A streaming library with a superpower: FS2 and functional programming

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Scala has a very special streaming library called FS2 (Functional Streams for Scala). This library embodies all the advantages of functional programming (FP). By understanding its design goals you will get exposure to the core ideas that make FP so appealing.

FS2 has one central type: Stream[Effect,Output]

You might get from this type that it’s a Stream and that it emits values of type Output.

The obvious question here is what is Effect? What is the link between Effect and Output? And what advantages does FS2 have over other streaming libraries?

Overview

I will start by reviewing what problems FS2 solves. Then I compare List and Stream with several code examples. After that, I will focus on how to use Stream with a DB or any other IO. That is where FS2 shines and where the Effecttype is used. Once you will understand what Effect is, the advantages of Functional Programming should be evident to you.

At the end of this post you will get the answers to the following questions:

  • What problems can I solve with FS2?
  • What can I do with Stream that List cannot ?
  • How can I feed data from an API/File/DB to Stream ?
  • What is this Effect type and how does it relate to functional programming?

Note: The code is in Scala and should be understandable even without prior knowledge of the syntax.

What problems can I solve with FS2?

  1. Streaming I/O: Loading incrementally big data sets that would not fit in memory and operating on them without blowing your heap.
  2. Control Flow (not covered): Moving data from one/several DBs/files/APIs to others in a nice declarative way.
  3. Concurrency (not covered): Run different streams in parallel and make them communicate together. For example loading data from multiple files and processing them concurrently as opposed to sequentially. You can do some advanced stuff here. Streams can communicate together during the processing stage and not only at the end.

List vs Stream

List is the most well known and used data structure. To get a feel for how it differs from an FS2 Stream, we will go through a few use cases. We will see how Stream can solve problems that List cannot.

Your data is too big and does not fit in memory

Let’s say you have a very big file (40GB) fahrenheit.txt. The file has a temperature on each line and you want to convert it to celsius.txt.

Loading a big file using List

import scala.io.Sourceval list = Source.fromFile("testdata/fahrenheit.txt").getLines.toList
java.lang.OutOfMemoryError: Java heap space
java.util.Arrays.copyOfRange(Arrays.java:3664)
java.lang.String.<init>(String.java:207)
java.io.BufferedReader.readLine(BufferedReader.java:356)
java.io.BufferedReader.readLine(BufferedReader.java:389)

List fails miserably because of course, the file is too big to fit in memory. If you are curious, you can go check the full solution using Stream — but do that later, read on :)

When List won’t do…Stream to the rescue!

Let’s say I succeeded in reading my file and I want to write it back. I would like to preserve the line structure. I need to insert a newline character \n after each temperature.

I can use the intersperse combinator to do that

import fs2._Stream(1,2,3,4).intersperse("\n").toList

Another nice one is zipWithNext

scala> Stream(1,2,3,4).zipWithNext.toList
res1: List[(Int, Option[Int])] = List((1,Some(2)), (2,Some(3)), (3,Some(4)), (4,None))

It bundles consecutive things together, very useful if you want to .

These are only a few from a lot of very useful ones, here is the .

Obviously Stream can do a lot of things that List cannot, but the best feature is coming in the next section, it's all about how to use Stream in the real world with DBs/Files/APIs ...

How can I feed data from an API/File/DB to Stream?

Let’s just say for now that this our program

scala> Stream(1,2,3)
res2: fs2.Stream[fs2.Pure,Int] = Stream(..)

What does this Pure mean? Here is the scaladoc from the source code:

/**
* Indicates that a stream evaluates no effects.
*
* A `Stream[Pure,O]` can be safely converted to a `Stream[F,O]` for all `F`.
*/
type Pure[A] <: Nothing

It means no effects, ok …, but What is an effect? and more specifically what is the effect of our program Stream(1,2,3)?

This program has literally no effect on the world. Its only effect will be to make your CPU work and consumes some power!! It does not affect the world around you.

By affecting the world I mean it consumes any meaningful resource like a file, a database, or it produces anything like a file, uploading some data somewhere, writing to your terminal, and so on.

How do I turn a Pure stream to something useful?

Let’s say I want to load user ids from a DB, I am given this function, it does a call to the DB and returns the userId as a Long.

import scala.concurrent.Futuredef loadUserIdByName(userName: String): Future[Long] = ???

It returns a which indicates that this call is asynchronous and the value will be available at some point in the future. It wraps the value returned by the DB.

I have this Pure stream.

scala> val names = Stream("bob", "alice", "joe")
names: fs2.Stream[fs2.Pure,String] = Stream(..)

How do I get a Stream of ids?

The naive approach would be to use the map function, it should run the function for each value in the Stream.

scala> names.map(loadUserIdByName)
res3: fs2.Stream[fs2.Pure,scala.concurrent.Future[Long]] = Stream(..)

I still got back a Pure! I gave the Stream a function that affects the world and I still got a Pure, not cool ... It would have been neat if FS2 would have detected automatically that the loadUserIdByName function has an effect on the world and returned me something that is NOT Pure but it does not work like that. You have to use a special combinator instead of map: you have to use evalMap.

scala> val userIdsFromDB = names.evalMap(loadUserIdByName)
userIdsFromDB: fs2.Stream[scala.concurrent.Future,Long] = Stream(..)

No more Pure! we got Future instead, yay! What just happened?

It took:

  • loadUserIdByName: Future[Long]
  • Stream[Pure, String]

And switched the types of the stream to

  • Stream[Future, Long]

It separated the Future and isolated it! The left side that was the Effect type parameter is now the concrete Future type.

Neat trick, but how does it help me?

You just witnessed true separation of concerns. You can continue to operate on the stream with all the nice List like combinators and you don't have to worry about if the DB is down, slow or all the stuff that is related to the network (effect) concerns.

It all works until I want to use toList to get the values back

scala> userIdsFromDB.toList
<console>:18: error: value toList is not a member of fs2.Stream[scala.concurrent.Future,Long]
userIdsFromDB.toList
^

What???!!! I could swear that I used toList before and it worked, how can it say that toList is not a member of fs2.Stream[Future,String] any more? It is as if this function was removed the moment I started using an effect-ful stream, that's impressive! But how do I get my values back?

scala> userIdsFromDB.compile
res5: fs2.Stream.ToEffect[scala.concurrent.Future,Long] = fs2.Stream$ToEffect@fc0f18da

First we use compile to tell the Stream to combine all the effects into one, effectively it folds all the calls to loadUserIdByName into one big Future. This is needed by the framework, and it will become apparent why this step is needed soon.

Now toList should work

scala> userIdsFromDB.compile.toList
<console>:18: error: could not find implicit value for parameter F: cats.effect.Sync[scala.concurrent.Future]
userIdsFromDB.compile.toList
^

What?! the compiler is still complaining. That’s because Future is not a good Effect type — it breaks the philosophy of separation of concerns as explained in the next very important section.

IMPORTANT: The ONE thing to take away from this post

A key point here, is that the DB has not been called at this point. Nothing happened really, the full program does not produce anything.

def loadUserIdByName(userName: String): Future[Long] = ???Stream("bob", "alice", "joe").evalMap(loadUserIdByName).compile

Separating program description from evaluation

Yes it might be surprising but the major theme in FP is separating the

  • Description of your program: a good example is the program we just wrote, it’s a pure description of the problem “I give you names and a DB, give me back IDs”

And the

  • Execution of your program: running the actual code and asking it to go to the DB

One more time our program has literally no effect on the world besides making your computer warm, exactly like our Pure stream.

Code that does not have an effect is called pure and that’s what all Functional Programming is about: writing programs with functions that are pure. Bravo, you now know what FP is all about.

Why would you want write code this way? Simple: to achieve separation of concerns between the IO parts and the rest of our code.

Now let’s fix our program and take care of this Future problem.

As we said Future is a bad Effect type, it goes against the separation of concerns principle. Indeed, Future is eager in Scala: the moment you create one it starts to executes on some thread, you don't have control of the execution and thus it breaks. FS2 is well aware of that and does not let you compile. To fix this we have to use a type called IO that wraps our bad Future.

That brings us to the last part, what is this IO type? and how do I finally get my list of usedIds back?

scala> import cats.effect.IO
import cats.effect.IO
scala> Stream("bob", "alice", "joe").evalMap(name => IO.fromFuture(IO(loadUserIdByName(name)))).compile.toList
res8: cats.effect.IO[List[Long]] = IO$2104439279

It now gives us back a List but still, we didn't get our IDs back, so one last thing must be missing.

What does IO really mean?

IO comes from . First let's finish our program and finally get out the ids back from the DB.

scala> userIds.compile.toList.unsafeRunSync
<console>:18: error: not found: value userIds
userIds.compile.toList.unsafeRunSync
^

The proof that it’s doing something is the fact that it’s failing.

loadUserIdByName(userName: String): Future[Long] = ???

When ??? is called you will get this exception, it means the function was executed (as opposed to before when we made the point that nothing was really happening). When we implement this function it will go to the DB and load the ids, and it will have an effect on the world (network/files system).

IO[Long] is a description of how to get a value of type Long and it most certainly involves doing some I/O i.e going to the network, loading a file,...

It’s the How and not the What. It describes how to get the value from the network. If you want to execute this description, you can use unsafeRunSync (or other functions prefixed unsafe). You can guess why they are called this way: indeed a call to a DB is inherently unsafe as it could fail if, for example, your Internet connection is out.

Recap

Let’s take a last look at Stream[Effect,Output].

Output is the type that the stream emits (could be a stream of String, Long or whatever type you defined).

Effect is the way (the recipe) to produce the Output (i.e go to the DB and give me an id of type Long).

It’s important to understand that if these types are separated to make things easier, breaking down a problem in subproblems allows you to reason about the subproblems independently. You can then solve them and combine their solutions.

The link between these 2 types is the following :

In order for the Stream to emit an element of type

  • Output

It needs to evaluate a type

  • Effect

A special type that encodes an effective action as a value of type IO, this IO value allows the separation of 2 concerns:

  • Description:IO is a simple immutable value, it’s a recipe to get a type A by doing some kind of IO(network/filesystem/…)
  • Execution: in order forIO to do something, you need to execute/run it using io.unsafeRunSync

Putting it all together

Stream[IO,Long] says:

This is a Stream that emits values of type Long and in order to do so, it needs to run an effective function that producesIO[Long] for each value.

That’s a lot of details packed in this very short type. The more details you get about how things happen the fewer errors you make.

Takeaways

  • Stream is a super charged version of List
  • Stream(1,2,3) is of type Stream[Pure, Int] , the second type Int is the type of all values that this stream will emit
  • Pure means no effect on the world. It just makes your CPU work and consumes some power, but besides that it does not affect the world around you.
  • Use evalMap instead of map when you want to apply a function that has an effect like loadUserIdByName to a Stream.
  • Stream[IO, Long] separates the concerns of What and How by letting you work only with the values and not worrying about how to get them (loading from the db).
  • Separating program description from evaluation is a key aspect of FP.
  • All the programs you write with Stream will do nothing until you use unsafeRunSync. Before that your code is effectively pure.
  • IO[Long] is an effect type that tells you: you will get Long values from IO (could be a file, the network, the console ...). It's a description and not a wrapper!r
  • Future does not abide by this philosophy and thus is not compatible with FS2, you have to use IO type instead.

FS2 videos

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Thanks to Gilad Foyer, Guy Perkal, Gal Abrass, Dor Sever, and Ben Maraney.

Daniel Sebban

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Scala/Akka/Functional Developer https://twitter.com/dsebban

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