- Preface
- Use Cases
- I want to train my Scala team in Functional Programming fundamentals
- I’m writing a performance-sensitive application
- I want to improve quality-of-life for my Scala devs
- I’m writing a library/application that works with a complex DSL
- I want to port a well-known, general-purpose Haskell library to Scala
- I care about licensing
- I care about which stays truer to Haskell
- I care about which has more industry backing
- I chain operations with
for
/yield
, isn’t that all I need? - I hear the
IO
Monad can help me logically organize my code - Futures suck and I hate JVM thread pools. Help?
- Just gimme Monads
- I’m interested in other FP options on the JVM
- Benchmarks
- Usage Considerations
- Library Health and Ecosystems
- Resources
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?
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:
- Functional Programming for Mortals by Sam Halliday (for FP beginners - assumes an OO Scala background)
- Training by the Fantasyland Institute
Cats:
- Scala with Cats by Noel Welsh and Dave Gurnell (assumes 1+ years of Scala experience)
- Training by Underscore Consultants
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.
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.
You probably need Recursion Schemes, which are supplied by the Matryoshka library in ScalaZ-land.
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.
ScalaZ is BSD3, while Cats is MIT + BSD3, since it derived from ScalaZ originally.
ScalaZ does. Its core has a larger API, provides more features up-front,
and tends to keep Haskell function names and operators (e.g. <*>
).
According to this survey, ScalaZ does.
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.
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.
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 theIO
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 inFiber
must end inIO
, 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 aboutExecutionContext
.
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.
Then either is fine, you can flip a coin.
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 were performed using the JMH plugin for SBT. Vanilla Scala and Haskell results are also included where applicable.
All times are in nanoseconds, lower numbers are better.
Kittens and scalaz-deriving were used to derive Eq instances.
Side Library | Version | |||
---|---|---|---|---|
scalaz-deriving | 1.0.0-RC1 | |||
kittens | 1.1.0 | |||
scalaz-ioeffect | 2.10.1 | |||
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 | 386,948 | 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 itsIList
type, not vanillaList
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’sShow
does as well. Cats does not do this, so it can “return early” in the case ofString
.IO
benchmarks for Vanilla Scala are usage ofFuture
.- The error ADT benchmarks for Cats and Haskell use
EitherT[IO, E, A]
, while ScalaZIO
is a bifunctor with explicit error type:IO[E, A]
. See the Features section for more information.
- 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 bothIO
andState
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.
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.
From its Scaladocs:
Safe, invariant alternative to stdlib
List
. Most methods onList
have a sensible equivalent here, either on theIList
interface itself or via typeclass instances (which are the same as those defined for stdlibList
). 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.
From its Scaladocs:
Maybe[A]
is isomorphic toOption[A]
, however there are some differences between the two.Maybe
is invariant inA
whileOption
is covariant.Maybe[A]
does not expose an unsafe get operation to access the underlyingA
value (that may not exist) likeOption[A]
does.Maybe[A]
does not come with an implicit conversion toIterable[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
.
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.
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
orThrowable
for Java-like exceptionsVoid
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 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.
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)
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)
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.
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.
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.
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:
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
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 acats.Functor
instance also has ascalaz.Functor
instance, and vice versa.
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!") }
}
)
The tendency is for Cats to have better documentation and examples up-front, while
ScalaZ has an extensive examples
subpackage.
- Functional Programming for Mortals by Sam Halliday (book)
- Learning ScalaZ by Eugene Yokota (blog series)
- Cheatsheet (typeclass usage and imports)
- ScalaZ README
- Scaladocs
- ScalaZ Gitter
- Cats Website
- Scala with Cats by Noel Walsh and Dave Gurnell (book)
- Scaladocs
- Herding Cats by Eugene Yokota (blog series)
- Cats Gitter
- The Limitations of Type Classes as Subtyped Implicits by Adelbert Chang
- The Eta Language