Java 8 Concurrency Updates

Oct 13, 20141 like604 views

This document summarizes new concurrency features in Java 8 including parallel streams, ConcurrentHashMap improvements, Striped64 for lock-free counting, CompletableFuture for asynchronous programming, and @Contended annotation for avoiding false sharing. It also discusses best practices for using parallel streams and new concurrent collection classes like ConcurrentHashMap.

Java 8 Concurrency Updates
Damian Łukasik

Parallel Streams
ConcurrentHashMap
Striped64
CompletableFuture
@Contended

List<Integer> ints = range(1, 5)
ints
.parallelStream()
.forEach(e -> print(e));
>> 3 2 4 1 5
Parallel Streams

final int[] counter = {0};
List<Integer> ints = range(1, 10000);
ints
.parallelStream()
.map(e -> {
counter[0]++;
return e;
});
>> 7329 Parallel Streams

ForkJoinPool.commonPool()
Parallel Streams

ForkJoinPool forkJoinPool =
new ForkJoinPool(1);
forkJoinPool.submit(() ->
ints.parallelStream()
.forEach(e -> counter[0]++)
).get();
>> 10000
Parallel Streams

List<Integer> ints = range(1, 5)
List<Integer> collected =
ints
.parallelStream()
.collect(Collectors.toList());
print(collected)
>> ???
Parallel Streams

List<Integer> ints = range(1, 5)
List<Integer> collected =
ints
.parallelStream()
.collect(Collectors.toList());
print(collected)
>> 1 2 3 4 5
Parallel Streams

Collector
.Characteristics
.CONCURRENT
Parallel Streams

Collectors
groupingByConcurrent()
toConcurrentMap()
Parallel Streams

Math.sin()
size parallel [ns] sequential [ns] ratio
1 77 51 -34%
10 7695 578 -92%
100 5461 6172 +13%
1000 25818 60636 +135%
10000 181363 621829 +243%
100000 1287799 6829144 +430%
1000000 12858699 70854694 +451%
Parallel Streams

Rule 1:
Fast functions and predicates
Parallel Streams

Rule 2:
Avoid side effects
Parallel Streams

Rule 3:
Don't parallel() everything
Parallel Streams

No more memory pitfall :)
ConcurrentHashMap

Power behind
Long/DoubleAdder
and
Long/DoubleAccumulator
Striped64

LongAdder
increment()
decrement()
add(+/-42)
sum()
Striped64

class FalseSharing {
int a;
int b;
}
--)(----)(--AB)(----)(--
@Contended

class FalseSharing {
int a;
@Contended int b;
}
--)(-A##)(####)(##B#)(##
@Contended

Feedback!
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Aim of this presentation is not to make you masters in Java 8 Concurrency, but to help you guide towards that goal. Sometimes it helps just to know that there is some API that might be suitable for a particular situation. Make use of the pointers given to search more and learn more on those topics. Refer to books, Java API Documentation, Blogs etc. to learn more. Examples and demos for all cases discussed will be added to my blog www.javajee.com.

Safe Automated Refactoring for Intelligent Parallelization of Java 8 StreamsRaffi Khatchadourian

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of $\sim$642 thousand lines of code. We found that 36.31% of candidate streams were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.

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This document summarizes Speedment, an open source Java library that allows developers to work with relational databases using Java 8 features like lambda expressions and streams. Speedment was created in 2010 to simplify database access and increase productivity by avoiding object-relational mappers. It allows developers to focus on business logic rather than database queries. The Speedment API embraces Java 8 paradigms like streams, filters, and optionals to interact with database entities. The document outlines how Speedment can be used to perform common database tasks like initialization, persistence, querying, and multi-threading in just a few lines of code without writing SQL. It also describes how Speedment is configured and concludes that it allows code generation

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Java 8 Concurrency Updates

1. Java 8 Concurrency Updates Damian Łukasik

2. Parallel Streams ConcurrentHashMap Striped64 CompletableFuture @Contended

3. Parallel streams

4. List<Integer> ints = range(1, 5) ints .parallelStream() .forEach(e -> print(e)); >> 3 2 4 1 5 Parallel Streams

5. final int[] counter = {0}; List<Integer> ints = range(1, 10000); ints .parallelStream() .map(e -> { counter[0]++; return e; }); >> 7329 Parallel Streams

6. ForkJoinPool.commonPool() Parallel Streams

7. ForkJoinPool forkJoinPool = new ForkJoinPool(1); forkJoinPool.submit(() -> ints.parallelStream() .forEach(e -> counter[0]++) ).get(); >> 10000 Parallel Streams

8. List<Integer> ints = range(1, 5) List<Integer> collected = ints .parallelStream() .collect(Collectors.toList()); print(collected) >> ??? Parallel Streams

9. List<Integer> ints = range(1, 5) List<Integer> collected = ints .parallelStream() .collect(Collectors.toList()); print(collected) >> 1 2 3 4 5 Parallel Streams

10. Collector .Characteristics .CONCURRENT Parallel Streams

11. Collectors groupingByConcurrent() toConcurrentMap() Parallel Streams

12. Math.sin() size parallel [ns] sequential [ns] ratio 1 77 51 -34% 10 7695 578 -92% 100 5461 6172 +13% 1000 25818 60636 +135% 10000 181363 621829 +243% 100000 1287799 6829144 +430% 1000000 12858699 70854694 +451% Parallel Streams

13. Rule 1: Fast functions and predicates Parallel Streams

14. Rule 2: Avoid side effects Parallel Streams

15. Rule 3: Don't parallel() everything Parallel Streams

16. ConcurrentHashMap

17. No more memory pitfall :) ConcurrentHashMap

18. Concurrency level ConcurrentHashMap

19. Load factor ConcurrentHashMap

20. Treeification ConcurrentHashMap

21. Striped64

22. Power behind Long/DoubleAdder and Long/DoubleAccumulator Striped64

23. LongAdder increment() decrement() add(+/-42) sum() Striped64

24. T1 10 Striped64