Freer Monads, More Better Programs

freer-monads, extensible-effects

If you consider yourself a Haskell beginner, this post is not aimed at you! You’re going to want to understand DataKinds and RankNTypes in order to get things done. Feel free to read anyway, but keep in mind that the technical solutions described here are tricky.

Every two weeks in the functional programming slack, we get into a big argument about “the right way to structure programs.” The debate goes around and around in circles; names get called; feelings get hurt. We never get anywhere, and the whole process starts again 14 days later. Frankly, it’s exhausting.

As best I can tell, the community roughly fragments itself along four lines—those who like mtl, those who say “just do everything in Reader IO”, those who like the three layer cake, and those who think free(r) monads are worth their weight in gold.

Being in the latter camp is frustrating, because everyone has strongly negative opinions on freer monads, and as best I can tell, nobody seems to have ever actually used them.

As one of the few people in the Haskell-sphere who has actually used freer monads in both anger and production, I wanted to set the record straight. So today, let’s talk about freer monads—what they are, what they buy you, how they work, and what wide-scale adoption could buy us. Yes, I’ll also talk about what their weaknesses are.

Criminally Misunderstood

Freer monads are amazingly powerful. Much more so than I think anyone realizes—including many of the people who maintain the libraries. There’s a lot of free, super-common, crazy-generic functionality that exists, but isn’t anywhere useful.

Freer monads are so much more than just a different way of expressing monad transformers. They’re a completely orthogonal means of composition that doesn’t really exist anywhere else in the Haskell ecosystem. By not using them, you are condemning yourself to writing a significant amount more of significantly more complicated code than you need to be.

Traditional monad stacks can be understood as “a small, established toolbox for side effects.” You say “I want some state, so I will add a StateT transformer,” with the understanding that this is isomorphic to s -> (s, a). That means it only ever does local state.

I’d suggest we instead think about freer monads as “implementation mix-ins,” or equivalently, as “compiler passes.” Code written against freer monads is exceptionally high-level and doesn’t mix concerns. It’s all “business logic”, where the implementation details are handled in layers, each one performed by a pass that simplifies the representation.

These passes are type-safe, independent and composable. You can mix-and-match which ones you want—which means testing is often just swapping in a test pass somewhere near the bottom of the stack. By mocking out different layers, it’s easy to get 100% test coverage, without ever needing to write full-scale integration tests.

The beauty of this system is that the testability itself also composes. If your program runs properly under the test pass, and you can prove that both your test and real pass are also correct, this correctness composes to the entire program.

Having an exceptionally high-level representation of your program’s intent is valuable in another way. Let’s take a State capability as an example. This thing might correspond to local state, or a database, or maybe even just GET/POST HTTP requests. Who knows? But also, who cares?

Most of the people reading the code, most of the time, don’t actually care what are the semantics behind the state. Those semantics are implementation details, interesting only to the people who care about the implementation. If you’re tracing a program flow, and aren’t interested in the database side of things, it’s a lot nicer to not need to wade through a bunch of irrelevant database code.

In short, freer monads let you separate the high-level “what am I trying to do” from the low-level “how to actually do it.”

Understanding Freer Monads

The Eff monad is parameterized by a type-level list of effects (or capabilities as I will also call them.) This list is kept polymorphic, and constraints are enforced on it to ensure that certain effects are available.

For example, the type StateT String (ReaderT Int IO) Bool is analogous to Eff '[State String, Reader Int, IO] Bool.

However, the type (MonadState String m, MonadReader Int m, MonadIO m) => m Bool in the mtl style also has an analogue: (Member (State String) r, Member (Reader Int) r, Member IO r) => Eff r Bool.

Freer monads are extensible in their effects—that means you can write your own, and use them completely interchangeably with existing effects. It’s trivial to write a new effect, as they’re just GADTs:

This is all it takes to define a new effect. We now have a Teletype effect, and we can use it after a small amount of (freely derivable) boilerplate:

Notice that the a in Teletype a describes the type you get back from calling this operation.

Our new Teletype effect corresponds to a domain specific language that can talk about reading and writing lines on a teletype. It’s important to keep in mind that there is no meaning associated with this effect. We get no semantics, other than an implicit, unverified guarantee that “it probably does what you expect.”

However, this lack of pre-established semantics is a feature, rather than a bug. The semantics are given after the fact by interpretations of the effects. One interpretation of Teletype might be to perform it in IO, interacting directly with the console. Another might be in the form of POSTing putLines to an HTTP server, and returning the results of a GET for getLine. Another could just do everything purely in memory.

Freer monads are extensible not only in their effects, but also in their interpretations. You can give new interpretations for existing effects, and for your own.

freer-simple offers several combinators for constructing new effects, which we’ll explore in the example below.

Solving Problems with Freer Monads

Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations.

– Melvin Conway

Freer monads are a data representation of your program, which then gets interpreted at finer-and-finer grained resolution until it’s just code.

In other words, they enforce a clean boundary between “saying what you mean” and “saying how to do it.” They let you focus on writing business logic, and relegate the implementation details to library code.

They give you composable, plug-and-play functionality for transforming a high-level business logic spec into an actual implementation.

As an example of how this works on a real-life application, let’s write a program that fetches a CSV file from FTP, decrypts it, streams its contents to an external pipeline, and tracks its stats in Redis.

Done!1 Pretty easy, hey?

“Now hold on a minute, Sandy! That program you wrote doesn’t do what you said! It doesn’t fetch files from FTP, it doesn’t decrypt them, and it doesn’t do anything with Redis.”

That’s right. It doesn’t. What this does is exactly what the business people say they want—it moves data from one place to somewhere else, and lets you know about it. The rest are implementation details, and aren’t relevant to anyone except the particular engineers responsible for this piece of the system. Engineers on other teams in your organization probably don’t even care about the implementation details.

Written like this it’s easy for people to get a sense of what you’re trying to accomplish, without needing to know the nitty-gritty details connection management, credential management, performance enhancements, error handling, or database details. It concisely describes the goal, and leaves the irrelevant bits out of sight and out of mind.

Of course; not everyone wants this high-level picture. The people responsible for this service really and truly do care about how the thing actually works. At least, at some level of abstraction. The people whose job it is to ingest data probably care about the service’s performance and error handling, but likely don’t have strong opinions on the semantics of fetching data, the encryption schemes used, the database layout or the choice of the external streaming pipeline. They probably don’t even care that they’re ingesting CSV files—they’d just as happily consume real-time JSON requests.

The goal is to make it easy for people to analyze the pieces they understand and are responsible for, and hide the noise of the underlying details to someone else.

So, how to do we get from our high-level description to a real program? We transform it into a slightly less-high-level program. For example, in order to get our Input of Records, we do actually need to parse a CSV file. You’ll notice that such a problem really doesn’t care where the file comes from; it just wants something to read.

So we write an interpretation of Input Record in terms of CSV files. This suggests we have some sort of FileProvider capability, whose job it is to actually get use the file in question.

csvInput takes a file name, reads that file in terms of some abstract FileProvider capability, and then returns one row of the result every time nextInput is called in the higher-level application.

This function is implemented in terms of the handleRelayS combinator. You can think of handleRelayS as being parameterized on what return and (>>=) mean for the effect being interpreted. In addition, it allows you to thread a piece of state between binds and the final return.

Our implementation of bind is to return a new row of the CSV file for every subsequent call to nextInput in the original program. We accomplish this by returning the head of the list of rows, and then passing the tail as the next piece of state.

In effect, we’ve described what it means to have an Input capability in terms of what it means to have a FileProvider capability. Notice that this isn’t the only interpretation of Input—it could just as well be implemented by reading from a streaming source, or by always giving back the same result, or by cycling through a static list.

The point is that the people writing the service don’t care where this data is coming from. All they care is that they can read it and pipe it to the right place. In fact, they might want to test that their service works by calling it on a constant stream of data—so instead they can interpret it purely:

(for bonus points, you can implement csvInput in terms of pureInput)

Ok, great! The next step towards a working program is to give an interpretation of a FileProvider. We’ll write two—one in terms of a lower level FTP capability, and one in terms of regular everyday IO:

Often you just want to reinterpret an effect in terms of some other effect you already have (here, FTP and IO, respectively). In this case, it’s sufficient to just use the interpret combinator, which takes implements your interpretation via a natural transformation (something of the form forall x. effect x -> Eff r x.)

For testing, you might also want a mock filesystem—pureFileProvider :: Map FilePath ByteString -> _.

Our program can now provide provide an Input capability via a FileProvider capability, via IO directly or via an FTP capability. You get the picture.

Something we haven’t yet handled is file decryption. It’s worth noting that this concern is largely orthogonal to FileProviders; we’d like to be able to mix-in the capability to deal with encrypted files regardless of what the actual mechanism for files looks like.

For that, we’re exposed to yet another combinator for writing interpretations; interpose. This combinator allows us to interpret a capability in terms of itself. Which means, we can intercept calls to a capability without necessarily handling them. Providing decrypted files is a good use case for this—we can intercept requests for files, and silently decrypt them before giving them back.

We’ve gained the ability to inject logic around other interpretations!

Assuming we have an FTP implementation, the Input side of the coin is done. Now to deal with the Outputs of our ingestion program. Remember, we want to put our records into some external streaming service. We can naively provide an interpreter that POSTs these records against our service.

Assuming we have another interpretation HTTP ~> IO, we’re now good to go!

This works, but accounting comes back a few days later and complains—our streaming bill is suddenly really big. Apparently we pay per API call. Uh oh. The good news is that the API can handle up to 500 records per POST. So, we can just write another interpret that batches writes before posting them.

Cool. Now sticking a batch 500 pass before postOutput will batch all of our transactions sent to the API. Again, our business-logic doesn’t change, because it need to care about this implementation detail.

We could continue on, but at this point you’ve seen most of the machinery freer monads give us. At the end of the day, main will end up looking like this:

It composes nicely, and the compiler will yell at you if you forget to handle any of required capabilities.

Behavior can be mixed in at will; some other common things you might want include retry-with-backoff, service discovery, chaos-injection, etc.

Over time and scale, you’ll realize that most of your application code is the same crap over and over again—read configuration, connect to a database, deal with retry, shuffle data from one buffer to another. It’s often hard to see this when it’s written with a traditional monad stack, because traditional monad stacks don’t give you the tools to abstract it away.

As you get into the habit of building new effects and interpretations for those effects, you’ll see that new applications are often ready to ship after 25 lines of business logic and another 25 lines of choosing the right interpretations for it.

Bad Arguments Against Freer Monads

There are several arguments against freer monad, some of which are good, but most of which are terrible.

Free Monads Have Bad Performance

Free monads suffer from \(O(n^2)\) complexity when used naively, which is unfortunately what you get by default. Freer monads are optimized via a queue which provides constant-time construction of the default case.

Yes, freer monads are today somewhere around 30x slower than the equivalent mtl code. That’s roughly on par with Python, but be honest, you’ve deployed Python services in the past and they were fast enough. And besides, the network speed already dominates your performance—you’re IO-bound anyway.

If you are writing real-time services maybe this will be an issue, but you’re probably not. And if you are, optimizing Haskell is likely a skill you already have.

A subtle point to notice is that it’s the monadic bits of the code that are 30x slower. Not “your program is 30x slower if you import Control.Monad.Freer”—but simply that you will spend more time in binds than you would in another monad. But your program isn’t only monadic in Eff; it also needs to compute expressions and wait for IO and all of that stuff.

If it makes you feel better, I recently got a 15% performance increase by just more aggressively inlining some of the combinators. This suggests there’s a lot of low-hanging optimization wins for anyone willing to go through the work to pluck it.

In short: worry about writing good code first, and deal with performance if it becomes an issue.

Purescript Abandoned Eff

Purescript had a thing called Eff, but it was not the same as this. From the purescript-eff readme:

As of PureScript 0.12 the default type for handling effects is Effect from purescript-effect. This differs from Eff by removing the row of effect types. This decision was made as getting the effect rows to line up was sometimes quite tricky, without providing a great deal of benefit.

There is also purescript-run now, which uses a similar effect row mechanic but provides true algebraic effect handling. [emphasis mine]

The Eff described in this document is equivalent to purescript-run.

Reasonably Good Arguments Against Freer Monads

ContT is Not an Algebraic Effect

I never really understood this one as stated—I’ve never actually used ContT in a real monad stack. Have you?

But the sentiment behind this argument is better stated in human as “Eff is unable to model resource bracketing.” Which is to say, it’s hard to make sure an Eff program calls all of its finalizers.

The good news is that there’s a solution if your allocation and cleanup code only requires IO—you can just interpret your entire Eff monad directly into ResourceT:

Specialize bracket with your own first two parameters to taste.

More annoyingly, the lack of ContT-support means that it’s hard to write effects that imply asynchronicity. That’s not to say it’s impossible, merely that it doesn’t compose in the same nice way that synchronous effects do.

This is bad, but not disastrously so. You can spin up a thread pool elsewhere, and add a capability that sends effects to it:

Changing the interface to fill an MVar upon completion of the task and make it available to the original Eff program is an exercise left to the reader.

The Error Messages Are Bad / It’s Too Complicated

This has historically been true. While freer-simple makes the situation significantly better, there is definitely room for improvement on this front.

First things first, Eff eschews the functional dependencies that mtl has. This means you can have multiple Writer effects in the same stack in Eff (but not in mtl) at the cost of type-inference.

This is both a feature, and, I won’t lie to you, amazingly annoying at times. It’s a feature because lots of things are just Writer effects. It’s annoying as heck because polymorphism makes it eat shit.

For example, consider the following innocuous looking program:

Seems fine, right? Wrong. Because 15 is actually fromInteger 15 :: Num a => a, this program will complain about not having a Writer a capability. You as a human know what should happen here, but the compiler is stupid.

Thankfully the solution is simple, but it requires knowing what’s wrong and how to fix it.

If you’re going to be doing a lot of work with polymorphic effects, a low-energy solution is to just provide a locally-bound monomorphic type:

All of this is much less user-friendly than it should be. However, in my experience, people quickly learn how to debug problems like this. It was enough to have an “Eff mentor” on our team, whose job it was was to promptly reply to “I don’t know why this doesn’t work.”

Jesus Help Me There Are A Lot of Unmaintained Free(r) Monad Packages

Tell me about it. Even as someone who is keenly interested in this stuff I have a hard time keeping up with the situation.

Here’s the skinny—I’d strongly recommend freer-simple. Failing that, if you really, really, really need the performance, take a look at fused-effects.

Ignore the other ones.2

Conclusion

Freer monads are fucking sick and you’d be foolish to not at least consider them for your next project.

Furthermore, if you’re going to continue insisting on saying that $technology is better, I strongly encourage you to write up a similar argument stating your case. My mind is open on this; if you make a strong argument, I’m more than happy to denounce this article and jump on the $technology train too.

It’s worth keeping in mind that despite our small differences; we’re all on the same team here. We all love functional programming and want to do our best to make it more popular. As best I can tell, the best strategy towards that aim is to come up with a consensus on how to do things, and to stop the needless infighting.

One love.


  1. Input and Output are called Reader and Writer respectively in freer-simple. I decided to not use this terminology in order to prevent people from thinking that these are the same monads they’re used to.

  2. If you’re the maintainer of another effects package and want me to include it here, shoot me an email and make an argument!

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