OverviewThe
Labeled ToString project provides several traits you can mix into case classes in order to get
toString representations that include parameter labels. That means you get
Person(name=John Doe,age=30) instead of
Person(John Doe,30).
ExampleHere's a normal case class:
case class Person(name: String, age: Int)
Person("John Doe", 30).toString
//result is "Person(John Doe,30)"
Here's our labeled case class:
import com.yuvimasory.tostring._
case class Person(name: String, age: Int) extends LabeledToStringDef
Person("John Doe", 30).toString
//result is "Person(name=John Doe,age=30)"
Choosing a traitThe
com.yuvimasory.tostring package provides three traits:
LabeledToStringDef,
LabeledToStringVal, and
LabeledToStringLazyVal. They override the default case class's
toString method with a
def,
val, and
lazy val, respectively.
- If you're not sure which to use, start with LabeledToStringDef, which works in all cases.
- Consider LabeledToStringVal if you know the case class's parameters are either immutable (e.g., primitive types, immutable collections), or have string representations that never change (e.g., arrays).
- Try LabeledToStringLazyVal if you meet the criteria for LabeledToStringVal and want lazy initialization.
- Both of the *Val traits may run slightly faster (since they don't have to recompute toString every time it's needed) at the cost of more memory usage.
- You must use LabeledToStringDef if your case classes are Squeryl tables. If you don't Squeryl will generate bogus SQL.
PerformanceThe
ToString traits use Apache Commons Lang under the hood, which uses reflection to find the parameter lables. Surprisingly, there does not seem to be any performance cost for this. In fact, using these traits actually results in code
faster than the default case class
toString. Try the tests yourself by running
sbt run.
Warning- These traits do not produce the right strings in the REPL yet due to the way the REPL wraps code and mangles names.
- These traits do not work if you add bodies to your case classes (i.e., additional methods or fields).
