Stream vs parallelstream java

Java 8 stream vs parallel stream

So we just measured not the Stream API or loop speed, but speed. Solution 1: One and two use ForkJoinPool which is designed exactly for parallel processing of one task while ThreadPoolExecutor is used for concurrent processing of independent tasks.

Java 8 stream and parallelStream

Suppose that we have a Collection like this :

Set> set = Collections.newSetFromMap(new ConcurrentHashMap<>()); for (int i = 0; i < 10; i++) < SetsubSet = Collections.newSetFromMap(new ConcurrentHashMap<>()); subSet.add(1 + (i * 5)); subSet.add(2 + (i * 5)); subSet.add(3 + (i * 5)); subSet.add(4 + (i * 5)); subSet.add(5 + (i * 5)); set.add(subSet); > 

set.stream().forEach(subSet -> subSet.stream().forEach(System.out::println)); 

set.parallelStream().forEach(subSet -> subSet.stream().forEach(System.out::println)); 

set.stream().forEach(subSet -> subSet.parallelStream().forEach(System.out::println)); 

set.parallelStream().forEach(subSet -> subSet.parallelStream().forEach(System.out::println)); 

so, can someone please explain me :

• What is the difference between them?
• Which one is better? faster? and safer?
• Which one is good for huge collections?
• Which one is good when we want to apply heavy processes to each item?

What is the difference between them?

Think of it as like two nested loops.

• In the first case there is no parallelism,
• in the second case the outer loop/collection is parallel
• In the third case the inner loop/collection if more parallel.
• in the last case, you have a mixture of parallelism which is likely to be more confusing than useful.

The forth case isn’t clear as there is only one thread pool in reality and if the pool is busy the current thread can be used, ie it might not be parallel^2 at all.

Which one is better? faster? and safer?

The first one, however using a flat map would be simpler again.

set.stream().flatMap(s -> s.stream()).forEach(System.out::println); 

The other versions are more complicated and since the console, which is the bottle neck, is a shared resource, the multi-threaded version are likely to be slower.

Which one is good for huge collections?

Assuming your aim is to do something other than print, you want to enough tasks to keep all your CPUs busy, but not so many tasks it creates overhead. The second option might be worth considering.

Which one is good when we want to apply heavy processes to each item?

Again the second example, might be best, or possibly the third if you have a small number of outer collections.

Java 8’s streams: why parallel stream is slower?, I am playing with Java 8’s streams and cannot understand the performance results I am getting. I have 2 core CPU (Intel i73520M), Windows 8 x64, and 64-bit Java 8 update 5. I am doing simple map over stream/parallel stream of Strings and found that parallel version is somewhat slower.

Java 8 — What is Parallel Stream || Sequential vs Parallel

In this video, I have explained What is Parallel Stream || Sequential vs Parallel Stream |ForkJoinPool |Prime Number StreamLearn:1. What is ForkJoinPool2. CP

Parallel Stream Vs Stream in java 8 Stream

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Comparison between legacy for loop, streams and parallelStream in Java 8

import java.util.ArrayList; import java.util.List; public class IterationBenchmark < public static void main(String args[])< Listpersons = new ArrayList(); persons.add("AAA"); persons.add("BBB"); persons.add("CCC"); persons.add("DDD"); long timeMillis = System.currentTimeMillis(); for(String person : persons) System.out.println(person); System.out.println("Time taken for legacy for loop : "+ (System.currentTimeMillis() - timeMillis)); timeMillis = System.currentTimeMillis(); persons.stream().forEach(System.out::println); System.out.println("Time taken for sequence stream : "+ (System.currentTimeMillis() - timeMillis)); timeMillis = System.currentTimeMillis(); persons.parallelStream().forEach(System.out::println); System.out.println("Time taken for parallel stream : "+ (System.currentTimeMillis() - timeMillis)); > > 

AAA BBB CCC DDD Time taken for legacy for loop : 0 AAA BBB CCC DDD Time taken for sequence stream : 49 CCC DDD AAA BBB Time taken for parallel stream : 3 

Why the Java 8 Stream API performance is very low compare to legacy for loop?

Very first call to the Stream API in your program is always quite slow, because you need to load many auxiliary classes, generate many anonymous classes for lambdas and JIT-compile many methods. Thus usually very first Stream operation takes several dozens of milliseconds. The consecutive calls are much faster and may fall beyond 1 us depending on the exact stream operation. If you exchange the parallel-stream test and sequential stream test, the sequential stream will be much faster. All the hard work is done by one who comes the first.

Let’s write a JMH benchmark to properly warm-up your code and test all the cases independently:

import java.util.concurrent.TimeUnit; import java.util.*; import java.util.stream.*; import org.openjdk.jmh.annotations.*; @Warmup(iterations = 5, time = 1000, timeUnit = TimeUnit.MILLISECONDS) @Measurement(iterations = 10, time = 1000, timeUnit = TimeUnit.MILLISECONDS) @BenchmarkMode(Mode.AverageTime) @OutputTimeUnit(TimeUnit.MICROSECONDS) @Fork(3) @State(Scope.Benchmark) public class StreamTest < Listpersons; @Setup public void setup() < persons = new ArrayList(); persons.add("AAA"); persons.add("BBB"); persons.add("CCC"); persons.add("DDD"); > @Benchmark public void loop() < for(String person : persons) System.err.println(person); >@Benchmark public void stream() < persons.stream().forEach(System.err::println); >@Benchmark public void parallelStream() < persons.parallelStream().forEach(System.err::println); >> 

Here we have three tests: loop , stream and parallelStream . Note that I changed the System.out to System.err . That’s because System.out is used normally to output the JMH results. I will redirect the output of System.err to nul , so the result should less depend on my filesystem or console subsystem (which is especially slow on Windows).

So the results are (Core i7-4702MQ CPU @ 2.2GHz, 4 cores HT, Win7, Oracle JDK 1.8.0_40):

Benchmark Mode Cnt Score Error Units StreamTest.loop avgt 30 42.410 ± 1.833 us/op StreamTest.parallelStream avgt 30 76.440 ± 2.073 us/op StreamTest.stream avgt 30 42.820 ± 1.389 us/op 

What we see is that stream and loop produce exactly the same result. The difference is statistically insignificant. Actually Stream API is somewhat slower than loop, but here the slowest part is the PrintStream . Even with output to nul the IO subsystem is very slow compared to other operations. So we just measured not the Stream API or loop speed, but println speed.

Also see, it’s microseconds, thus stream version actually works 1000 times faster than in your test.

Why parallelStream is much slower? Just because you cannot parallelize the writes to the same PrintStream , because it is internally synchronized. So the parallelStream did all the hard work to splitting 4-element list to the 4 sub-tasks, schedule the jobs in the different threads, synchronize them properly, but it’s absolutely futile as the slowest operation ( println ) cannot perform in parallel: while one of threads is working, others are waiting. In general it’s useless to parallelize the code which synchronizes on the same mutex (which is your case).

Java 8: performance of Streams vs Collections, Collections: Elapsed time: 41856183 ns (0.041856 seconds) Streams: Elapsed time: 546590322 ns (0.546590 seconds) Parallel streams: Elapsed time: 1540051478 ns (1.540051 seconds) That’ s for a big task! ( int max = 10000000) Conclusion: collecting items to collection took majority of time. The …

Task Executor vs Java 8 parallel streaming

I can’t find a specific answer to the line of investigation that we’ve been requested to take on

I see that parallel streams may not be so performant when using small amount of threads, and that apparently it doesn’t behave so well when the DB blocks the next request while processing the current one

However, I find that the overhead of implementing Task Executor vs parallel streams is huge, we’ve implemented a POC that takes care of our concurrency needs with just this one line of code:

List> listWithAllMaps = mappedValues.entrySet().parallelStream().map(e -> callPlugins(e)) .collect(Collectors.toList()); 

Whereas in Task Executor, we’d need to override the Runnable interface and write some cumbersome code just to get the runnables not to be void and return the values we’re reading from the DB, leading us into several hours, if not days of coding, and producing a less maintainable, more bug prone code

However, our CTO is still reluctant to using Parallel Streams due to unforeseen issues that could come up down the road

So the question is, in an environment where I need to make several concurrent read-only queries to a database, using different java-components/REST calls for each query: Is it preferrable in any way to use Task Executor instead of parallel streaming, if so, why?

Use the TaskExecutor as an Executor for a CompletableFuture .

List futures = mappedValues.entrySet().stream().map(e - > CompletableFuture.supplyAsync(() -> callPlugins(e), taskExecutor)).collect(Collectors.toList()); List> listWithAllMaps = futures.stream().map(CompletableFuture::join).collect(Collectors.toList()); 

Not sure how this is cumbersome. Yes it is a bit more code, but with the advantage that you can easily configure the TaskExecutor and increase the number of threads, queueu-size etc. etc.

DISCLAIMER: Typed it from the top of my head, so some minor things might be of with the code snippet.

Java 8 parallel stream confusion/issue, I am new to parallel stream and trying to make 1 sample program that will calculate value * 100(1 to 100) and store it in map. While executing code I am getting different count on each iteration. java 8 parallel stream confusion/issue. Ask Question Asked 3 years, 8 months ago. Modified 3 years, 8 …

Performance of Java Parallel Stream vs ExecutorService

Suppose we have a list and want to pick all the elements satisfying a property(let say some functions f). There are 3 ways to parallel this process.

listA.parallelStream.filter(element -> f(element)).collect(Collectors.toList()); 

listA.parallelStream.collect(Collectors.partitioningBy(element -> f(element))).get(true); 

ExecutorService executorService = Executors.newFixedThreadPool(nThreads); //separate the listA into several batches for each batch < Future> result = executorService.submit(() -> < // test the elements in this batch and return the validate element list >); > //merge the results from different threads. 

Suppose the testing function is a CPU intensive task. I want to know which method is more efficient. Thanks a lot.

One and two use ForkJoinPool which is designed exactly for parallel processing of one task while ThreadPoolExecutor is used for concurrent processing of independent tasks. So One and Two are supposed to be faster.

When you use .filter(element -> f(element)).collect(Collectors.toList()) , it will collect the matching elements into a List , whereas .collect(Collectors.partitioningBy(element -> f(element))) will collect all elements into either of two lists, followed by you dropping one of them and only retrieving the list of matches via .get(true) .

It should be obvious that the second variant can only be on par with the first in the best case , i.e. if all elements match the predicate anyway or when the JVM’s optimizer is capable of removing the redundant work. In the worst lase, e.g. when no element matches, the second variant collects a list of all elements only to drop it afterwards, where the first variant would not collect any element.

The third variant is not comparable, as you didn’t show an actual implementation but just a sketch. There is no point in comparing a hypothetical implementation with an actual. The logic you describe, is the same as the logic of the parallel stream implementation. So you’re just reinventing the wheel. It may happen that you do something slightly better than the reference implementation or just better tailored to your specific task, but chances are much higher that you overlook things which the Stream API implementors did already consider during the development process which lasted several years.

So I wouldn’t place any bets on your third variant. If we add the time you will need to complete the third variant’s implementation, it can never be more efficient than just using either of the other variants.

So the first variant is the most efficient variant, especially as it is also the simplest, most readable, directly expressing your intent.

Advantages of parallelStream in Java SE8, Stream of numbers is created by a range method. The stream is then switched to parallel mode, numbers that are not primes are filtered out and the remaining numbers are counted. You can see that stream API allow us to describe the problem in a neat and compact way. Moreover, parallelization is just a matter …

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