Traits extended by one-line case classes

Let's start talking about Scala, that pretty language making you able to use both object-oriented and functional programming paradigms.
If you've never heard about it, take a look at Scala School. You have to be at ease with trait and case class concepts to understand this post.

We all agree: keeping an abstract layer in your code is something good, but it's often painful when you have to rewrite n-times some children having a similar implementation.
I always try to keep that children readable and fast understandable by making them one-liners. Adding a child becomes quite easy, even for a non-scala developer.

Trivial trait

Let's define a trait that will be the minimal representation of a message.

trait Message {
  def id: String
  def content: String
}

case class DefaultMessage(id: String, content: String) extends Message
case class FromToMessage(id: String, content: String, from: String, to: String) extends Message

No need for writing overriding methods. Actually, id and content parameters are defined as methods that implicitly override Message's methods. Nice, isn't it?

Trait using serialization

OK, that was quite simple. But how will you do if you have to create custom serializer object for each message type? In that example, we will be using the lift framework to (de)serialize JSON.

trait MessageSerializer[M <: Message] {
  implicit val format = DefaultFormats

  def apply(m: M): String = {
    Serialization.write(m)
  }

  def unapply(s: String)(implicit mf: Manifest[M]): Option[M] = {
    for {
      jvalue <- JsonParser.parseOpt(s)
      m <- jvalue.extractOpt[M]
    } yield m
  }
}

object DefaultMessageSerializer extends MessageSerializer[DefaultMessage]
object FromToMessageSerializer extends MessageSerializer[FromToMessage]

If you don't know what are apply and unapply methods, just take a look at this sample code to understand their usage:

/* implicit call to apply method */
val a = DefaultMessage("1234", "Hello world!")
println(DefaultMessageSerializer(a)) // print: {"id": "1234", "content": "Hello world!"}

/* implicit call to unapply method */
"""{"id": "5678", "content": "Love", "from": "me", "to": "you"}""" match {
  case FromToMessageSerializer(m) => println(m) // print FromToMessage("5678", "Love", "me", "you")
  case _ => println("error")
}

Blog

À lire également

Elasticsearch Observability: logs, metrics, and traces explained

Modern architectures generate ever-growing volumes of data. Microservices, APIs, cloud workloads, and serverless environments multiply potential failure points. In this context, understanding what is really happening in production has become a central challenge.
Engineering

ELK Stack: what it is used for and how to use it for observability

Understanding what is really happening inside a modern application has become increasingly complex. Microservices, cloud environments, and the growing number of physical or virtual servers all contribute to an explosion of technical signals. This distribution makes so-called “traditional” log analysis—based on directly connecting to a single machine—hard to sustain at scale.
Engineering

Identity Access Management: pillar of security and compliance

Identity Access Management (IAM) is now one of the foundations of information system security and governance.…

Engineering