "flatten" are
            // unbounded, meaning it represents a DStream.
            // So we could end up with an unbounded unified DStream.
            final List<JavaDStream<WindowedValue<T>>> dStreams = new ArrayList<>();
            final List<Integer> streamingSources = new ArrayList<>();
            for (PValue pv : pcs.values()) {
                checkArgument(pv instanceof PCollection, "Flatten had non-PCollection value in input: %s of type %s", pv, pv.getClass().getSimpleName());
                PCollection<T> pcol = (PCollection<T>) pv;
                Dataset dataset = context.borrowDataset(pcol);
                if (dataset instanceof UnboundedDataset) {
                    UnboundedDataset<T> unboundedDataset = (UnboundedDataset<T>) dataset;
                    streamingSources.addAll(unboundedDataset.getStreamSources());
                    dStreams.add(unboundedDataset.getDStream());
                } else {
                    // create a single RDD stream.
                    Queue<JavaRDD<WindowedValue<T>>> q = new LinkedBlockingQueue<>();
                    q.offer(((BoundedDataset) dataset).getRDD());
                    // TODO (BEAM-10789): this is not recoverable from checkpoint!
                    JavaDStream<WindowedValue<T>> dStream = context.getStreamingContext().queueStream(q);
                    dStreams.add(dStream);
                }
            }
            // start by unifying streams into a single stream.
            JavaDStream<WindowedValue<T>> unifiedStreams = SparkCompat.joinStreams(context.getStreamingContext(), dStreams);
            context.putDataset(transform, new UnboundedDataset<>(unifiedStreams, streamingSources));
        }

        @Override
        public String toNativeString() {
            return "streamingContext.union(...)";
        }
    };
}