How to solve it? Complexity in code - Improving flow control through functional pipelines

Code: https://gitlab.com/danbunea/improving-control-flow-in-code-using-functional-pipelines

Why?

You:

Why?

Me:

Because code is better...

You:

Code is better? How?

Me:

Because is it easier to: 

• read
• extend
• debug

You:

Prove it!

Me:

Ok let's look at the following problem:

In practice

We have to write an endpoint which returns an offer by its id:

GET /offer-by-id/:offer-id
Possible results:

- 200 {"offer-id":2, "offer-data":""}
- 400 {"errors":["The id you provided is invalid"}]}
- 404 {"errors":["The id you provided cannot be found"}]}
IMPORTANT: if an id is valid but not found the deposit must be notified!

Solution 1: IFs

The problem could be solved by:

Or in code:

Whenever we have ifs in code, it becomes dificult to read so maybe we could simplify it, making it more like:

step 1
then step 2
then step 3

Or more like

validate
then find offer
then jsonify

Hmm, can we?

Solution 2: Exceptions

We'll use exceptions to break out flow:



The solution above is very present in Java, even if the way the errors are caught might not be this explicit.

Because it's a fairly simple example we could also use the strategy pattern. Basically we use ifs to choose a strategy, then we execute it to give us the result. 

Solution 3: strategy pattern

Functional programming gives us sever possibilities using pipes. 

What is a pipe?

A pipe makes sure that steps get executed in a certain order and that the result of a step is passed to the next step. Like:

In our case we'd like something like:

validate(requestId)
.then(findOfferById)
.then(jsonify)

Solution 4 Functional either or railway oriented programming

This is a very common solution in typed functional languages such as F# or Haskel, but it's becoming very common in Java as well.

Having a single pipeline is very beautiful but how do we handle errors? By using two parallel pipes. A pipe for the happy path and a path for the errors. Basically all our functions can return either a SuccesfullResponse or a ErrorResponse. This will get passed on to the next function that will process it and return again either a SuccesfullResponse or a ErrorResponse.

In F# we'd have something like:

findOrderById: IResponse -> SuccesfullResponse | ErrorResponse

we might describe it like:

SuccesfullResponse | ErrorResponse aFunction(SuccesfullResponse | ErrorResponse response)

For the happy path we'd have:

And if we get a validation error in the first step we'll have:

And the code, will have a class for Success and a class for Error. Each will inherit an IResponse and will implement two functions receiving a function (lambda) then and fail. In Succes we'll return the result of applying the function on what we have on then and the data we have on fail, and on Error we'll do exactly the opposite. 

Imaging you'd replace then with map and fail with orElseGet, doesn't that sound like an optional?



In dynamic languages, there are other options. But first let's describe functional composition. 

Functional composition is when you combine 2 (or more) functions into one, then apply it. It's pretty much like pipe but you may do the composition at runtime. 

Now we can look at the two options. First is:

Solution 5: pipeline with flag

We'll pass through the pipe an object that contains a flag which tells you whether there were errors before. Basically using a value in your data instead of using the type of the data (has response property instead of type: Successful or Error).

In our case, the flag is whether a response has been set already:

if(state.response) return state;

Solution 6: pipeline + overflow pipeline 

The second option is to have a pipeline and an overflow pipeline.  

 We'll use exceptions once again to bypass the normal pipeline and go to the overflow pipeline.

Unlike the previous two options when an exception happend it will jump straight to the end, bypassing the next steps directly.



The code: https://gitlab.com/danbunea/improving-control-flow-in-code-using-functional-pipelines/tree/stage-1

Readability

In terms of readability it could be a lot easier, to see the code as a pipeline:

step 1
then step 2
then step 3

While also handling the errors:

step 1
then step 2
then step 3
fail on-error

or 

safe(
   step 1
   then step 2
   then step 3
)

Extensibility

But what about extensibility?

We now have to modify our endpoint

1. to check if the offers are still active. If the aren't we need to return an error
2. to update the number of times the offer has been accessed

GET /offer-by-id/:offer-id

Possible results:

- 200 {"offer-id":2, "offer-data":"", "active":true, "requests":1}
- 400 {"errors":["The id you provided is invalid"}]}
- 400 {"errors":["The offer expired"}]}
- 404 {"errors":["The id you provided cannot be found"}]}

IMPORTANT: if an id is valid but the offer expired the deposit must be notified!

We changed the tests:

Then we change the code and we can look at how
For solution 1:

The way we solved it was to add more ifs inside an existing if thus increasing the cyclomatic complexity of the solution making it even harder to read. And real life code tends to be more complex than this.


For solution 2:

We did:

• added a new exception
• changed the code inside the try catch block
• changed the code in the catch

For solution 4:

What did we do:

• added two new functions, completely independent 
• modified an existing one
• added steps to the pipeline

For solution 5:

What did we do:

• added a new function
• heavily modified an existing one
• added a step to the pipeline

For solution 6:

What did we do:

• added a two new functions
• added them as steps to the pipeline

No existing code modified! (except for the pipeline, which is expected)

The code: https://gitlab.com/danbunea/improving-control-flow-in-code-using-functional-pipelines/tree/stage-2

The merge request in gitlab: https://gitlab.com/danbunea/improving-control-flow-in-code-using-functional-pipelines/merge_requests/3/diffs#faff669626dfa73714964353a02a5101dce1b3a7

Debugging

Let's say we need to start logging what is going on. For ifs we may end up with:

Not exactly easy to know where to insert the logging. 
But for a pipeline, we just need to insert some tracing function between the steps:

giving us:

or

And the log is like:

Last, let's see where pipelines could be used.

Frontend/Javascript with Promises:

or front end Clojurescript:

or backend Python:

or Java pipelines

or

The example comes from the book "Functional Style" by our colleague Richard Wild  https://functional.works-hub.com/learn/the-functional-style-part-5-higher-order-functions-i-function-composition-and-the-monad-pattern-bc74a?utm_source=blog&utm_medium=blog&utm_campaign=j.kaplan

Conclusion

Typed languages (including Java) you should use Either.

Dynamic languages the best would be pipeline with exceptions.

Outside-In TDD the bank kata in Clojure/ClojureScript

Code: https://github.com/DanBunea/katas/tree/master/outside-in%20tdd%20bank%20kata 


In "London school" TDD or Outside-In we start from an acceptance test, then write unit tests. This is better explained as the double loop of TDD:




Some time ago, I found this great step by step explanation of Outside-In TDD, by Sandro Mancuso from Codurance:

However it was all Java and obviously OOP, so I wanted do the same thing but in Clojure, and fully functional: functions and not classes. So here we go:





Restrictions (adapted for functional):

1. Start with a module account that has the following functions:

(defn deposit [account amount] ...)

(defn withdraw [account amount] ...)

(defn print-statement [account] ...)

Threat the methods, as if they cannot return any values!

2. You're not allowed to add any new functions to the module
3. Strings and integeres are to be used for dates and amounts for simplicity
4. Don't worry about spacing in the statement printed at the console

Step 1: The acceptance test (the big loop)

we need to take this test, unit it fails for the right reason: the results of the console-print are not what was expected, so we write two modules:



and account.cljs:



and now the test:

Step 2: The first unit test - account module - the business layer (the small loop)

The very first module we should test is account, so we make account_tests. For account, we could test the deposit function. Considering we might have a module which will actually do the depositing/withdrawals in a database or something, that module will be separate and we'll call it transaction-repository. So we could test the interactions between modules like:



We expect that when we invoke deposit from the account module, the add-deposit from the transaction-repository will be invoked.



we make it pass,



and continue with withdraw and later with print-statement for which we'll do a separate module: statement-printer. The final test will be:



and:



and:

Step 3: the unit tests for the "data layer" (the small loop)

Like for the account, we start with a simple test, where we make a deposit and check if all-transactions will return it. We also need to make sure it's on a certain date. We make the test pass, and we move on to withdraw, ending up with:



and:



and:

Step 4: Unit testing the statement-printer (the small loop)

Now, we'll go straight to the results, even though I used TDD to get to it:

The tests:




The code obtained:



The tests result:



Let's not forget that our console-print function is still mocked, and the implementation is:



and we can now implement it:



Tests still pass.

Step 5: Going back to the acceptance test (the big loop)

We will modify the test, mocking the dates delivered, as well as console-print:




And when we run all the tests:

Conclusion

Starting from the outside, we could test-drive the design and the code.

acceptance-test -> 
      account-tests->account->
      transaction-respository-tests->transaction-repository->

      statement-printer-tests->statement-printer->console