*There is a talk about this article here: **https://www.youtube.com/watch?v=vePeILeSv4E*

If you are new to monads, I recommend reading the first part of this series for a basic introduction to monads in Java: **Monads for Java developers: Part 1 — The Optional Monad**

**abstract:** In this second part I will reinforce the the concept of monad by showing what happens inside the bind (flatMap) method. Also I will present the Log class to show how we can have side-effects-free logging capabilities thanks to monadic composition.

## Summary of Part 1

- Monads refer to types that wrap values such as Optional and Stream in Java.
- The monad provides a context to the value: Optional<String> provides an Optional context to the String it wraps.
- A monad has to implement bind (flatMap) and unit. Also it has to comply with three laws: Left identity, Right identity and associativity.
- Monads benefits are clearer when composing them because they help us forget about the context. Composition in this case refers to function composition[1] and not to OOP composition.

Let’s write a monad from scratch with the aim of showing the internals of the flatMap method.

## The Result class

In Java, one way of dealing with failure scenarios in business logic is by throwing exceptions albeit it’s not the only way. Without the aim of discussing when we should use exceptions and when we should not, let’s try to write an alternative to them that will help us to highlight the properties of monads.

The Result class represents the outcome of an operation that might fail. It contains either a value that represents the positive outcome of such operation, or an error String. [2]

First, let’s see the way it can be used:

Here we have some methods that return a Result type. See in the body of the method *adjustValue* how we check a business condition and return *error* with a message or an *ok* result after executing the operation. Also *calculateAverage *does not return a *Result* type since it doesn’t have any business failure scenario*.*

So far, the implementation of the Result class would be rather simple:

Two factory methods, one private constructor and some accessors. Nothing more.

Now, what would happen if we want to operate with methods that returns a *Result* object? Let’s take the ones we defined before and use them inside a business operation:

What we do here is checking the Result of each method invocation to see if there is an error[3]. If not, we continue with the next step. Since *calculateAverage *does not return *Result*, then we just wrap the value with *Result.ok *(line 25)*.*

This is fine, but we have seen this pattern before haven’t we? It is pretty similar to the Optional monad examples in the previous post. So how about we try to convert the *Result* type into a Monad. Remember, we need *unit*, *flatMap* and a bunch of rules[7].

*Unit* is already there: “*Result.ok*”. It will take any type and wrap it in the *Result* context producing a *Result* of that type: *Result.ok(3)* will produce a *Result<Integer>*.

Now let’s add *flatMap* to the *Result* class:

The *flatMap *method receives a *Function,* which we will call ** mapper**, and returns a

*Result*of type

**. Mapper in turn, receives the**

*U**Result*parameter

**T**and returns a

**different**

*Result*with parameter

**U**. This allows us to call

*flatMap*on a

*Result<Integer>*and produce a

*Result<String>*for example.

Look how we invoke the *mapper* function (line 11) only if the *Result* its not an error. In case the *Result* is an error, we just return a new error[4] with the same message (line 8).

After implementing f*latMap*, the last step to transform *Result* into a monad is to check that it complies with the Monads rules. Good news, it does! Here is the proof: https://github.com/afcastano/JavaMonadsExample/blob/master/test/afcastano/monads/result/ResultMonadLawsTest.java

Great! now we have the *Result* monad. It’s very similar to the *Optional* monad, just that it does store an error value instead of empty. Now we should be able to rewrite the *businessOperation* method using monadic composition:

As we did in the previous post when composing Optionals, we chained and nested *flatMap* calls to compose the monads and produce a *Result*. Since *calculateAverage *(line 12)* *needs two values, we had to nest the flatMap calls on lines 10 and 11. Also, remember that *calculateAverage* does not return a *Result* type, so we use the *unit* function (*Result.ok*) to wrap it into the *Result* context. Again, if any of the operations in the composition returns an error, the composition chain stops in there and the final result will be that error. Otherwise you will get an ok *Result<Double>*.

As you can see, we compose the *Result* monad the same way we compose the *Optional* monad. As said before: **All monads compose the same way**. This is one of its main benefits.

Unfortunately, Java does not provide syntax sugar for Monadic composition, but other languages such as Scala or Haskell, take advantage of this and provides nice generic utilities that apply to all monads [5].

Until now we have focused on the way monads are composed, but haven’t explored too much the way they abstract the context from us. Hopefully the *Log* type will show this more explicitly.

**The Log class**

The Log class is an implementation of the Writer monad. The idea with this class is to keep a trace of the changes that a value has suffered during a period of time.

Before diving into the implementation, let’s take a look at how you would use this class.

Here we have some methods that receive a value and return a *Log* object. *Log.trace* would simply create a Log object with a value and a trace associated to it.

The interesting thing happens on the *run* method: Because *Log* is a monad, we compose it the same way we have done it in the previous examples. Remember that it’s the same way for all monads.

If we run this method, the console will output:

`Value: 3.5`

Trace: [initial value: 5, run operation by adding 2: 7,

divided by 4: 1.75, Multiplied by two: 3.5]

The final *Log* contains a value equals to 3.5 and also a trace which is a concatenation of all the traces involved in the composition. Look also how the trace contains the partial result at that particular time.

Remember when I said that Monads help us composing them without needing to worry about the context? Well, in this case the context is the trace added to the value, and effectively, we didn’t do anything to aggregate the trace or to input the partial value to each one of them.

With this approach, we can add logs anywhere and the composed Log monad will aggregate them transparently. Lets change *operation2* to execute two different things instead of one:

The *run* method hasn’t changed and yet the output includes the new logs of *operation2*:

`Value: 6.0`

Trace: [initial value: 5, 2a -> add 2: 7, 2b -> add 5: 12,

divided by 4: 3.0, Multiplied by two: 6.0]

Part of the implementation of the Log type is as follows:

First, each Log contains a value and a list of *String* that represent the trace for the value. On line 6 we have the *trace* method that will create a new *Log *with a list of one trace. Also this is the place where the partial value is added to the trace.

The *flatMap* method (line 10)* *calls the mapper function to obtain the new *Log* (line 11) and then concatenates the current trace with the trace obtained from the *mapper* result. Finally a new *Log* is created with the value from the *mapper* and the aggregated trace.

Monads can wrap other monads as well. If we put a *Result* into a *Log *like this: *Log<Result<Integer>>* we will have a *Log* of a *Result* that yields an *Integer*, so that if it is ok, you will have the *Integer* value and trace of changes, but if the *Result* is error, then you will have an error *Result* and a trace. Effectively, a stack trace with a twist: It will show the partial value of the *Result* on each step.

## To finalise

We can think of Monads as a design pattern that we can use to define the way parameterised types can compose. In Java, they are just another tool that has benefits and drawbacks. In other languages using monads is mandatory since it’s the only way to have side effects[6].

The classes we have explored are implementations of some well known monads: Optional is an implementation of the Maybe monad, Result is an Either monad and Log is a Writer monad. Although there are some famous monads, you can come up with your own one. Just be sure to implement the methods and follow the laws[7].

Monads also exists in non typed languages, such as Javascript. A Promise is a monad in which the *bind* method is called *then*. That is a perfect example of a monad that can not be unwrapped (get the value out). The only way to get the value out of a Promise is via the *bind* (*then*) function.

Thank you for reading.

**ps: **If you liked the article and believe that it might be interesting for someone else, please *❤.*

## Notes:

*[1] Function composition in Java refers to: given B method1(A a) and C method2(B b) then the composition is: c = method2(method1(a))*

*[2] In this case, String could be any type, including an Exception. I am using String for simplicity.*

*[3] A new error has to be created because we have to transform Result<Integer> to Result<Double>*

*[4] Same as [3], we need to transform Result<T> to Result<U>.*

*[5] In Haskell (**https://en.wikibooks.org/wiki/Haskell/do_notation**) and in Scala (**http://lampwww.epfl.ch/~emir/bqbase/2005/01/20/monad.html**) monads have special notation.*

*[6] The side effects I refer in here are wanted side effects, such as writing to disk, or showing something in screen. In Haskell the only way to achieve this is via the IO monad.*

*[7] In this post, the author talks a bit about the benefits of monad laws: **https://cdsmith.wordpress.com/2012/04/18/why-do-monads-matter/**. I plan to write another post that will try to explain the benefits in a more detailed way. If you are interested, leave a comment and I will let you know when the post is ready.*

## Recommended reads:

**http://learnyouahaskell.com/for-a-few-monads-more**** **The monad section of this book is just awesome.

**https://github.com/jasongoodwin/better-java-monads** with the implementation of the Try monad, which is a better version of the Result class we implemented.

**http://nazarii.bardiuk.com/java-monad/** An interesting approach to understand monads using Java.