We’ve been using Play Framework with Elasticsearch and elastic4s for a while. Getting it all to work together requires some boilerplate code, though. We decided to create a small library that would make things easier in future projects. We learned some lessons about building Scala libraries along the way.

play-elastic4s automatically generates elastic4s converters from regular Play JSON formatters

play-elastic4s automatically generates elastic4s converters from regular Play JSON formatters

The library itself is hosted on GitHub. It’s a work in progress, but any comments are welcome. There is also an activator project with a small example; it should be added to the activator templates directory in a matter of days.

The Problems

There are two main issues we wanted our library to solve.

The first one is the necessity to define two types of JSON formatters for the domain objects. For any type T, Play requires Reads[T] and/or Writes[T] from play.api.libs.json (play JSON docs). In elastic4s, on the other hand, one should provide Indexable[T] (see indexing from classes) for inserting documents and HitAs[T] (see search conversion) for parsing search results. The only real difference between the two approaches is the fact that Play parser is fed with JSON only, while HitAs can consume additional metadata returned by Elasticsearch server (such as magic _id, _index or _score fields). The difference is small enough to consider writing both formatters a violation of DRY principle. We could definitely do something to avoid it.

The second feature of our dreamed-of library was integration with Play lifecycle and configuration management. We got accustomed to setting all the configuration vars in application.conf and having all the necessary wiring done behind the scenes.

Initial Approach

The library started as a simple one-day hackathon without too much designing. We simply exploited the golden rule of Object Oriented Programming:

Every issue can be solved by introducing an extra layer of indirection.

So at the core of our module was a new ElasticClientWrapper class. It had plenty of methods and a few clever tricks, but for the purpose of this post we might as well assume it looked similar to this:

import org.sksamuel.elastic4s.ElasticClient
import play.api.libs.json._

class ElasticClientWrapper(val underlying: ElasticClient) {

    def index[T: Writes](id: String, index: String, doc: T): Future[Boolean] = { ... }
    def getById[T: Reads](id: String, index: String): Future[Option[T]] = { ... }
    def search[T: Reads](query: QueryDefinition, index: String): Future[List[T]] = { ... }

The main task of this interface layer was to accept communication with Elasticsearch using our already written Play JSON formatters. The methods defined on ElasticClientWrapper used Play JSON formatters to create valid elastic4s queries.

Having dealt with JSON formatting, we moved on to configuration management. We decided the parameters should be loaded from application.conf and injected transparently. So we defined an extra node in the configuration file and provided a module which would read the cluster definitions and provide named bindings for the connections. A relevant node in application.conf was:

# application.conf elastic4s { clusters { fooCluster { uri: "elasticsearch://foo-es-host.myorg.com:9300" } barCluster { uri: "elasticsearch://bar-es-host.myorg.com:9300" } } }

The proper clients were then injected using named bindings:

class FooDao @Inject() (@Named("fooCluster") client: ElasticClientWrapper) { def get(id: String): Future[Option[Foo]] = client.getById(id, "foos") ... }

And it worked like a charm. At least until we wanted to plug it into one of our real projects.

Why it didn’t Work

We happily plugged the library in one of the projects in order to remove the boilerplate mentioned at the beginning of the post. It very quickly turned out that the small API provided by our wrapper is not enough and we had to add more methods. As we did that, we realized that it’s a path to nowhere. Every user of the library would eventually form the same conclusion:

In order to profit from implicit JSON conversions, I have to stick to the methods exposed on the wrapper.

But the wrapper interface was extremely small compared to the capabilities of underlying elastic4s library. We had to do better.

Goals Revisited

So we reviewed our goals again, and focused on achieving them while keeping our library as small as possible. This time, it lead to the following decisions:

Keep the Concepts Separated

The library was supposed to handle two issues (JSON serialization and configuration management). The initial approach tried to solve them both at the same time. In the revised design, we kept those issues separate, to the point that users might use any one of them without the other.

Don’t Make Users Think

Instead of providing a carefully crafted set of classes and methods, we kept our API as small as possible. Instead of providing our own methods for interacting with ES cluster, we rely on original elastic4s API and only provide a mixin that automatically derives elastic4s typeclasses based on Play JSON formatters. The mixin API, in fact, can be described in just a few lines:

import play.api.libs.json.{Reads, Writes}
import com.sksamuel.elastic4s.HitAs
import com.sksamuel.elastic4s.source.Indexable

trait PlayElasticJsonSupport {
    implicit def playReadsToHitAs[T](reads: Reads[T]): HitAs[T] = { ... }
    implicit def playWritesToIndexable[T](writes: Writes[T]): Indexable[T] = { ... } 

And that’s it. The only think the user has to do is extend the trait. Under the hood, quite a few things have to happen, because the typeclasses have slightly different interfaces. From the user’s perspective, though, everything is simple – they use the original elastic4s API without having to provide the typeclass instances by hand.


From a user’s perspective, including a library in their project is always a trade-off. The more libraries in a project, the more difficult it is to keep them all up-to-date and resolve conflicts. The libraries get old and support gets dropped. Having that all in mind, forcing the users to learn another new API is an additional cost that might tip the scale.