Usage examples and benchmarks between Scalaz and Cats (w/ Haskell ground-truth).
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README.md

ScalaZ and Cats

Preface

ScalaZ and Cats are libraries which provide Functional Programming constructs for Scala (i.e. Monad n' friends).

This repository is a comparison of these two libraries by someone who isn't predisposed to either one. If you're a contributor to either library and notice a discrepancy here, please let me know!

We seek to answer the following question:

Should I use ScalaZ or Cats?

The answer is, of course, "it depends". What's your use-case?

Use Cases

I want to train my Scala team in Functional Programming fundamentals

Functional Programming in Scala, by Runar Bjarnson and Paul Chiusano, is a valuable book for learning about Functional Programming in general. Otherwise, both ScalaZ and Cats have specific books and professional training available.

ScalaZ:

Cats:

I'm writing a performance-sensitive application

If microbenchmarks and object allocations matter, lean toward Cats, it tends to be faster in aggregate for strict calculations.

If your project has expensive chunks of work that you wish to avoid evaluating unless absolutely needed, lean towards ScalaZ with its preference for lazy evaluation.

I want to improve quality-of-life for my Scala devs

Any dedicated application of FP concepts will help you organize and simplify your code. Libraries like simulacrum, decline and circe can provide immediate wins by drastically cutting down boilerplate. The latter two can be used natively with Cats, or via ScalaZ with the shims library.

I'm writing a library/application that works with a complex DSL

You probably need Recursion Schemes, which are supplied by the Matryoshka library in ScalaZ-land.

I want to port a well-known, general-purpose Haskell library to Scala

You'd be a champ to write a backend for both ScalaZ and Cats, but know that Cats has a head start and has a nice set of ported libraries already.

I care about licensing

ScalaZ is BSD3, while Cats is MIT + BSD3, since it derived from ScalaZ originally.

I care about which stays truer to Haskell

ScalaZ does. Its core has a larger API, provides more features up-front, and tends to keep Haskell function names and operators (e.g. <*>).

I care about which has more industry backing

According to this survey, ScalaZ does.

I chain operations with for / yield, isn't that all I need?

Vanilla for has funny GOTCHAs that can affect both performance and usability.

Luckily, the better-monadic-for plugin can help. It desugars for blocks into saner use of .flatMap and .map to avoid redundant calls, type casts, and object allocations.

I hear the IO Monad can help me logically organize my code

Both ScalaZ 7 and Cats have a effects subpackage which provides an IO type. They both help you contain "real world" side-effects into smaller areas of your code base, freeing the rest of it to purity (referential transparency). They also help you wrangle IO-based Exceptions.

A recent (2018 April) development is that of scalaz-ioeffect, a backport of the IO Monad implementation from ScalaZ 8. This offers a 2 order-of-magnitude performance improvement over scalaz-effect, which puts it about 20% faster than IO from Cats, and around 50x faster than Future from vanilla Scala.

Futures suck and I hate JVM thread pools. Help?

The IO Monad can help you, my friend. First, tell me why Future is in your life:

  • I'm using Actors. Are you sure you need to be? Actors are very pervasive: once code is "Actory" it's hard to reverse that. Are you sure you don't just need simple concurrency (see below)?
  • I'm using a database library that returns Futures. Slick, maybe? Consider doobie instead, which returns in IO.
  • I'm using a webserver whose endpoints need Futures. akka-http? Play? Consider http4s, which is built on fs2 and runs in the IO Monad of your choice.
  • I'm doing some simple concurrency work. IO types come with a friend, Fiber, that allows you to logically and safely model concurrent operations. The result of all operations in Fiber must end in IO, so concurrent effects can never "escape" into pure code. Bonus: Fiber s aren't fixed to JVM threads - they yield intelligently to each other, so you can have as many as you want. You also don't need to worry about ExecutionContext.

Future does not have your best interests at heart. The fundamental difference between it and IO is this: IO is a description of a runnable program which can be composed with other programs (other IO). Future is a running operation. As soon as you have:

// Fetch Foo from the DB
val fut: Future[Foo] = ...

fut is running, and you need to keep track of that in your head. This is not the case for IO, which makes it much easier to reason about program behaviour in general.

Just gimme Monads

Then either is fine, you can flip a coin.

I'm interested in other FP options on the JVM

If you're not already entrenched in Scala, then you're in luck. Eta is a Haskell dialect that targets the JVM. It can access a large portion of the existing Haskell library ecosystem, and also has a Java FFI that handles the possibility of null more explicitely than Scala.

An example:

-- | Type-safe import of a Java method that is null-safe.
foreign import java unsafe "@static java.lang.System.getenv"
  getEnv :: String -> IO (Maybe String)

-- | Checks the environment for the HOME environment
-- variable and prints it out if it exists.
main :: IO ()
main = do
  home <- getEnv "HOME"
  case home of
    Just homePath ->
      putStrLn $ "Your home directory is " ++ homePath ++ "!"
    Nothing ->
      putStrLn "Your HOME environment variable is not set"

Things like typeclasses and the IO Monad are first-class concepts, so no extra library like ScalaZ or Cats is necessary. Eta supports unsigned integer types (called Word in Eta/Haskell and sometimes uint elsewhere) which neither Java nor Scala have natively. Eta also has bindings to Apache Spark.

If you're already in Scala-land but want to integrate Eta or gradually migrate to it, there exists an sbt plugin for Eta<->Scala integration.

Benchmarks

Benchmarks were performed using the JMH plugin for SBT. Vanilla Scala and Haskell results are also included where applicable.

Results

All times are in nanoseconds, lower numbers are better.

Kittens and scalaz-deriving were used to derive Eq instances.

  • scalaz-deriving v0.13.1
  • kittens 1.0.0
  • scalaz-ioeffect-2.3.0 (ScalaZ 8 IO backport)
  • cats-effect-1.0.0-RC2
Benchmark ScalaZ 7.2.24 Cats 1.1.0 Vanilla Scala Haskell 8.2.2
Eq - same [Int] 10.4* 2.5 2.4 3,974
Eq - different [Int] 5,792 3,983 5,180
Eq - while w/ Int 3,188 199 198
Eq (derived) - same [Foo] 10.2 2.7 2.5
Eq (derived) - different [Foo] 2,941 45,416 2,071
Eq (derived) - while w/ Foo 463,595 45,652 5,335
Eq (hand-written) - same [Foo] 10.1 2.8 2.5
Eq (hand-written) - different [Foo] 2,962 7,835 2,071
Eq (hand-written) - while w/ Foo 8,980 5,341 5,335
Show - [Int] 571,753 45,006 41,079 38,190
Show - String 2,841* 3.2 2.8 140,000
Foldable.fold on [Int] 3,448 5,026 7,939 3,330
Foldable.fold on [Maybe Int] 6,430 12,506 14,260
State - get 18.6 30.6 3.9
State - >>= 90.1 139.1 10.43
State - flatMap 64.5 146.6
State - countdown 8,753,951 6,069
StateT - countdown 4,387,924 9,744,808 15.4
Applicative - sum (<*>) 31,429 32,132 22,140
Applicative - sum (cartesian) 54,774 33,638
IO - Deep flatMap - 1000 8,869 14,559 506,433* 616.8
IO - Deep flatMap - 10000 88,675 147,758 4,859,057 6,021
IO - Deep flatMap - 100000 896,186 1,305,728 46,518,625 59,670
IO - Deep flatMap w/ error ADT - 1k 10,843 49,625* 626
IO - Deep flatMap w/ error ADT - 10k 97,106 487,752 6,058
IO - Deep flatMap w/ error ADT - 100k 1,100,008 4,770,665 60,270
IO - Deep flatMap w/ Exception - 1k 12,747 12,887 479,240 1,147
IO - Deep flatMap w/ Exception - 10k 103,312 102,690 4,965,881 11,050
IO - Deep flatMap w/ Exception - 100k 1,079,179 1,004,176 45,739,491 109,600

Notes:

  • Eq benchmarks for ScalaZ employ its IList type, not vanilla List
  • Show for ScalaZ and Cats behaves differently. ScalaZ's prefixes and affixes quotation marks, so that Strings can be copy-pasted between editor and REPL. This is what Haskell's Show does as well. Cats does not do this, so it can "return early" in the case of String.
  • IO benchmarks for Vanilla Scala are usage of Future.
  • The error ADT benchmarks for Cats and Haskell use EitherT[IO, E, A], while ScalaZ IO is a bifunctor with explicit error type: IO[E, A]. See the Features section for more information.

Observations

  • Type-safe equality checking is on-par or faster than Vanilla Scala. So, there seems to be no reason not to use Eq.=== in all cases.
  • Avoid Future from Vanilla Scala. Other than being less safe and harder to reason about, its performance is the worst of the four by far.
  • Except for a few outliers, performance of the two libraries is within the same ballpark.
  • One should favour hand-written typeclass instances for Cats, while deriving seems reliable for ScalaZ.
  • Neither library performs well on recursive Monadic operations (State especially). Haskell is two to three orders of magnitude faster in this regard. In particular, GHC heavily optimizes both IO and State operations.
  • As of 2018 April, both ScalaZ and Cats have fastly improved the performance of their IO Monad. This bodes well for Scala-based webservers like http4s.

Usage Considerations

API Accessibility

Up front, Cats has much more documentation and usage examples. Their website is good for this. However, given that they both have blog posts and books written about them, overall the availability of resources should be about equal between the two libraries.

The Cats import story is consistent - for most tasks you only need:

import cats._            /* To refer to top-level symbols like Monad */
import cats.implicits._  /* To get typeclass instances and operators */

ScalaZ has a bit more flexibility with their imports, but honestly you can just avoid that and do:

import scalaz._
import Scalaz._

and you'll get all data types, typeclasses, instances, and operators. If you're willing to do that, then the import experience for both libraries is the same.

Features

ScalaZ: IList

From its Scaladocs:

Safe, invariant alternative to stdlib List. Most methods on List have a sensible equivalent here, either on the IList interface itself or via typeclass instances (which are the same as those defined for stdlib List). All methods are total and stack-safe.

Between being invariant and avoiding connection to Scala's enormous Collections API, IList manages to be the fastest general-purpose Scala container type to iterate over. Specifically, it handles tail-recursive algorithms with pattern matching (thus mimicking .map and .foldLeft) twice as fast as vanilla List. Only an Array of Int or Double via a while loop can iterate faster.

ScalaZ: Maybe

From its Scaladocs:

Maybe[A] is isomorphic to Option[A], however there are some differences between the two. Maybe is invariant in A while Option is covariant. Maybe[A] does not expose an unsafe get operation to access the underlying A value (that may not exist) like Option[A] does. Maybe[A] does not come with an implicit conversion to Iterable[A] (a trait with over a dozen super types).

The implication is that Maybe should be safer and slightly more performant than Option. Ironically, many ScalaZ methods that yield an "optional" value use Option and not Maybe.

Where Monad Transformers are concerned, ScalaZ provides both MaybeT and OptionT.

ScalaZ: EphemeralStream

From its Scaladocs:

Like scala.collection.immutable.Stream, but doesn't save computed values. As such, it can be used to represent similar things, but without the space leak problem frequently encountered using that type.

The dream of lazy Haskell lists realized? Maybe. With EphemeralStream (or EStream as the cool kids call it), even the "head" value is lazy. So one would use EStream when there's no guarantee that even the first value might be used.

How does it perform?

All times are in microseconds.

Benchmark List IList Vector Array Stream EphemeralStream Iterator
foldLeft 33.3 31.3 68.9 56.4 56.9 163.1 55.4
foldRight 69.2 89.5 228.39 55.1 Stack Overflow Stack Overflow 147.6
Tail Recursion 45.9 24.1 69.8

We see similar slowdowns for chained higher-order ops as well. Looks like building in the laziness has its cost.

ScalaZ: Bifunctor IO[E, A]

Thanks to the backport library scalaz-ioeffect, ScalaZ 7 IO is now a bifunctor: IO[E, A]. Any possible error is explicit in the type signature. Typically this will be:

  • Exception or Throwable for Java-like exceptions
  • Void for when an error is provably impossible
  • Some custom error ADT unique to your application

IO-as-a-bifunctor is a living experiment that offers semantics not yet available in Cats or even Haskell's IO. The closest approximation is a Cats/Haskell EitherT[IO, E, A], which, having two modes of error reporting has been found over time to not be ideal. In the case of Scala, this EitherT wrapping incurs a 4x slowdown.

Typeclasses

Typeclasses are a powerful programming construct to relate data types that have common behaviour. They describe how a type should behave, as opposed to what a data type is (re: Object Oriented programming).

Both ScalaZ and Cats provide the "standard" typeclasses, namely Monoid, Functor, Applicative, and Monad, as well as a wealth of others for more specialized work. In general, the ScalaZ typeclass hierarchy is larger than the Cats' one.

Custom Typeclasses

Scala doesn't yet have first-class support for typeclasses. While it's very possible to create trait/object structures that represent a typeclass, there is no built-in syntax for it. The library simulacrum helps greatly with this:

package mylib

import simulacrum._

@typeclass trait Semigroup[A] {
  @op("<>") def combine(x: A, y: A): A
}

This significantly reduces boilerplate. At compile time, this tiny definition is expanded into everything necessary to use .combine (or its optional operator <>!) as an injected method on your A type. Here's how to write an instance:

case class Pair(n: Int, m: Int)

object Pair {
  implicit val pairSemi: Semigroup[Pair] = new Semigroup[Pair] {
    def combine(x: Pair, y: Pair): Pair = Pair(x.n + y.n, x.m + y.m)
  }
}

This way, whenever Pair is in scope, its Semigroup instance will also be automatically visible. Defining the Semigroup[Pair] somewhere else makes it an Orphan Instance, which runs the risk of burdening your users with confusing imports.

Now extend some top-level package object of yours like:

package object mylib extends Semigroup.ToSemigroupOps

And then full use of your typeclass is just one import away!

import mylib._

scala> Pair(1, 2) <> Pair(3, 4)
res0: Pair = Pair(4, 6)

Instance Derivation

In Haskell, automatic typeclass instance derivation is frequent:

-- The usuals - many more can be derived.
data User = User { age  :: Int
                 , name :: Text
                 } deriving (Eq, Ord, Show, NFData, Generic, ToJSON, FromJSON)

Fortunately, both ScalaZ and Cats provide a similar mechanism. Nobody wants to write boilerplate!

scalaz-deriving exposes the @deriving macro for ScalaZ typeclasses:

@deriving(Equal, Show, Encoder, Decoder)
case class User(age: Int, name: String)

Where Encoder and Decoder are from play.json.

Kittens provides shapeless-based "semi-auto" derivation for Cats:

case class User(age: Int, name: String)

object User {
  implicit val userEq: Eq[User] = cats.derive.eq[User]
  implicit val userShow: Show[User] = cats.derive.show[User]
}

Which requires more typing, but has more features, like auto-derivation of higher-kinded things like Functor.

For Circe Encoder and Decoder instances specifically, the following was already possible:

import io.circe.generic.JsonCodec

@JsonCodec
case class User(age: Int, name: String)

Caveat

With the current form of the Scala language and compiler, typeclasses have limitations in both performance and correctness. The details are described in the recent paper The Limitations of Type Classes as Subtyped Implicits, by Adelbert Chang.

If this concerns you, there are safer options for FP on the JVM.

Monadic Recursion

If you're not careful, Monadic Recursion with ScalaZ can blow the JVM stack. For instance, the following will "just work" with Cats:

def countdown: State[Int, Int] = State.get.flatMap { n =>
  if (n <= 0) State.pure(n) else State.set(n - 1) *> countdown
}

Which in ScalaZ would blow the stack for n greater than a few thousand. The proper ScalaZ equivalent is:

def trampolineCountdown: StateT[Trampoline, Int, Int] = State.get.lift[Trampoline].flatMap{ n =>
  if (n <= 0) StateT(_ => Trampoline.done((n,n)))
  else State.put(n - 1).lift[Trampoline] >> trampolineCountdown
}

Trampoline seems like an implementation detail, but it's exposed to the user here.

A quote from Cats:

Because monadic recursion is so common in functional programming but is not stack safe on the JVM, Cats has chosen to require tailRecM of all monad implementations as opposed to just a subset.

So tailRecM gets us stack safety - if you can figure out how to implement it correctly. I tried for Tree and was not successful.

John de Goes on ScalaZ 8:

tailRecM will not be a function on Monad, because not all monads can implement it in constant stack space.

So ScalaZ chooses lawfulness over convenience in this case.

Library Health and Ecosystems

Project Pulses

As of 2017 November 6.

Project Releases Watchers Stars Forks Commits Prev. Month Commits ScalaJS Scala Native
ScalaZ 106 257 3312 534 6101 45 Yes Yes
Cats 22 174 2118 493 3280 51 Yes No

ScalaZ's numbers are higher, but that's to be expected as it's an older project. Otherwise the projects seem to be about equally active. Notably missing is the lack of Scala Native support in Cats.

Sub-libraries

The diagram below looks one-sided, but must be taken with a grain of salt. As projects, Cats and ScalaZ have different aims. Cats has a small, tight core and espouses modularity. ScalaZ frames itself as a batteries-included standard library for FP in Scala. ScalaZ certainly has a larger and more featureful API than Cats at current. This will be increasingly true for the up-coming ScalaZ 8, which aims to provide the equivalent functionality of Dogs, Monocle, and Matryoshka directly. It also plans to provide low-level concurrency primitives which see no analogue in Cats or Vanilla Scala.

That in mind, here is a simplified view of their library ecosystems:

img

Notes:

  • Origami is a port of Haskell's foldl library
  • Atto is a port of Haskell's attoparsec library
  • Decline and optparse-applicative are ports of Haskell's optparse-applicative library
  • Refined is a port of Haskell's refined library
  • Monocle is a port of Haskell's lens library

Shims

Libraries like circe, atto and decline are immense standard-of-living improvements for Scala developers. Luckily, the shims library allows us to use them via ScalaZ, too. Likewise, Matryoshka becomes usable via Cats. From the shims project:

Shims aims to provide a convenient, bidirectional, and transparent set of conversions between scalaz and cats, covering typeclasses (e.g. Monad) and data types (e.g. \/). By that I mean, with shims, anything that has a cats.Functor instance also has a scalaz.Functor instance, and vice versa.

Here is a working example:

package shimmy

import scalaz._
import Scalaz._
import shims._
import com.monovore.decline._  /* Depends on Cats */

object Shimmy extends CommandApp(
  name = "shimmy",
  header = "Demonstrate how shims works.",
  main = {
    /* These are `decline` data types with `Applicative` instances from Cats */
    val foo = Opts.option[String]("foo", help = "Foo")
    val bar = Opts.option[Int]("bar", help = "Bar")
    val baz = Opts.flag("baz", help = "Baz").orFalse

    /* These are ScalaZ operators that use ScalaZ's `Applicative` */
    (foo |@| bar |@| baz) { (_, _, _) => println("It worked!") }
  }
)

Resources

The tendency is for Cats to have better documentation and examples up-front, while ScalaZ has an extensive examples subpackage.

ScalaZ

Cats

Heretical Materials