Scala Guide - Best Practices

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We recommended you read Twitter’s “Effective Scala” guide. The following sections highlight areas most often seen in our applications:

  1. Return type annotations

  2. Type aliases

  3. Implicits

  4. Collections - Pay special attention to performance

  5. Functional programming

  6. Pattern matching

Additional recommendations

  1. Avoid pattern matching on Options.

  2. Never use return: http://tpolecat.github.io/2014/05/09/return.html

  3. Parentheses in Scala are not optional. Be aware of extraneous parentheses:

    val bad = Set.empty() // expands to Set.empty.apply(), evaluates to false   val evil = Seq(1,2,3).toSet() // same as above

  4. Always use the most generic collection type possible, typically one of: Iterable[T], Seq[T], Set[T], or Map[T].

  5. Use Seq[T], not List[T] (see: http://stackoverflow.com/a/10866807/410286) except where you specifically need to force one implementation over another. The most common exception is that Play form mappers require List[T], so you have to use it there. Seq is the interface, List the implementation, analogous to Map and HashMap in Java.

  6. When possible, use scala.collection.breakOut to avoid producing and converting intermediate collections with .toMap, .toSeq, .toSet, etc.

    // Produces an intermediate Seq[(String, Int)] and converts it to Map[String, Int] val bad = Seq("Toronto", "New York", "San Francisco").map(s => (s, s.length)).toMap   // No intermediate values or conversions involved val good: Map[String, Int] = Seq("Toronto", "New York", "San Francisco").map(s => (s, s.length))(breakOut)

    Please note that the type annotation is required or breakOut will not be able to infer and use the proper builder. To know when breakOut can be used, check in the Scaladoc if the higher-order function (map in the example above) takes an implicit CanBuildFrom parameter. The attentive reader may notice that breakOut is in fact being passed in lieu of the implicit CanBuildFrom parameter.

  7. Do not overuse tuples, decompose them or better, use case classes:

    val paul = Some(("Paul", 42))   // Bad paul map (p => s"Name: ${p._1}, Age: ${p._2}")   // Good paul map { case (name, age) => s"Name: $name, Age: $age" }   // Better case class Person(name: String, age: Int)   val paul = Some(Person("Paul", 42))   paul map (p => s"Name: ${p.name}, Age: ${p.age}")

  8. Do not overdo method chaining, use vals to name intermediate steps:

    val bad = xs.filter(...).map(...).exists(...)   val filteredXs = xs filter (...) val ys = filteredXs map xToY val answer = ys exists (...)

  9. Avoid implicit conversions. Instead, use implicit value classes (extension methods), they are more efficient:

    class Bad(n: Int) { def stars = "*" * n } implicit def bad(n: Int) = new Bad(n)   implicit class Good(private val n: Int) extends AnyVal { def stars = "*" * n }

Be sure to make the wrapped parameter private! Otherwise you end up exposing an additional method that just returns the same object. You do have to state private val explicitly, otherwise it defaults to private[this] which is too strong for AnyVal wrappers.

  1. Whenever possible, avoid matching on case _. Try to be specific and thorough, favor exhaustive matches. Do not let unforeseen conditions silently fall through.

  2. Never use null, use Option instead. If dealing with legacy APIs, wrap possible nulls:

    Option(javaClass.returnsNull())   javaClass.takesNull(opt.orNull)

  3. Avoid Some.apply, use Option.apply instead. Option.apply protects against null, while Some.apply is perfectly happy to return Some(null), eventually raising an NPE. Also note that Some.apply is type-inferred as Some[T], not Option[T].

    val bad = Some(System.getenv("a")) // NPE waiting to happen   val good = Option(System.getenv("a")) // Would return None

    By the principle of least astonishment, use Option.apply even if you “know” your reference can never be null. By being consistent, we avoid wondering whether a Some.apply in the code was intentional or something the developer overlooked:

    val bad = Some(math.random())   val good = Option(math.random()) // And never have to worry about it again

  4. Do not abuse Option. Some types already provide a good default to represent “nothing”. For instance, before declaring an Option[Seq[T]], ask yourself whether there is any semantic difference between Some(Nil) and None. If not (and usually, there isn’t), use Seq[T] and return the empty list.

  5. Never extend a case class. Extending a case class with another case class is forbidden by the compiler. Extending a case class with a regular class, while permitted, produces nasty results:

    case class A(a: Int)   // error: case class B has case ancestor A, but case-to-case inheritance is prohibited. case class B(a: Int, val b: String) extends A(a)   class C(a: Int, c: Int) extends A(a)   val a = A(1) val c = new C(1, 2) assert(a == c) assert(a.hashCode == c.hashCode) assert(c.toString == "A(1)") // Wat   val d = new C(1, 3) assert(c.hashCode == d.hashCode)   val e = c.copy(4) // note there is no C#copy(Int, Int) method assert(!e.isInstanceOf[C]) // e is a proper instance of A

  6. Do not use JavaConversions, use JavaConverters and its multiple asScala and asJava methods. While JavaConversions may happen “automagically” at unexpected times, usually masquerading type errors, JavaConverters gives you explicit control of when conversions happen (only as dangerous as you want). You can then transparently use Java collections as if they were Scala collections, usually for performance or interoperability reasons:

    import scala.collection.JavaConverters.mapAsScalaMapConverter import scala.collection.mutable   val map: mutable.Map[String, String] = new java.util.HashMap[String, String].asScala map += "foo" -> "bar" assert(map("foo") == "bar")

    // Counter-example val m = Map(1 -> "one") m.contains("") // Fails to compile: type mismatch   import collection.JavaConversions._ m.contains("") // false

  7. Instead of throwing exceptions, use a more suitable type: Option, Either, or Try. Exceptions are not functional. Functions that throw exceptions are not total functions, they do not return a value for all possible inputs.

  8. Do not try to catch exceptions, Try to catch exceptions! Try does for exceptions what Option does for null.

  9. Remember that Future already encapsulates a Try. Instead of Future[Try[T]], use Future.failed, Future.fromTry, and Future.successful.

  10. No matter what, never use a “catch-all” exception handler. Some features in Scala are implemented relying on exceptions. Use NonFatal instead:

    import scala.util.control.NonFatal   try { 1 / 0 } catch { case e: Exception => // Bad case _: Throwable => // Worse case _ => // Worst, scalac will warn and "recommend" the expression above case NonFatal(e) => // The only acceptable way to catch all exceptions }

    Please note that NonFatal is not needed when pattern matching Future or Try since they already filter for it.

  11. Make judicious use of the various assertions (contracts) offered by Scala. See scala.Predef for the complete reference.

    1. Assertions (assert(b != 0)) are used to document and check design-by-contract invariants in code. They can be disabled at runtime with the -Xdisable-assertions command line option.

    2. require is used to check pre-conditions, blaming the caller of a method for violating them. Unlike other assertions, require throws IllegalArgumentException instead of AssertionError and can never be disabled at runtime.

      Make ample and liberal use of require, specially in constructors. Do not allow invalid state to ever be created:

      case class Person(name: String, age: Int) { require(name.trim.nonEmpty, "name cannot be empty") require(age >= 0, "age cannot be negative") require(age <= 130, "oldest unambiguously documented human was 122") }

      Please note that even though require throws an exception, this is not a violation of the previous recommendations. Constructors are not methods, they cannot return Try[Person].

    3. ensuring is used on a method return value to check post-conditions:

      def square(a: Int) = {a * a} ensuring (_ > 0)

  12. Know well and make good use of the standard Scala collections library classes and methods:

    1. Never test for c.length == 0 (may be O(n)), use c.isEmpty instead.

    2. Instead of c.filter(_ > 0).headOption, use c find(_ > 0).

    3. Instead of c.find(_ > 0).isDefined, use c exists (_ > 0).

    4. Instead of c exists (_ == 0), use c contains 0.

    This is not only a matter of style: for some collections, the recommendations above can be algorithmically more efficient than the naive approach.

  13. Do not import scala.collection.mutable._ or even any single mutable collection directly. Instead, import the mutable package itself and use it explicitly as a namespace prefix to denote mutability, which also avoids name conflicts if using both mutable and immutable structures in the same scope:

    import scala.collection.mutable.Set val bad = Set(1, 2, 3) // Too subtle and risky for the inattentive reader   import scala.collection.mutable val good = mutable.Set(1, 2, 3)

  14. Prefer a mutable val over an immutable var:

    import scala.collection.mutable   var bad = Set(1, 2, 3) bad += 4   val good = mutable.Set(1, 2, 3) good += 4   assert(good.sameElements(bad))

  15. No “stringly” typed code. Use Enumeration or sealed types with case objects. Enumeration and sealed types have similar purpose and usage, but they do not fully overlap. Enumeration, for instance, does not check for exhaustive matching while sealed types do not, well, enumerate.

    object Season extends Enumeration { type Season = Value val Spring, Summer, Autumn, Winter = Value }   sealed trait Gender case object Male extends Gender case object Female extends Gender

  16. When creating new Enumerations, always define a type alias (as above). Never use MyEnum.Value to refer to the type of enumeration values:

    def bad(a: Season.Value) = ???   import Season.Season def good(a: Season) = ???

  17. If a function takes multiple arguments of the same type, use named parameters to ensure values are not passed in the wrong order:

    def geo(latitude: Double, longitude: Double) = ???   val bad = geo(a, b)   val good = geo(latitude = a, longitude = b)

  18. Always use named parameters with booleans, even when they are the only parameter:

    // Bad Utils.delete(true)   // Good Utils.delete(recursively = true)

  19. Avoid declaring functions with boolean arguments (“magic booleans”). Do not model any two possible states as boolean:

    // Bad case class Person(male: Boolean) case class Car(isAutomatic: Boolean, isElectric: Boolean, isFourDoor: Boolean)   // Good case class Person(gender: Gender)   // Bad def findPeople(filterMales: Boolean): Seq[People]   // Good def findMales: Seq[People] def findFemales: Seq[People]

  20. Do not define abstract vals in traits or abstract classes. Abstract vals are a source of headaches and unexpected behavior in Scala:

    trait Bad { val bad: Int val worse = bad + bad }   object ImBad extends Bad { val bad = 1 }   assert(ImBad.worse == 2) assert(ImBad.worse == 0)

    Always define abstract defs, they are more general and safer:

    trait Good { def good: Int val better = good + good }   object ImGood extends Good { def good = 1 }   assert(ImGood.better == 2)

    Note: beware that overriding the abstract def with a concrete val also suffers from the problem above.

  21. Avoid lazy val. lazy val is not free, or even cheap. Use it only when you absolutely need laziness semantics for correctness, never for “optimization”. The initialization of a lazy val is super-expensive due to monitor acquisition cost, while every access is expensive due to volatile. Worse, lazy val may deadlock even when there are no circular dependencies between them.

    lazy vals are compiled into a double-checked locking instance with a dedicated volatile guard. Notably, this compilation template yields a source of significant performance woes: we need to pass through the volatile read on the guard boolean for every read of the lazy val. This creates write barriers, cache invalidation and general mayhem with a lot of the optimizations that HotSpot tries to perform. All for a value that may or may not even care about asynchronous access, and even more importantly, is guaranteed to be true only once and is false indefinitely thereafter (baring hilarious tricks with reflection). Contrary to what is commonly believed, this guard isn’t simply optimized away by HotSpot (evidence for this comes from benchmarks on warm code and assembly dumps). - Daniel Spiewak

You keep using that word, I do not think it means what you think it means

  1. objects are lazily initialized. If an object is defined at the top level or inside another object, then that initialization is relatively cheap, done via Java’s static initializer blocks. However if an object is defined inside a class or trait then it compiles the same as a lazy val! That means it suffers all the same problems described in the above section. There are probably no cases where an object inside a class definition is necessary anyway, so just avoid it. But feel free to define them inside other objects!

    trait T { def x: Int }   class Bad { object Inner extends T { val x = 10 } }   class Good { val inner: T = new T { val x = 10 } }   object ThisIsFine { object Inner extends T { val x = 10 } }

  2. Secondary constructors: with default parameters, secondary constructors are a lot less frequently needed in Scala than in Java. But they can still be quite useful, use them when needed. Just avoid pathological cases:

    class Bad(a: Int, b: Int) { def this(a: Int) = this(a, 0) }   class Good(a: Int, b: Int = 0)

  3. If you need to wrap a value that can be immediately computed into a Future, use Future.successful, which returns an already completed Future. Calling Future.apply incurs all the overhead of starting an asynchronous computation, no matter if the computation is a simple, immediate result:

    val bad = Future(0)   val good = Future.successful(str.trim)

  4. When combining Futures in for-comprehensions, do not use the following idiom:

    for { a <- futureA b <- futureB c <- futureC } yield a + b + c

    Unless futureB depends on a and futureC depends on b, that will unnecessarily chain (serialize) the Futures, starting a future only after the previous one has finished, which most likely defeats its purpose. To properly start Futures in parallel and combine their results, use the following idiom:

    val fa = futureA val fb = futureB val fc = futureC   for { a <- fa b <- fb c <- fc } yield a + b + c

  5. Avoid isInstanceOf or asInstanceOf. Safe casting is actually one of the best use cases for pattern matching. It is more flexible, guarantees that a cast will only happen if it can succeed, and allows you, for instance, to use different branches to carry out multiple conditional casts at the same time for various types, perform conversions, or fallback to a default value instead of throwing an exception:

    val bad = if (a.isInstanceOf[Int]) x else y   val good = a match { case _: Int => x case _ => y }   val bad = a.asInstanceOf[Int]   val good = a match { case i: Int => i case d: Double => d.toInt case _ => 0 // Or just throw new ClassCastException }

  6. Avoid structural types, do not import scala.language.reflectiveCalls. Structural types are implemented with reflection at runtime, and are inherently less performant than nominal types.

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