Have you ever scrolled someone’s code and bumped into this weird method called flatMap, not knowing what it actually does from the context? Or maybe you compared it with method map but didn’t really see much difference? If that is the case then this article is for you. 

flatMap is extremly usfull when you try to do proper functional programming. In Java it usually means using Streams and Optionals - concepts introduced in version 8. These two types can be thought of as some kind of wrapper over a type, which adds some extra behaviour - Stream<T> wrapps type T allowing to store any number of elements of type T inside, whereas Optional<T> wrapps some type T to implicitly say that the element of that type may or may not be present inside. Both of them share methods map and flatMap. Before we move on, I want to make sure you understand what the map method is doing. It basically allows you to apply the method to the element inside the wrapper and possibly change it’s type:
Function<String, Long> toLong = Long::parseLong; // function which maps String to Long
Optional<String> someString = Optional.of("12L");
Optional<Long> someLong = someString.map(toLong); //aplying the function to the possible String inside
Stream<String> someStrings = Stream.of("10L", "11L");
Stream<Long> someLongs = someStrings.map(toLong); //applying the function to all Strings inside

After applying the function toLong to our wrappers, their inner types changed to the second type of the toLong signature (the result type). 
Let’s examine the function Long::parseLong. If we call it using a string that is not actually a valid long it will throw NumberFormatException. But what if Java designers decide to implement it so it returns Optional<Long> instead of just Long and removed the exception? Our code for Optional part would look like that:

Function<String, Optional<Long>> toLongOpt = Long::parseLongOpt;//method I made up
Optional<String> someString = Optional.of("12L");
Optional<Optional<Long>> someLong = someString.map(toLongOpt); //:<

Wow, that is nasty! When we appliednew method to our wrapper, the inner type was changed from String to Optional<Long> (the result type of toLongOpt which we applied). We don’t really need to have a double Optional because just one is perfectly fine. Now, to get the value we need to extract it twice, not mentioning how it would look like when we want to map it again without unwrapping… To restore it to the single type we would need to write a method like this one:

public static <T> Optional<T> flatten(Optional<Optional<T>> optional) {
    return optional.orElse(Optional.empty());
}

This method will flatten our Optional<Optional<T>> to Optional<T> without changing the inner value. The code will look like this:

Function<String, Optional<Long>> toLongOpt = Long::parseLongOpt;
Optional<String> someString = Optional.of("12L");
Optional<Long> someLong = flatten(someString.map(toLongOpt));

This is exactly what method flatMap is doing. It first applies the function returning another Optional to the inside object (if present) and then flattens the result before returning it, so you don’t have to do it yourself. This is how we can use it:

Function<String, Optional<Long>> toLongOpt = Long::parseLongOpt;
Optional<String> someString = Optional.of("12L");
Optional<Long> someLong = someString.flatMap(toLongOpt);

For Stream, we can use it in the situation when the function we want to map our elements with, returns the Stream. (example signature: Function<String, Stream<Long>>).

That’s it. Just remember that flatMap = map + flatten.

If you want to dive into the topic read about the Functor and Monad concepts and the relationship between them. 
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I have recently started implementing different distributed system protocols to get some understanding how they work. I think that using Akka Actors to simulate hosts is a good choice because they are easy to set up. What is more you can kill actors on demand to test some failure scenarios.

First protocol I implemented is Paxos - probably the most important consensus protocol, which guarantees storng consistecy, full transactions, read/write failover and prevents from data loss. This comes with the cost of higher latency and lower throughput. In terms of CAP theorem, Paxos is used to make your system CP. I used this sweet article as a reference.

Implementation can be found on my github repository. Go to the Test.scala and run some simulations.

When I started working as a Java Developer, me and my teammate got a first task to repair all broken tests (great task for new starters!) in some old project. Replacing some old configuration and upgrading a few libraries helped making the tests status green but there was another problem. End-to-end test suite took a few hours to pass making it impossible to get the feedback for a new change quickly.

In this article I am going to share some cool features I stumbled upon while coding with Intellij. These are not the most popular/productivity improving ones - for these you should watch this video. 

1. Set debugger breakpoint by pattern

Problem: You want to check if the debugger steps into some code during the run. Usually you suspect where the flow will end up but sometimes finding the exact line could be hard.

I have recently pushed very simple Map Reduce concept implementation on my Github account (click). My idea was to focus on the concept and mock the rest.

In this article I will try to map methods of Java’s Optional to Kotlin’ssimilar scattered language features and built-in functions. The code in the examples is written in Kotlin, because the language has all the JDK classes available.

Representation

Let’s start with the representation.

Have you ever scrolled someone’s code and bumped into this weird method called flatMap, not knowing what it actually does from the context? Or maybe you compared it with method map but didn’t really see much difference? If that is the case then this article is for you. 

flatMap is extremly usfull when you try to do proper functional programming. In Java it usually means using Streams and Optionals - concepts introduced in version 8.

Fact - End-to-end  tests are critical if you want to make sure your software works as it should. To be 100% sure that you covered every (or almost every) possible branch in your business code, it is worth to check what code has been invoked after your E2E suite finished successfully. The solution I am going to present may be much easier, if you are able to stand up your application locally. Sometimes though, it is not the case.

Functional Programming in Java

Stream and Optional classes - added to Java 8 - allow you to have some fun with functional programming. The problem is Java still misses quite a lot to be taken as a serious FP language. Lambda notation and two monads (Optional and Stream) are just the tip of the iceberg. This leads to arising of libraries like vavr or functionaljava - both deriving from purely functional language Haskell.

In this article, I am going to present you a simple trick that will make using java.util.function.Function.andThen() more useful. 

As an example I will use ExternalSystemGateway class, which job is to call external system along with serializing/mapping the messages:

You can see that every line of the invoke method does some kind of action, which transforms some input type to another output type.
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Checked exceptions & Java 8

Defining custom exceptions (both checked and unchecked) is a common approach to handling errors in Java applications. It usually leads to creating a new class for every different type of error, marking methods with throws keyword or wrapping code with try-catch blocks. This can lead to the code which is hard to read since every block adds another level of complexity. 

Lambdas in Java 8 started the boom for functional approach in writing the code.
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