Java 8 stream map exception handling

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Java 8 stream map exception handling

The Stream API and lambda, where there is a big improvement in Java since version 8. From this point on, we can also work in a functional syntax style. Now, after a few years of working with these code structures, one of the bigger questions that remains is how to deal with verified exceptions inside lambda. As you probably all know, it is not possible to call a method that throws directly at the verified exception of lambda. We're going to have to find a way to get the exception to compile the code. Of course we can do a simple try-catch inside the lambda and wrap the exception with RuntimeException, as shown in the first example, but I think we can all agree that this is not the best way to go. myList.stream() .map(item -> { try { return doSomething(item); } catch (MyException e) { throw new RuntimeException(e); } }) .forEach(System.out::p rintln); Most of us are aware that block lambdas are cumbersome and less readable. In my opinion, these should be avoided as much as possible. If you need to do more than one line, you can subtrad the function body into a separate method and simply call it the new method. A better and more readable way to solve this problem is to close the call using a simple old method that does the try-catch and call this method from inside the lambda. myList.stream() .map(this::trySomething) .forEach(System.out::p rintln); private item trySomething(Item item) { try { return doSomething(item); } catch (MyException e) { throw new RuntimeException(e); } } This solution is at least a little more readable and we are separated from our concerns. If you really want to take the exception and do something specific and not simply wrap the exception into a RuntimeException, this can be a possible and readable solution for you. RuntimeException In many cases, you will see that people are using these types of solutions to repack the exception to runtimeException or to repackag an unverified exception in a more specific implementation. With this method you can call within lambda and use higher-order functions. I also relate a little bit to this practice because I personally don't see much value in verified exceptions in general, but it's a completely different discussion that I'm not going to start here. If you want to wrap every call with a lambda that has one logged into a RuntimeException, you will see it repeat the same pattern. To avoid rewriting the same code over and over again, why not abstract it into a utility feature? In this way, you just need to write once and call it every time you need it. To do this, you must first write your own version of the functional interface of a function. Only this time, you need to determine whether the function can cause an exception. @FunctionalInterface public interface CheckedFunction<T,R> { R apply(T) drum Exception; } Now, ready to write your own generic utility function, accepted by CheckedFunction as described in the interface. In this utility function, you can manage the try-catch function and package the original </T,R> </T,R> a RuntimeException (or other unverified version). I know you now have an ugly block of lambda here and you can abstract your body from this. Choose for yourself if it's worth the effort of the only utility. public static <T,R>function<T,R> wrap(CheckedFunction<T,R> checkedFunction) { return t -> { try { return checkedFunction.apply(t); } catch (Exception e) { throw new RuntimeException(e); } }; } With a simple static import, you can now pack your lambda so that you can throw an exception to the brand new utility feature. From this point on, everything will work again. myList.stream() .map(wrap(item -> doSomething(item))) .forEach(System.out::p rintln); The only problem left is that if an exception is made, the processing of the stream stops immediately. If that's not a problem for you, then go for it. However, I can imagine that direct termination is not ideal in many cases. If you're working with streams, you probably don't want to stop processing the stream if an exception occurs. If the stream contains a very large amount of items that you want to process, does the stream stop when, for example, the second item makes an exception? Probably not. Reverse our mindset. Why not take into account the exceptional situation only as much as a possible result as we would a successful result. Consider both as data, continue processing the stream, and then decide what to do with it. We can do it, but in order for that to be possible, we need to introduce a new type - the Both Types. The Either type is a common type in functional languages and not (yet) part of Java. Like the optional type of Java, one is a standard wrapper paper with two options. It can be left or right, but it's never both. Both left and right can be of any type. For example, if you have an Or value, this value can hold either a string type or an integer string, or <String,Integer>. If you use this principle for exception handling, you can say that any of your types holds an exception or value. Convenience, in general, the left is the exception, and the right is the successful value. This can be noted by thinking not only of the right, but also of the good, the reason, etc. The following is a basic implementation of any type. In this case, I used the Optional type, when we try left or right because: public class<L, r=> Or { private final L left; private final R right; private Or (L left, R right) { this.left = left; this.right = right; } public static Either <L,R><L,R> Left( L value) { return new Either(value, null); } public static <L ,R>Either<L,R> Right( R value) { return new Either(null, value); } public Optional<L> getLeft() { return Optional.ofNullable(left); } public getRight() { return Optional.ofNullable(right); } public boolean isLeft() { return left != null; } public boolean isRight() {</R> </L> </L,R> </L,R> </L,R> </L,R> </L,> </String,Integer> </T,R> </T,R> </T,R> </T,R> right != null; } public <T>optional<T> mapLeft(Feature Map) <? super= l,= t=?> { if (isLeft()) { return Optional.of(mapper.apply(left)); } return Optional.empty(); } public <T>Optional<T> mapRight(Function <? super= r,= t=?> mapper) { if (isRight()) { return Optional.of(mapper.apply(right)); } return Optional.empty(); } public String toString() { if (isLeft()) { return Left( + left +); } return Right( + right +); } } } 2000 can now return its own functions instead of making an exception. But it doesn't help if you want to use existing methods to throw the checkered exception inside lambda right? Therefore, we need to add a small utility function to both types described above. public static <T,R><T, either=> function raise(CheckedFunction<T,R> function) { return t -> { try { return Either.Right(function.apply(t)); } catch (Exception ex) { return Either.Left(ex); } }; } By adding this static lift method to either, you can now simply lift a feature that throws in a verified exception and lets you return none. If we accept the original problem, we will now end up with a Stream of Eithers instead of a possible RuntimeException that could blow up the entire Stream. myList.stream() .map(Either.lift(item -> doSomething(item))) .forEach(System.out::p rintln); That simply means we've taken back control. The Stream APU filter function makes it easy to filter out left instances and log them, for example. You can also filter the right instances and easily ignore exceptional cases. Either way, you can regain control again and the stream won't end immediately if a possible RuntimeException occurs. Because Or is a generic wrapper, it can be used for any type, not just for exception handling. This gives you the opportunity to do more than wrap the exception in any left part. The question now may be whether you are just keeping the packaged exception and we can't do it again because we lost the original value. With the help of the ability to or keep anything, we can store both the exception and the value inside the left. To do this, simply use a second static lift function like this. public static <T,R><T, either=> function raisingWithValue(CheckedFunction<T,R> function) { return t -> { try { return Either.Right(function.apply(t)); } catch (Exception ex) { return Either.Left(ex,t)); } }; } You can see that in this liftwithValue function, you can pair both the exception and the original value on one or the left side of the Pair function. We have all the information we need if something goes wrong instead of just the exception. The Few types used here are another generic type that can be found in the Apache Commons lang directory, or you can easily implement your own. Anyway, it's just a type that keep two values. public class Pair<F,S> { public final F fst; public final S snd; private Pair(F fst, S snd) { this.fst = fst; this.snd =</F,S> </T,R> </T,> </T,R> </T,R> </T,> </T> </T> </T> </T> </T> </T> } public static <F,S><F,S> pair(F fst, S snd) { new pair <> (fst,snd); } } } With the use of liftWithValue, we now have all the flexibility and control to use methods that throw exceptions inside lambda. If it's Or Right, we know that the function was applied correctly, and we can expand the result. But if you're a left, we know something went wrong, and we can take out both the exception and the original value to go the way we want it to. By using the Or type, instead of wrapping the check-in in runtimeexception, we prevent the stream from ending halfway. Try people that might have worked for example scala you can use to try instead of treating the or exception. The Try type is something that is very similar to any type. Again, it has two cases: success or failure. The error can only hold the Exception type, while success can retain the type you want. So Try is an implementation of Or, where the type on the left (the error) is fixed for The Exception. public class Try<Exception, r=> { private final Exception failure; private final R succes; public Try(Exception failure, R succes) { this.failure = failure; this.succes = succes; } } Some people are convinced that it is easier to use, but I think that since we can only keep the exception itself in the failure section, we have the same problem as what was explained in the first part of any section. I personally like the flexibility of both types more. In any case, if you're using Try or Either, you'll solve the initial problem of exception handling and won't let the stream stop because of RuntimeException. Directories Both or and Try are very easy to implement yourself. On the other hand, you can also take a look at the functional directories available. For example, VAVR (formerly known as Javaslang) does not have implementations of both the types and assistive features available. I advise you to take a look at it because it takes a lot more than these two varieties. However, you can ask yourself if you want this large directory to be an addiction, only for exception handling, when you can implement it with only a few lines of code. Conclusion If you want to use a method that throws plaidException, you need to do something extra if you want to call lambda. Packing into RuntimeException can be a solution for work. If you prefer this method, I ask you to create a simple packaging tool and reinstall it so you don't interfere with the try/catch all the time. If you want more controls, you can use Or or try types to break the function result so that you can treat it as data. The stream will not cease if runtimeexception is thrown, and you will have the freedom to manage the data inside the stream as you like. If you liked this article and want to learn</Exception,> </F,S> </F,S> for Java Streams, check out this collection of tutorials and articles on all things Java Streams. Streams.

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