RxJava calculation in parallel

Question

I am trying to demonstrate the performance of RxJava compared to sequential (what I assumed to be) blocking calculations.

I was looking at this post and this SO question. From experience, benchmarking using System.currentTimeMillis() and Thread.sleep() does not yield consistent results when dealing with calculations instead of I/O so I tried setting up a simple JMH benchmark instead.

My benchmark calculates two ints and adds them up:

public class MyBenchmark {

    private Worker workerSequential;
    private Worker workerParallel;

    private int semiIntenseCalculation(int i) {
        Double d = Math.tan(Math.atan(Math.tan(Math.atan(Math.tan(Math.tan(Math.atan(Math.tan(Math.atan(Math.tan(Math.atan(Math.tan(Math.tan(Math.atan(Math.tan(Math.atan(Math.tan(i)))))))))))))))));
        return d.intValue() + i;
    }

    private int nonIntenseCalculation(int i) {
        Double d = Math.tan(Math.atan(Math.tan(Math.atan(Math.tan(Math.tan(Math.atan(i)))))));
        return d.intValue() + i;
    }


    private Observable<Object> intensiveObservable() {
        return Observable.fromCallable(new Callable<Object>() {
            @Override
            public Object call() throws Exception {

                int randomNumforSemi = ThreadLocalRandom.current().nextInt(0, 101);
                Integer i =  semiIntenseCalculation(randomNumforSemi);
                int randomNumforNon = ThreadLocalRandom.current().nextInt(0, 101);
                Integer j = nonIntenseCalculation(randomNumforNon);

                return i+j;
            }
        });
    };

    private Observable<Object> semiIntensiveObservable() {
        return Observable.fromCallable(new Callable<Object>() {
            @Override
            public Object call() throws Exception {
                int randomNumforSemi = ThreadLocalRandom.current().nextInt(0, 101);
                return semiIntenseCalculation(randomNumforSemi);
            }
        });
    };

    private Observable<Object> nonIntensiveObservable() {
        return Observable.fromCallable(new Callable<Object>() {
            @Override
            public Object call() throws Exception {
                int randomNumforNon = ThreadLocalRandom.current().nextInt(0, 101);
                return nonIntenseCalculation(randomNumforNon);
            }
        });
    };

    public interface Worker {
        void work();
    }

    @Setup
    public void setup(final Blackhole bh) {

        workerSequential = new Worker() {

            @Override
            public void work() {


                Observable.just(intensiveObservable())
                  .subscribe(new Subscriber<Object>() {

                    @Override
                    public void onError(Throwable error) {

                    }

                    @Override
                    public void onCompleted() {

                    }

                    @Override
                    public void onNext(Object arg) {
                        bh.consume(arg);

                    }
                });
            }
        };

        workerParallel = new Worker() {
            @Override
            public void work() {
                Observable.zip(semiIntensiveObservable().subscribeOn(Schedulers.computation()),
                               nonIntensiveObservable().subscribeOn(Schedulers.computation()),
                               new Func2<Object, Object, Object>() {

                    @Override
                    public Object call(Object semiIntensive, Object nonIntensive) {
                        return (Integer)semiIntensive + (Integer)nonIntensive;
                    }

                }).subscribe(bh::consume);
            }
        };

    }

    @Benchmark
    public void calculateSequential() {
        workerSequential.work();
    }

    @Benchmark
    public void calculateParallel() {
        workerParallel.work();
    }
}

I am puzzled by the result:

# Run complete. Total time: 00:00:21

Benchmark                        Mode  Cnt      Score      Error  Units
MyBenchmark.calculateParallel    avgt    5  15602,176 ± 1663,650  ns/op
MyBenchmark.calculateSequential  avgt    5    288,128 ±    6,982  ns/op

Obviously I was expecting the parallel calculation to be quicker. Is RxJava only good for parallel I/O or why am I getting these results?

score 1 · Accepted Answer · answered Sep 18 '17 at 17:00

You are doing the benchmark wrong. You should be waiting for the parallel work to finish otherwise (via blockingSubscribe) you'll start quite a lot of them which adds significant GC overhead and inflates the internal queues of the executors as well.

Here is the reference benchmark for measuring various parallel work. Note that dispatching work has overhead on its own and unless you have 500+ cycles per work item in a parallel setting, you may not see any improvements in such fork-join type parallel workload.

Awesome! It _is_ the dispatching overhead and comparison to more conventional java implementations of multi-threading that interest me, and ParallelPerf.java is a great starting point! — ChopperOnDick, Sep 20 '17 at 11:40

RxJava calculation in parallel

1 Answers1