There is a very long issue under Wartremover titled: “Default arguments are insane” needs explanation. Wartremover is a Scala linter that I personally love using as it definitely increases the quality of any Scala code I write.

The issues around using Default Arguments or Default Parameter Values (as Scala refers to them) are somewhat subtle. The Wartemover issue seems to go on forever, but there are lots of really great ideas in there and I though I could summarise some of them here.

What is a Default Parameter Value?

Scala provides the ability to give parameters default values that can be used to allow a caller to omit those parameters.

Here’s a quick example of Default Parameter Values (DPV):

def log(message: String, level: String = "INFO") = println(s"$level: $message")

log("System starting")  //We didn't supply level which defaults to INFO; prints INFO: System starting
log("User not found", "WARNING")  // prints WARNING: User not found

One of the main benefits of DPV is that you don’t need to supply all the parameters to a method - just the ones that are mandatory. This sounds like a really useful idea, so why are people recommending that we don’t use it?


1. Unclear Code

Here’s an example of some code that uses DPV:

val streamName: String = ...

Now if you just read the above method it looks like it might be doing the wrong thing. Why are we supplying a Stream name to a method that clearly states that it needs a Url?

The method is defined as:

object KinesisStream {
  def fromUrl(streamName: String, url: Option[String] = None): ...

The default value for url hides the true nature of what this method needs. The url parameter has been made optional because under some circumstances it is not needed.

2. Breaks Currying and Partial Application

Rob Norris has a nice article on Methods are not Functions which covers why currying and partial Application of Functions is broken with DPV. Here’s a simple example:

def log(message: String, level: String = "INFO") = println(s"$level: $message")

scala> log("hello!") //one param
INFO: hello!

scala> log("hello!", "WARN") //both params
WARN: hello!

//can we map over log?
scala> val messages = List("hello", "world")
messages: List[String] = List(hello, world)

 //does not work
<console>:14: error: type mismatch;
 found   : (String, String) => Unit
 required: String => ?
 //does not work
scala> _)
<console>:14: error: type mismatch;
 found   : (String, String) => Unit
 required: String => ? _)

INFO: hello
INFO: world
res6: List[Unit] = List((), ())

//also works
scala> => log(x))
INFO: hello
INFO: world
res7: List[Unit] = List((), ())

Weird. So it seems like you can use Currying and Partial Application if you tweak the syntax a little.

Let’s have a go with Rob’s example:

scala> def foo(n: Int = 3, s: String) = s * n
foo: (n: Int, s: String)String

scala> foo(s = "$$")
res36: String = $$$$$$

scala> val p1 = foo(42, _:String)
p1: String => String = $$Lambda$1192/1172016038@6c826924

scala> p1("@")
res38: String = @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

//can we get defaults for free after conversion to a function?
scala> val f1 = foo _
f1: (Int, String) => String = $$Lambda$1193/1526085518@205b3a1a

//does not work
scala> f1(10)
<console>:13: error: not enough arguments for method apply: (v1: Int, v2: String)String in trait Function2.
Unspecified value parameter v2.

scala> val p2 = f1(10, _: String)
p2: String => String = $$Lambda$1408/1660635397@7f8ac326

scala> p2("*")
res44: String = **********

scala> val p3 = f1(_:Int, "$")
p3 => String = $$Lambda$1409/1435397638@4047789d

scala> p3(5)
res48: String = $$$$$

//we can also supply all arguments
scala> f1(10, "*")
res50: String = **********

//use in higher-order functions
res51: List[String] = List(hellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohellohello, worldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworldworld)

res52: List[String] = List(hellohellohellohellohellohellohellohellohellohello, worldworldworldworldworldworldworldworldworldworld)

scala> List(1,2,3).map(p3)
res53: List[String] = List($, $$, $$$)

This seems to be a moot issue. While the syntax is more awkward than necessary, Currying and Partial Application is certainly possible with DPV. Once we η-expand the method foo to the value f1, we lose the defaulted values defined in foo though; which seems a little odd.

Another way to partially apply methods with default parameters is create a wrapper method with only the mandatory fields:

scala> def foo(n: Int = 3, s: String) = s * n
foo: (n: Int, s: String)String

//wrap foo with foo2
scala> def foo2(s: String) = foo(s = s)
foo2: (s: String)String

scala> foo2("#")
res29: String = ###

//now we can use foo2 in higher-order functions
res30: List[String] = List(hellohellohello, worldworldworld)

The above technique alludes that there should have been two separate methods all along.

3. Bugs of Convenience

In a project I worked on we had some asynchronous tasks that split a workload into chunks using a sliding window of ten elements. Here’s a simplified version of the code:

scala> val elements = (1 to 30).toList
elements: List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)

scala> val it = elements.sliding(10)
it: Iterator[List[Int]] = non-empty iterator

res22: List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) ??? //what does this print? ??? //what does this print?

We witnessed a subtle bug where performance of our processing pipeline was terrible. What was going on?

The actual definition of sliding is:

def sliding[B >: A](size: Int, step: Int = 1): GroupedIterator[B]

Notice the default step of 1. When we used the sliding function we assumed that the size supplied would also be the step. Everything compiled and there were no warnings. Here is the output of the above example:

res22: List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

res23: List[Int] = List(2, 3, 4, 5, 6, 7, 8, 9, 10, 11)

res24: List[Int] = List(3, 4, 5, 6, 7, 8, 9, 10, 11, 12)

As you can see we were processing the same items multiple times. If the step parameter were explicit this would not have happened. Once the mistake was corrected:

scala> val it = elements.sliding(10, 10)
it: Iterator[List[Int]] = non-empty iterator

res25: List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

res26: List[Int] = List(11, 12, 13, 14, 15, 16, 17, 18, 19, 20)

res27: List[Int] = List(21, 22, 23, 24, 25, 26, 27, 28, 29, 30)

We can see the code slides over ten elements at a time as expected. These kind of bugs are hard to find. We lean on the compiler a lot to point out our mistakes. With DPV while our lives are more convenient because we have less parameters to supply our functions, the compiler fails to see our errors and omissions and can’t help.

4. Bugs due to Refactoring

Consider an API at version 1.x that has the following method:

object Database
  def fromUrl(url: String): ...

A client of the library may use it like:


The developer of the library decides to add a tableName parameter as the url is optional when run locally. Not wanting to introduce an additional method for this our developer then decides to make url a DPV.

As this is a breaking change, he bumps the version of the library to 2.x.

object Database {
  def fromUrl(tableName: String, url: Option[String] = None): ...

Now this all seems fine. The major version of the library has been bumped so it indicates potential for a breaking change.

Unfortunately, the client code still compiles after moving to version 2.x of the library:


But now we have problem. Since the location of the url parameter has changed, we are supplying a url as the tableName parameter, and the compiler can’t inform us that anything is broken. We have to find out at runtime that we have a problem.

Alternate Designs

Here are some ways to get around using DPV.

Supply all Parameters

Replace developer convenience for software correctness. Get the developer to supply all parameters. We can change the log function from:

def log(message: String, level: String = "INFO") = println(s"$level: $message")


def log(message: String, level: String) = println(s"$level: $message")

Callers of the log method have to now supply both the message and the level:

log("System starting", "INFO")

Better yet, convert the level parameter to an ADT so that the callers can’t pass through invalid values.

However this technique can get tedious if you have a lot of parameters that you don’t really care about.

Breakout Separate Methods

If you don’t want to supply all the parameters each time, consider creating separate methods for the situations you care about.

In the case of the bug with the Database refactoring, we could have pulled out some extra methods:

object Database {
  def remote(url: DatabaseUrl): ...
  def remoteWithTable(tableName: String, url: DatabaseUrl): ...
  def local(tableName: String): ...

Rob Norris on splitting out methods:

If you have a method with a single default arg you could reasonably suggest splitting it into two methods (as you might do with a single option or boolean arg)

Have a Default Object

If you have a lot of parameters to your function (and this might be a problem by itself) you could use a default object.

Take tcpConnect as an example:

def tcpConnect(host: String, port: Int = 80, sslEncryption: Boolean = false, localAddress: Option[String] = None):  String = "connected"

This could be re-written with a default object:

//original method with defaults removed
def tcpConnect(host: String, port: Int, sslEncryption: Boolean, localAddress: Option[String]):  String = "connected"

//config class
case class TcpConnection(host: String, port: Int, sslEncryption: Boolean, localAddress: Option[String])

//function that calls tcpConnect
def fromTcpConnection(tcpConnection: TcpConnection): String =
  tcpConnect(, tcpConnection.port, tcpConnection.sslEncryption, tcpConnection.localAddress)

//default object
def defaultTcpConnection: TcpConnection = TcpConnction(host: String = "localhost", port: Int = 80, sslEncryption: Boolean = false, localAddress: Option[String] = None)

//usage for a specific URL
fromTcpConnection(defaultTcpConnection.copy(host = "http://...."))

//usage for a specific port
fromTcpConnection(defaultTcpConnection.copy(port = 8080))

//usage for a secure URL
fromTcpConnection(defaultTcpConnection.copy(host = "https://...", port = 443, sslEncryption = true))

Rob Norris on using default config objects:

… right, but then you have the awful copy method to contend with, then you add lenses, then you add phantom types to ensure that options haven’t been set more than once, etc., etc., and I’m not convinced that the complexity is warranted, given the lack thus far of any convincing reason not to use default args

As Rob mentions, depending on how far you want to take it, avoiding DPVs might lead to very complex solutions.

As Maxwell Swadling points out you could also break out separate methods for this:

def connectHTTP(host: String):  String //where port = 80, sslEncryption = false, localAddress = None
def connectHTTPPort(host: String, port: Int):  String //where sslEncryption = false, localAddress = None
def connectHTTPS(host: String):  String //where port = 443, sslEncryption = true, , localAddress = None
def tcpConnect(host: String, port: Int, sslEncryption: Boolean, localAddress: Option[String]):  String //the normal connect with all the arguments.

If none of these alternates seem attractive, go ahead and use DPV but think hard about how it may introduce bugs into your code base.

Parting Thoughts

Mark Hibberd recommends not using DPV for the following:

  1. Allocation of resources (there are even examples of this in scalaz) - which is utterly wrong. Anything that is allocated by a default argument has no reasonable lifecycle and is unlikely (or impossible) to be closed properly.
  2. Default configurations - these are a developer convenience that lead to operational bugs. There is no such thing as a “safe” default, where it could mean forgetting to set something in production leads to an incorrect value rather than an error (this is closely related to what Minsky says as mentioned by Eric above).
  3. Common arguments through delegating methods - these are representative of what @maxpow4h originally stated. That if you have multiple methods with optional arguments, it is extremely easy for incorrect code to compile by forgetting to delegate one of the arguments.
  4. Faux overloading - it is cool to hate on overloading so I will avoid it by using named arguments with defaults, ending up with the exact same situation. Code that is subtly wrong (such as forgetting to pass argument) still compiles. This is not an acceptable situation.

Eric Torreborre on when to use DPV:

So my own conclusion is that default arguments (and overloading) still have some value (for non-critical DSLs) but you need to be very careful where you use them.

Mark Hibberd on focussing on correct programs:

But the most troublesome part of this thread, is that almost all of the discussion is about what developers find “convenient” and aesthetically pleasing, when we should be asking how a language feature adds or removes from our ability to build robust, correct programs - and, as quickly as possible. When held in this light, default arguments do not hold up. They are a mere syntactic convenience - that does not help us with this goal. This might be ok, if they didn’t come with risk or issues, but even the gentler arguments in this thread should be enough to highlight their use in a linting tool - especially given their inherent lack of motivation to begin with.

But yeh. Everyone gets to live in their own teams codebases. I just prefer mine without these undue risks.

So in summary:

  1. Don’t use DPV in production code. This could lead to bugs that are hard to find
  2. Possibly use DPV in non-production code like such as test DSLs
  3. If DPV helps to reduce the number of methods or the complexity of your solution, consider using it but be aware of the consequences. Alternatively redesign your code so it does not require DPV.