Updating Controllers


We are still working on making the controllers setup more modular. At the moment, adding support to new pipelines means changing the source code of the existing controllers directly.

For our Maple pipeline, there are two controllers that need to be made aware of the processors we wrote in the previous section. The controllers, as mentioned in an earlier section, are responsible for mapping a given URL to a specific action that Sentinel will execute. Two controllers that are tightly coupled with the processors we have written are called RunsController, which handles a URL for summary file uploads, and StatsController, which handles URLs for statistics query. Accordingly, the RunsController needs to be made aware of our MapleRunsProcessor and the StatsController of our MapleStatsProcessor.

What do we mean by making the controllers aware of our processors? Basically, it means they need to know how to initialize the processor classes. They are handled quite differently for each controllers. This is because Sentinel by default has one endpoint for uploading the summary files (by default this is a POST request on the /runs endpoint), but more than one endpoint for querying the statistics. For the current version, we can simply edit the main ScalatraBootstrap class to add our MapleRunsProcessor class while we need to manually edit the StatsController class to add our MapleStatsProcessor.

The difference in instantiating the processor classes is mostly caused by the need to document the schema returned by different statistics queries. A given pipeline may return a metrics object (the metrics case class we defined earlier) which is completely different from another pipeline. This is not the case for the summary file uploads, where all pipeline processors use the same processRunUpload method. This limitation may be removed in future versions.


The RunsController can be made aware of our MapleRunsProcessor by injecting the class name in the class responsible for instantiating the controller itself. This class, the ScalatraBootstrap class, has an init method where it instantiates all controllers. What we need to do, is to have an implicit value of the type Set[MongodbAccessObject => RunsProcessor]. In other words, a set of functions that creates a RunsProcessor object given a MongodbAccessObject object.

The MongodbAccessObject object is an object representing access to our database. In production runs, this represents access to a live database server, while in testing this is replaced by a testing server. Sentinel has a helper method called makeDelayedProcessor in the nl.lumc.sasc.sentinel.utils.reflect package, which can create the function we require from the processor class.

Invoking it is then quite simple:

class ScalatraBootstrap extends LifeCycle {


    override def init(context: ServletContext) {

        val runsProcessors = Set(

        // continue to initialize and mount the controllers

And that’s it. That’s all we require so that the RunsController class can process uploaded Maple summary files.


The last step is to update the stats controller. There are four endpoints that we can define, each using a method that we have written in MapleStatsProcessor:

  • For the sample-level data points endpoint, /stats/maple/samples
  • For the sample-level aggregated endpoint, /stats/maple/samples/aggregate
  • For the read group-level data points endpoint, /stats/maple/readgroups
  • For the read group-level aggregated endpoint, /stats/maple/readgroups/aggregate

We will define the sample-level endpoints together and leave the read group-level endpoints for you to define.


The first part of the endpoint, /stats is already automatically set by Sentinel, so our route matchers only needs to define the last part.

Before we begin, we need to import the Maple stats containers and their processor in the StatsController.scala file:

import nl.lumc.sasc.sentinel.lumc.ext.maple._

Different from its runs processor counterpart, we will need to instantiate the MapleStatsProcessor directly inside the StatsController body:

val maple = new MapleStatsProcessor(mongo)

After this, we can start with implementing the actual endpoints.


In the StatsController.scala file, add the following Swagger operation definition:

val statsMapleSamplesGetOp =
        summary "Retrieves Maple sample-level data points"
        parameters (
            .description("Run ID filter.")
          queryParam[String]("userId").description("User ID.")
          headerParam[String](HeaderApiKey).description("User API key.")
        responseMessages (
          StringResponseMessage(400, "Invalid Run IDs supplied"),
          StringResponseMessage(401, Payloads.OptionalAuthenticationError.message)))

While the definitions is not required per-se, it is always useful to let users know the parameters your endpoint accepts. In this case, our endpoint accepts three optional parameters: run ID for filtering and user ID with the associated API key for optional authentication. We also define the HTTP error code we will return in case any of the supplied arguments are invalid.

Here comes the route matcher for the data points query:

get("/maple/datapoints", operation(statsMapleSamplesGetOp)) {

  val runIds = params.getAs[Seq[DbId]]("runIds").getOrElse(Seq.empty)
  val idSelector = ManyContainOne("runId", runIds)

  val user = Try(simpleKeyAuth(params => params.get("userId"))).toOption
  if ((Option(request.getHeader(HeaderApiKey)).nonEmpty || params.get("userId").nonEmpty) && user.isEmpty)
    halt(401, Payloads.OptionalAuthenticationError)

  new AsyncResult {
    val is = maple.getMapleSampleStats(idSelection, user)
      .map {
        case -\/(err) => err.toActionResult
        case \/-(res) => Ok(res)

In the code block, you can see that the first two val declarations capture the parameters supplied by the user. The runIds parameter is an optional parameter for selecting only particular run IDs. These are IDs that users get when they upload the run summary file for the first time and are assigned randomly by the database. We then proceeded to create a selector object (basically a MongoDBObject) which will then be used for filtering the metrics. Here we use the Sentinel-defined ManyContainOne helper case class, which has the effect of selecting any metrics whose run ID is contained within the user-supplied run ID. If the user does not supply any run IDs, then no filtering will be done.

The val user declaration allows for optional user authentication. A succesfully authenticated user will get additional information for data points that he/she has uploaded, such as the sample name. He/she may still see data points uploaded by other users, only without any identifying information.

Finally, we run the query on the database using the AsyncResult class provided by Scalatra. This allows our query to be run asynchronously so that Sentinel may process other queries without waiting for this to finish.


With that set, we can now define the endpoint for aggregated queries. Let’s start with the API definition as before:

val statsMapleSamplesAggregateGetOp =
        summary "Retrieves Maple sample-level aggregated data points"
            .description("Run ID filter.")
        responseMessages StringResponseMessage(400, "Invalid Run IDs supplied"))

The API definition is similar to the single data points, with difference being the authentication is not present anymore. This makes sense, since aggregated data points do not have any name labels associated with them.

get("/maple/datapoints/aggregate", operation(statsMapleDatapointsAggregateGetOp)) {
  val runIds = getRunObjectIds(params.getAs[String]("runIds"))
  val idSelector = ManyContainOne("runId", runIds)

  new AsyncResult {
    val is =
          .map {
            case -\/(err) => err.toActionResult
            case \/-(res) => Ok(res)

This is almost the same as our previous endpoint, except that there is an extra None argument supplied to the function above. This is used only when our stats processor distinguishes between single-end and paired-end data. In our case, we made no such distinction and thus we can simply use None there.


The MapleStatsController implementation marks the end of our tutorial. You have just added a new pipeline support to Sentinel! Feel free to play around with uploading and querying the endpoints you just created. When you’re more familiar with the code base, you can experiment with adding support for more complex pipelines. If that’s not enough, head over to the Contributing page and see how you can contribute to Sentinel development.