Parallel streams in java 8

The slides from the talk I gave at Oracle III #JuevesTecnológicos in Madrid.

A review of how the ParallelStreams Work in Java 8 and some considerations we must know in order to get the better performance from the concurrent data processing in #Java8

1. ParallelStreams
Concurrent data processing in Java 8
David Gómez G.
@dgomezg
[email protected]

2. Do you remember?
use stream()
for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers.parallelStream() 

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());


System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
Thread. activeCount());

}
4999299 elements computed in 225 msecs with 9 threads
4999299 elements computed in 230 msecs with 9 threads
4999299 elements computed in 250 msecs with 9 threads
@dgomezg

3. Previously on…

4. Streams?
What’s that?

5. A Stream is…
An convenience method to iterate over
collections in a declarative way
List<Integer>,numbers,=,new,ArrayList<Integer>();

for,(int,i=,0;,i,<,100,;,i++),{

,numbers.add(i);

},
List<Integer> evenNumbers = numbers.stream() 

.filter(n -> n % 2 == 0)

.collect( toList());
@dgomezg

6. Anatomy of a Stream
Source
Intermediate
Operations
filter
map
order
function
Final
operation
pipeline
@dgomezg

7. Iterating a Stream
List<Integer> evenNumbers = numbers.stream() 

.filter(n -> n % 2 == 0)

.collect( toList());
Internal Iteration
- No manual Iterators handling
- Concise
- Fluent API: chain sequence processing
Elements computed only when needed
@dgomezg

8. Iterating a Stream
List<Integer> evenNumbers = numbers.parallelStream() 

.filter(n -> n % 2 == 0)

.collect( toList());
Easily Parallelism
- Concurrency is hard to be done right!
- Uses ForkJoin
- Process steps should be
- stateless
- independent
@dgomezg

9. Parallel Streams
use stream()
List<Integer> numbers = new ArrayList<>();

for (int i= 0; i < 10_000_000 ; i++) {

numbers.add(( int)Math.round(Math.random()*100));

}
//This will use just a single thread
Stream<Integer> evenNumbers = numbers.s tream();
or parallelStream()
//Automatically select the optimum number of threads
Stream<Integer> evenNumbers = numbers.parallelStream();
@dgomezg

10. Let’s test it
use stream()
for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers.stream() 

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());


System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
Thread. activeCount());

}
5001983 elements computed in 828 msecs with 2 threads
5001983 elements computed in 843 msecs with 2 threads
5001983 elements computed in 675 msecs with 2 threads
5001983 elements computed in 795 msecs with 2 threads
@dgomezg

11. Going parallel
use stream()
for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers.parallelStream() 

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());


System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
Thread. activeCount());

}
4999299 elements computed in 225 msecs with 9 threads
4999299 elements computed in 230 msecs with 9 threads
4999299 elements computed in 250 msecs with 9 threads
@dgomezg

12. Previously on…
http://www.slideshare.net/dgomezg/streams-en-java-8

13. Parallelism
Under the hood

14. Fork/Join Framework
Proposed by Doug Lea
"a style of parallel programming in
which problems are solved by
(recursively) splitting them into
subtasks that are solved in parallel."
Available in Java 7
Used by ParallelStreams

15. The F/J algorithm
Result solve(Problem problem)
{
if (problem is small)
directly solve problem
else
{
split problem into independent parts
fork new subtasks to solve each part
join all subtasks
compose result from subresults
}
}
as proposed by Doug Lea

16. ForkJoinPool
ExecutorService implementation that
• has a defined number of Workers (threads)
• executes ForkJoinTasks
• submitted by execute(ForkJoinTask,
task),
• or by invoke(ForkJoinTask,task)

17. ForkJoinTask
Abstract class that represents a task to be run
concurrently
Every ForkJoinTask could be splitted (if not small
enough) and solved Recursively
Two concrete implementations
• RecursiveAction,if not returning value
• RecursiveTask,if returning a value

18. ForkJoinWorkerThread
Any of the threads created by the ForkJoinPool
Executes ForkJoinTasks
Everyone has a Dequeue for tasks (allows task
stealing)

19. ForkJoinWorkerThread
Result solve(Problem problem)
{
if (problem is small)
directly solve problem
else
{
split problem into independent parts
fork new subtasks to solve each part
join all subtasks
compose result from subresults
}
}
the F/J algorithm
plus Task Stealing.

20. Fork/Join. When to use?
For computations that could be splitted into smaller
tasks
aka ‘divide and conquer’ algorithms
Independent
Reduction with no contention.

21. ParallelStreams
in action!

22. ParallellStreams
for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers. parallelStream()

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());


System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
Thread. activeCount());

}
4999299 elements computed in 225 msecs with 9 threads
4999299 elements computed in 230 msecs with 9 threads
4999299 elements computed in 250 msecs with 9 threads

23. Thread.activeCount not accurate
for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers. parallelStream()

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());


System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
Thread. activeCount());

}
Thread.activeCount() does not show the effective
number of threads processing the stream

24. Better count threads involved
Set<String> workerThreadNames = new ConcurrentSet<>();

for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers.stream() 

.filter(n -> n % 2 == 0)

.peek(n -> workerThreadNames.add(
Thread. currentThread().getName()))

.sorted() 

.collect( toList());



System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
workerThreadNames.size());

}

25. Threads usage
ParallelStreams use the common ForkJoinPool
Number of worker threads configured with
PDjava.util.concurrent.ForkJoinPool.common.parallelism= n
Useful to keep CPU parallelism under control…
…but …

26. Limiting parallelism
for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers.stream() 

.filter(n -> n % 2 == 0)

.peek(n -> workerThreadNames.add(
Thread. currentThread().getName()))

.sorted() 

.collect( toList());



System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
workerThreadNames.size());

}
PDjava.util.concurrent.ForkJoinPool.common.parallelism= 4
5001069 elements computed in 269 msecs with 5 threads
WTF

27. Limiting parallelism
for (int i = 0; i < 100; i++) {

long start = System.currentTimeMillis();

List<Integer> even = numbers.stream() 

.filter(n -> n % 2 == 0)

.peek(n -> workerThreadNames.add(
Thread. currentThread().getName()))

.sorted() 

.collect( toList());



System.out.printf(
"%d elements computed in %5d msecs with %d threads\ n”,

even.size(), System. currentTimeMillis() - start,
workerThreadNames.size());

}
System.out.println("credits to threads: “
+ workerThreadNames);
5001069 elements computed in 269 msecs with 5 threads
credits to threads:
ForkJoinPool.commonPool-worker-0,
ForkJoinPool.commonPool-worker-1,
ForkJoinPool.commonPool-worker-2,
ForkJoinPool.commonPool-worker-3, main
WTF

28. Threads Involved in ParallelStream
ParallelStreams use the common ForkJoinPool
Thread invoking ParallelStream also used as
Worker
Caveats:
•ParallelStream processing is synchronous for
invoking thread
•Other Threads using common ForkJoinPool
could be affected

29. ParallelStream Hack
ParallelStream can be forced to use a custom
ForkJoinPool
ForkJoinPool forkJoinPool = new ForkJoinPool(4);



long start = System.currentTimeMillis();

numbers.parallelStream() 

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());




30. ParallelStream Hack
ParallelStream can be forced to use a custom
ForkJoinPool
ForkJoinPool forkJoinPool = new ForkJoinPool(4);



long start = System.currentTimeMillis();

ForkJoinTask<List<Integer>> task = 

forkJoinPool.submit(() -> { 

return numbers.parallelStream() 

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());

}

);
List<Integer> even = task.get();

31. ParallelStream Hack
ParallelStream can be forced to use a custom
ForkJoinPool
ForkJoinPool forkJoinPool = new ForkJoinPool(4);



ForkJoinTask<List<Integer>> task = 

forkJoinPool.submit(() -> { 

return numbers.parallelStream() 

.filter(n -> n % 2 == 0)

.sorted() 

.collect( toList());

}

);
List<Integer> even = task.get();
Task submitted in 1 msecs
5000805 elements computed in 328 msecs with 4 threads

32. ParallelStream Hack benefits
A custom ExecutorService
• Does not affect other ParallelStreams
• Does not affect Common ForkJoinPool users
• Reduces unpredictable latency due to other
CommonForkJoin Pool load
• Invoking thread not used as worker (async
parallel process)

33. Problems derived from
Common ForkJoinPool

34. Blocking for IO
If firsts URLs stuck on a ConnectionTimeOut, overall
performance could be affected
Stream<String> urls =
Files.lines(Paths.get("urlsToCheck.txt"));



List<String> errors = urls.parallel().filter(url -> { 

//Connect to URL and wait for 200 response or timeout 

return true;

}).collect(toList());


35. Nested parallelStreams
Outer parallelStream could exhaust ForkJoin
Workers:
long start = System.currentTimeMillis();

IntStream.range(0, 10_000).parallel()
.forEach(i -> {

results[i][0] = (int) Math.round(Math.random() * 100);



IntStream.range(1, 9_999)
.parallel().forEach((int j) ->
results[i][j] =
(int) Math.round(Math.random() * 1000));



});

Process finalized in 22974 msecs
Process finalized in 22575 msecs
Process finalized in 22606 msecs

36. Nested parallelStreams
Outer parallelStream could exhaust ForkJoin
Workers:
long start = System.currentTimeMillis();

IntStream.range(0, 10_000).parallel()
.forEach(i -> {

results[i][0] = (int) Math.round(Math.random() * 100);



IntStream.range(1, 9_999)
.sequential().forEach((int j) ->
results[i][j] =
(int) Math.round(Math.random() * 1000));



});

Process finalized in 12491 msecs
Process finalized in 12589 msecs
Process finalized in 12798 msecs

37. Other performance
problems

38. Too much Auto(un)boxing
outboxing and boxing of Integers in every filter call
List<Integer> even = numbers.parallelStream() 

.filter(n -> n % 2 == 0)

.sorted()

.collect(toList());

4999464 elements computed in 290 msecs with 8 threads
4999464 elements computed in 276 msecs with 8 threads
4999464 elements computed in 257 msecs with 8 threads
4999464 elements computed in 265 msecs with 8 threads

39. Less Auto(un)boxing
outboxing and boxing of Integers in every filter call
List<Integer> even = numbers.parallelStream() 

.mapToInt(n -> n) 

.filter(n -> n % 2 == 0)

.sorted()

.boxed()

.collect(toList());
4999460 elements computed in 160 msecs with 8 threads
4999460 elements computed in 243 msecs with 8 threads
4999460 elements computed in 144 msecs with 8 threads
4999460 elements computed in 140 msecs with 8 threads

40. Conclusions

41. Conclusions
ParallelStreams eases concurrent processing but:
• Understand how it works
• Don’t abuse the default common ForkJoinPool
• Don’t use when blocking by IO
• Or use a custom ForkJoinPool
• Avoid unnecessary autoboxing
• Don’t add contention or synchronisation
• Be careful with nested parallel streams
• Use method references when sorting

42. Thank You.
@dgomezg
[email protected]