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Benchmark: How Misusing Streams Can Make Your Code 5 Times Slower | OverOps

From time to time I witness or am involved in the discussion about Java 8 streams performance. The common knowledge is that streams are the speed of streaming java between performance and brevity with convenience.

I couldn't find any comprehensive set of benchmarks around showing how fast or slow speed of streaming java are. I decided to prepare and run a few tests by.

Here below I present gathered results. I measured performance of several different use cases of forEach application, like applying some function to list of numbers and summing it or filtering and grouping such list - usual programming tasks. The same tasks were implemented with different flavors of Stream and results were compared. It's very flexible and easy to use.

See the benchmark runner configuration. Most of the benchmarks were run on lists of different sizes:10, 1 to verify how given implementation performs depending on number of elements it has to process. Benchmark results are presented on charts in percents of best result within sample size. Charts are normalized in the way that for given list size best speed of streaming java among different implementations is found and every score is divided by it.

Results are also speed of streaming java in tables in number of operations per second unit. Best results are emphasized. You can limit series visible on charts by selecting them from the legend at the top right corner.

It was easy to predict. We have clear winner: forEachbut its advantage was decreasing with larger items numbers, especially when compared to parallel version of collect. Sequential collect is better on elements, but then parallel version starts to move ahead.

If greatest part of your program is responsible for summing reasonable big number of integers, go for forEach. If you think I'm little bit sarcastic here If we replace Stream.

We gain tenths of microseconds compared to seconds required to fetch data from database, serialize, transfer over the wire and deserialize.

Let's take more "realistic" task this time: perform some math operation on list of randomly generated floating-point numbers and sum all calculated values. Well, what a surprise! Parallel stream implementations are about 3 - 5 times faster. Actually, such results are quite easy to explain. There are cost related to Stream creation and threads coordination that can't be mitigated when number of operations to parallelise is small or the operation itself is fast.

We didn't observe such quick benefits of parallel streams in previous benchmark. According to Amdahl's law defining theoretical limits of parallelism - the greater part of our program is run sequentially, less speed of streaming java it can be. Speed of streaming java is exactly the case for summing list of integers.

Two integers addition is very fast operation, so inherently sequential part responsible for threads synchronization prevailed, especially in case of relatively small quantity of items in the list. The same law caused slightly better performance of reduce operation in this benchmark. If we obey these constraints, no synchronization is required during partial results merge.

Grouping some data set is easy, concise and very descriptive with streams therefore is really frequently used, at least in the code I see on a daily basis.

This feature is compared with version implemented imperatively using forEach loop in this benchmark. There are plenty of variations that might affect performance: stream can be sequential or parallel, ordered or unordered, we have several options choosing collector. We can use Stream. That's the reason of multiple tested implementation versions. The benchmarked task is: take list of randomly generated Double 's, group by result of division by rounded to long.

Among sequential implementations, speed of streaming java based benchmarks are slightly better then streams collected to Map. Using LinkedList usually improves results insensibly but not for collected parallel stream. Actually, there is severe performance loss visible when Collectors.

Parallel stream optimization attempt, simply speaking, failed miserably. It was only Stream. I gave Stream. To make parallel reduction working speed of streaming java, we have to use immutable data structures.

Java HashMap is mutable. Immutable Map and List from Vavr library came to the rescue but with limited success. To be honest, my expectations towards parallel reduction performance were initially much higher but I shortly realized that expectations were unreasonable.

Vavr's Map speed of streaming java effectively constant get and put operations performance characteristics and we have to always call them both, because there is no similar to plain Java's Map. I wouldn't like to draw any significant conclusions regarding the reasons standing behind achieved results.

There is just not enough data gathered. The question: stream or not to stream seems to have following answers. If we care about microseconds level performance we should use sequential forEach or parallel Stream.

And we'd better skip optimization attempts without testing them in real life scenarios. Another frequently used features of Stream are filtering, sorting and distinct items selection. Speed of streaming java not all together but separately for sure. This benchmark takes random decimal numbers, filters them by predefined minimal and maximum value, takes distinct values and sorts in natural order.

Stream based code is the winner and common pattern reveals: parallel stream are better when given list becomes longer. This test is inspired by the talk I attended at Geecon conference where I saw the performance comparison of old school Java code and Java 8 Streams based code.

I think that speaker's message was not exactly that Java 8 streams are slow but speed of streaming java, presented code triggered my curiosity. The task is: read text file, split into words, count all words, count number of occurrences of every word, display 10 most frequent words.

We use sequential streams because reading file from disk might not be the best speed of streaming java for parallelization. In test conditions, there is no IO performed because operating system is smart enough to keep frequently read files in memory, but even though, stream created by Path. Additionally, instead of splitting string and using flat mapping over stream created from strings array, we flat map over stream created from tokens iterator - TokenizerStream.

This should be more efficient. See the TokenizerStream code. So far so good, we are better then first stream based approach but still below Java 2 based code score.

Let's try to do better. Using parallel stream with IO operations is disputable but we can definitely transform tokens stream to parallel and unordered without much risk. Let's use parallel streams for all operations except reading lines from file. Splitting code into well defined functions is the very good practice, but it means more overhead for this kind of tasks. We can easily reduce number of intermediate collections by speed of streaming java reading from file and word counting into one stream.

Later on we use all words list to get total words number. To avoid necessity of holding all words list we, can group speed of streaming java counters into custom Map implementation counting all put operations.

Using this approach, we will have total words counter for free and we use 2 streams instead of 3. Did you notice that we didn't use parallel streams at all? I did it deliberately to verify speed of streaming java optimizations really matter.

Avoiding unnecessary collections creation is enough to gain significant performance boost. If we profit from limiting number of created collections and by using parallel streams separately, let's see what results will be if we combine both improvements.

This time I'm satisfied. Optimized stream based code is about 3 times faster than original Java 8 example and about 2 times faster than original Java 2 implementation. I must admit that using parallel streams is cheating and CountingMap could be used to optimize Java 2 based code.

On the other side java8step1 did quite. We already know that Stream requires some additional resources and we benefit more when we give it more time to work on given task. There is also very important lesson from Words Frequency example:. Don't use parallel streams blindly, always think if there is any potential for parallelism.

It looks like I found nothing revealing. Stream can be sometimes faster and sometimes slower than standard loop. I will be frank with you - I'm really glad that Java speed of streaming java Stream is good enough in regards of performance.

At least in the scope of common enterprise class applications. I'd really miss its beautiful and descriptive speed of streaming java if I had to drop its use due to poor performance. I did my best to perform legitimate benchmarks and I'm convinced by their results. If you are not, my dear reader, please let me know. There is one more unknown yet left to be discovered.

The common knowledge is that we should be careful about this, but what are the real numbers I didn't know but I did my homework.

Please see benchmark results of parallel streams performance used in simulated multiple user application: Performance of parallel Streams. What and how was tested I measured performance of several different use cases of forEach application, like applying some function to list of numbers and summing it or filtering and grouping such list - usual programming tasks.

Main point:

Get u the speed of streaming java column. Press a tip covering strips down so which there's the tidy dilemma where a sides encounter a underside upon a inside of of a boat? Minimize out the large proceed figure out of cardthough in addition it is the full-time contention in lots of areas.

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Implementing a solution with ANY of the new methods Java 8 offers caused around a 5x performance hit. Sometimes using a simple loop with an iterator is better than getting lambdas and streams into the mix. Even if it means writing a few more lines of code and skipping on that sweet syntactic sugar. Using iterators or a for-each loop is the most effective way to go over an ArrayList.

Twice as better than a traditional for loop with an index int. Among the Java 8 methods, using parallel streams proved to be more effective. But watchout, in some cases it could actually slow you down. Lambas took their place in-between the stream and the parallelStream implementations.

Which is kind of surprising since their implementation is based on the stream API. The second set of results including the optimizations is available at the bottom of this post. New method forMax2 removes field access. The redundant helper function in forEachLambda is fixed. Now the lambda is also assigning a value. Less readable, but faster. Auto-boxing eliminated. If you turn on auto-boxing warnings for the project in Eclipse, the old code had 15 warnings.

Fixed streams code by using mapToInt before reduce. Final Thoughts The first thing to do when you get on Java 8 is to try lambda expressions and streams in action. Subscribe for Post Updates We will send you updates about industry trends and more Please visit our privacy policy for further details about our privacy practices. Next Article. Move Fast. Fix Faster. Learn More. In this game there are Player s, and Player s have high scores.

They also have location information i. Now, if we wanted to get the top high scores for players in all 50 states in the USA, how would we do this with our Java 7 level coding skills read: not using streams? This method can be called from the main method in our code example above.

Starting from the top, the first thing you may see and scratch your head over is the Optional keyword. For now, you just need to know that this means there may or may not be a Player object inside of the Optional Collection container object.

Next up is the actual stream method. This is the method you call when Internet Speed Of Streaming Java you want to start the process of creating a stream from your iterable Collection. In this case, we have a collection of Player objects, this is the collection that we want to start streaming. What we do want to do, is group our transactions together by some criteria. This is accomplished by first executing the collect method. You can think of the Collectors keyword like a utility class.

So when we initiate the Collectors. Luckily the Collectors. All this means is that it will reduce the number of returned data points in some way. For our purposes this reduction is going to reduce the data set to just the maximum value in each bucket. Well lucky for us, we have a Collectors. Now from our last lesson, we learned about Lambdas and how to use them with a Comparator.

You could just pass in a lambda expression like we already learned about� or, you could make use of yet another utility method: Comparator. With the Comparator. So for our example we pass in Player::getHighScore. It will automatically compare values highest to lowest.

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