Better Spray metrics with Kamon

Introducing spray-kamon-metrics

Posted by Daniel Solano Gómez on September 24, 2015

At Monsanto, we have adopted Kamon for monitoring our microservices implemented in Scala with Spray. Kamon provides an integration that will automatically instrument our services to generate traces for each incoming request. This is great, but we wanted more. Some of the things we wanted to improve included:

  • Providing better response metrics
  • Detecting requests that time out
  • Reporting Spray can server statistic through Kamon

To accomplish this, we have created and open sourced the spray-kamon-metrics library. This library contains two independent pieces of functionality:

  1. TracingHttpService, a drop-in replacement for spray-routing’s HttpService class. TracingHttpService provides better trace metrics and handles timed out requests.
  2. KamonHttp, a drop-in replacement for spray-can’s Http Akka I/O extension. It will transparently collect Spray can’s server metrics.

For the rest of the post, we will explore in greater detail what the library does and how it works. Finally, we will wrap up by presenting some ideas for future development. If you like, you can visit the project’s page on GitHub for more details about how to integrate the library into your application.

Improving service metrics with TracingHttpService

The TracingHttpService fulfils the first two of our goals:

  1. Providing better response metrics
  2. Detection and tracing of request timeouts

Providing better response metrics

Kamon’s Spray integration is immensely useful. However, we felt like the default behaviour makes it difficult to really understand the application that is being measured. In particular:

  1. It creates traces for each response, but they all have a default name of UnnamedTrace. The intention is for application developers to give meaningful names to each response. However, it would be nice if the library provided a more meaningful default.
  2. There are metrics collected under the http-server category, but they only contain the trace name and resulting status code, and it is not easily to correlate the http-server metrics with corresponding traces, especially if we have not given the traces meaningful names.

While we could have resolved these issues to some extent by providing a name generator, the core problem was that, even with more meaningful names, there is no way to add tags to a trace that has already been established. As a result, the dimensionality of the metrics that are produced are restricted to trace name and response status code. We want more, including:

  • What was the method of the request, e.g. GET or POST?
  • What was the path of the request?
  • Did the request time out?

At first we considered modifying the kamon-spray library, but it works by using AspectJ. That’s great because it means we can use it without making any changes to our application. Unfortunately, it also means that in order to be able to use it, you need to have knowledge of both AspectJ and a very deep understanding of the code you are trying to instrument (in this case, Spray routing). We had neither, so we opted to try something else. However, in the long run, moving spray-kamon-metrics’ functionality into kamon-spray seems like a good idea.

Next, we looked to see if we could just create a new directive that captures the information we wanted. Thus, we could theoretically just do something like:

class ServiceActor extends HttpServiceActor {
  override def receive =
    runRoute {
      withKamonMetrics {

This works most of the time, but if the request results in an error or a rejection, it fails. The reason for this is that in the case of rejections and errors not handled explicitly by the route, the route does not produce the resulting HttpResponse. As shown in figure 1, when you use runRoute, it seals your route with implicitly given rejection and exception handlers. It is these handlers that actually generate the HttpResponse object that is sent to the client.

Figure 1: How HttpService handles rejections and exceptions
How HttpService handles rejections and exceptions

While it is possible to provide custom handlers, it becomes very difficult to manage state (in particular, start time) across all of these different places. We could make the directive itself provide rejection and exception handling, but that departs from the norm and also does not solve the problem with managing state.

In the end, what we decided to do is to replace HttpService with TracingHttpService, which is largely identical to HttpService, the biggest difference being in how it seals routes:

// from HttpService
def sealRoute(route: Route)(implicit eh: ExceptionHandler, rh: RejectionHandler): Route =
  (handleExceptions(eh) & handleRejections(sealRejectionHandler(rh)))(route)

// from TracingHttpService (simplified, no timeout handling included)
def sealRoute(route: Route)(implicit eh: ExceptionHandler, rh: RejectionHandler): Route = {
  mapRequestContext { ctx: RequestContext =r
    val path = ctx.request.uri.path.toString()
    val method =
    val start = System.nanoTime()
    val tagBuilder = Map.newBuilder[String, String]
    tagBuilder += "path" -> path
    tagBuilder += "method" -> method
    ctx.withHttpResponseMapped { response =>
      val duration = System.nanoTime() - start
      tagBuilder += "status-code" -> response.status.intValue.toString
  } {
    (handleExceptions(eh) & handleRejections(sealRejectionHandler(rh)))(route)

As we can see, HttpService.sealRoute is simply a higher order function that wraps a route with exception and rejection handlers. In the case of TracingHttpService, sealRoute just adds another wrap to the mix. It still wraps the route with the handlers, but it adds its own wrapper around that. Before the internally sealed route runs, it records the start time and starts building a set of tags. Once the internal route completes, it records the timing to a Kamon histogram.

Because we include these various tags, the metrics we collect are now much richer. It is now possible to filter and analyse the metrics based on the tags, allowing us to answer questions such as ‘which types of request (method/path) are resulting in errors?’ and ‘What is the average response time for a particular type of request?’.

Additionally, creating our own version of HttpService allowed us to tackle the next issue we had: How do we know when a request times out?

Detecting requests that time out

Due to the asynchronous nature of the Spray server, the way it handles request timeouts may be surprising to newcomers.

Figure 2: How Spray times out routes
How Spray times out routes

Figure 2 presents an overview of how Spray works (in particular, the timeout route itself can time out, resulting in an invocation of a last ditch timed out timeout route). In particular there are couple of things to note:

  1. A route will continue running until it completes, regardless of how long it is taking. This may have an impact performance and resource utilisation. Unfortunately, Spray does not include any sort of mechanism for cooperative cancellation.
  2. Spray invokes the timeout handler via a different mechanism than a standard request.

As a result of this, the instrumentation built into kamon-spray is completely blind to the timeout mechanism. It will record a requested that timed out as if it completed normally, and it will not generate a trace. In fact, that is a reason for EmptyTraceContext present while closing the trace with token showing up in your logs.

We want to measure both cases:

  1. If a request times out, we want to make a note of it so we can see which requests are timing out and with what status code (is the timeout too short for what we need to do, or is hanging due to an error?).
  2. We also want to know about the timeout responses, as they should be aggregated to response time and error count metrics.

To help us account for timeouts, we modify our sealRoute implementation:

def sealRoute(route: Route, timeoutNanos: Long, isTimeout: Boolean)
             (implicit eh: ExceptionHandler, rh: RejectionHandler): Route = {
  mapRequestContext { ctx: RequestContext =>
    val path = ctx.request.uri.path.toString()
    val method =
    val start = System.nanoTime()
    val tagBuilder = Map.newBuilder[String, String]
    tagBuilder += "path" -> path
    tagBuilder += "method" -> method
    ctx.withHttpResponseMapped { response =>
      val duration = System.nanoTime() - start
      tagBuilder += "status-code" -> response.status.intValue.toString
      val timedOut = duration > timeoutNanos
      tagBuilder += "timed-out" -> timedOut.toString
      val realDuration = if (isTimeout) duration + timeoutNanos else duration
  } {
    (handleExceptions(eh) & handleRejections(sealRejectionHandler(rh)))(route)

A couple of notes about this method:

  1. This method is called both when handling the regular route and when handling the timeout route. The isTimeout flag lets us know which if the two we are handling.
  2. We do not know for a fact that a non-timeout response timed out. We use the heuristic that if the duration is greater than the request timeout, the request probably timed out.
  3. Measuring an accurate duration for a timeout route is similarly tricky. The duration we calculate is only for the timeout route and does not include the time that elapsed between when the request arrived and the timeout route was invoked. As an approximation, we simply add the configured request timeout length to the measured duration.

With this code in place, now our TracingHttpService implementation will now measure:

  • Durations for all regular and timeout responses
  • For each response:
    • The request method and path
    • The response status code
    • Did the response time out?

This data gives us a much better picture of what clients are experiencing and helps us identify problematic routes within our services. Now, we just want to know a little more about the metrics the Spray can server itself is collecting.

Getting Spray can server metrics with KamonHttp

The Spray can server automatically collects some statistic about its operation and provides a method for retrieving them. Hooking up these metrics to Kamon essentially requires two steps:

  1. When a server socket is established, set up a job that periodically asks the server for its latest statistics.
  2. Each time those statistics are collected, update a Kamon entity with their values.

This is not difficult to do, but neither is it a one-liner that can be trivially done. Arguably, the trickiest bit is setting up the job that will monitor the server. To review, you set up a new server by sending an Http.Bind message the Spray can’s Http Akka I/O extension.

IO(Http) ! Http.Bind(myServiceActor, interface = "localhost", port = 80)

The tricky bit is that we need to capture the reference to the actor that replies to this message, which the application may never do. Furthermore, if the application does want to get a reference to the responder, the library should not interfere with that. What is the solution?

Proxying Spray

The solution we settled on was to create a new Akka I/O extension that proxies the Spray extension, which sounds much more complex than it actually is. To do this, there is a little bit of boilerplate to ensure that Akka will find the extension, but, beyond that, an Akka extension is just an actor. For KamonHttp, this actor is called SprayProxy, a slightly simplified version of which is listed below:

class SprayProxy extends Actor {
  import constext.system

  private val ioActor = IO(Http)

  override def receive = {
    case x: Http.Bind =>
      val proxied = sender()
      val monitor = context.actorOf(SprayMonitor.props(proxied))
      ioActor.tell(x, monitor)
    case x => ioActor.forward(x)

This class instantiates an instance of Spray’s extension and stores it in ioActor. From that point forward, this actor does one of two things:

  1. If a Http.Bind message arrives, instantiate a SprayMonitor actor, which we will cover next. As part of this, we pass in a reference to the actor that sent the original message. Finally, we send the Http.Bind message to Spray, but do so in a matter so that Spray believes that the monitor actor we just created was the original sender.
  2. For any other message, we simply forward it to Spray without changing the sender, rendering our extension invisible.

Monitoring Spray

Next, let us review what this Spray monitor does. This actor is slightly more complex. It exists in one of two states:

  1. The initial state is binding, which means we have sent the Http.Bind message to Spray and we are waiting to hear the result of the operation.
  2. In the bound state, the server is up and running and we periodically poll it for its statistics.

These two states exist as methods on the actor, each of which returns a Receive. Let’s take a look at binding first.

def bindind: Receive = {
  case x: Http.CommandFailed =>
  case x @ Http.Bound(address) =>

In this state, we have handle two possible messages: Http.Bound and Http.CommandFailed. These indicate whether Spray succeeded in binding a new server. In both cases, we forward the message to the original sender of the Http.Bind message, rendering our proxy effectively invisible. In the case were the bind fails, we simply shut down. In the case where the bind succeeded, we become into the bound state, which we will examine next.

def bound(address: InetSocketAddress): Receive = {
  import context.dispatcher

  val httpListener = sender()

  val updateTask = context.system.scheduler.schedule(
    15 seconds, 15 seconds, httpListener, Http.Getstats)
  val metricsName = s"${address.getHostName}:${address.getPort}"
  val metrics = Kamon.metrics.entity(SprayServerMetrics, metricsName)

    case _: Terminated =>
      Kamon.metrics.removeEntity(metricsName, SprayServerMetrics.category)
    case s: Stats =>

When we become bound, a few things take place:

  1. We capture the reference to the sender of the Http.Bound message. This is the HTTP listener actor which handles all connections to that particular server.
  2. We start watching the listener. When it dies, that means the server has died, so we should stop monitoring and shut down.
  3. We schedule a task that will send the listener a Http.GetStats every 15 seconds (this is configurable in the real code). Keep in mind that when we create this task, it uses the self implicit value as the sender, meaning that as far as the listener is concerned, it is the monitor that is sending these messages.
  4. We instantiate a Kamon entity we created specifically for recording the Spray server metrics. Its name is generated from the host name and port where the server is listening.
  5. Finally we have the partial function that handles the two types of messages that our actor will receive from this point forward:
    • When we get a Terminated message, that means the server has stopped. As a result, we clean up by cancelling the recurring task, removing the Kamon entity, and finally stopping ourselves.
    • In the case where we get new Stats, we update the Kamon entity with the new values.

All in all, this code is relatively straightforward. However, we are not quite done, yet. Updating the Kamon entity’s metrics was not as straightforward as we initially thought.

Reporting the statistics to Kamon

It might seem like having dealt with all of the Akka bits, we would be out of the woods. However, it turned out that what Spray is reporting is not entirely in line with how Kamon’s instruments behave. Spray reports the following statistics:

  • connections, the total number of connections over time
  • open-connections, the number of currently open connections
  • max-open-connections, the maximum number of open connections ever
  • requests, the total number of requests over time
  • open-requests, the number of currently open requests
  • max-open-requests, the maximum number of open requests ever
  • request-timeouts, the total number of request timeouts over time
  • uptime, the current uptime of the server, in nanoseconds

Most of these fit nicely into one of Kamon’s instrument types:

  • connections, requests, and request-timeouts all work will as Kamon counters, which must be strictly increasing, and are reported as time-series data
  • open-connections and open-requests are conceptually gauges, but given that we are already dealing with sampled data, we decide to map these to Kamon histograms.

This leaves:

  • We could have treated max-open-connections and max-open-requests like their non-max counterparts, but given that these values rarely change, we really only want to report the latest value.
  • uptime, which is conceptually a type of counter, but we do not want that to be reported as time-series data, i.e. the server was up 15 seconds in the last 15 seconds. We really want to report the latest value.

So how, did we deal with this? Well, for the most part, we just defined our own entity recorder. However, it does contains a couple of twists worth sharing:

class SprayServerMetrics(instrumentFactory: InstrumentFactory)
  extends GenericEntityRecorder(instrumentFactory) {
  private val stats = new AtomicReference[Stats](new Stats(0.nanoseconds, 0, 0, 0, 0, 0, 0, 0))
  private val connections = counter("connections")
  private val openConnections = histogram("open-connections")
  private val requests = counter("requests")
  private val openRequests = histogram("open-requests")
  private val requestTimeouts = counter("request-timeouts")

  override def collect(collectionContext: CollectionContext): EntitySnapshot

  def updateStats(newStats: Stats): Unit

We keep a copy of the most recent statistics, starting with a value of all zeroes. Next, for the metrics that map nicely to Kamon’s instruments, we do that.

override def collect(collectionContext: CollectionContext): EntitySnapshot = {
  val parentSnapshot = super.collect(collectionContext)
  val metrics = parentSnapshot.metrics ++ Map(
    counterKey("uptime", Time.Nanoseconds)  CounterSnapshot(stats.uptime.toNanos),
    counterKey("max-open-connections")  CounterSnapshot(stats.maxOpenConnections),
    counterKey("max-open-requests")  CounterSnapshot(stats.maxOpenRequests)
  new DefaultEntitySnapshot(metrics)

When it is time for Kamon to collect the values for our entity, that is were things get a little tricky. We need to do a combination of both the default behaviour for the Kamon-friendly statistics along with some custom behaviour for those that are not. Luckily this was not too difficult:

  1. We invoke the parent class’s collect method to get a snapshot that includes all of the Kamon-friendly statistics.
  2. From that snapshot, we can get the recorded instrument snapshot and append to it fabricated instrument snapshots with the values that we want. We use report uptime, max-open-connections, and max-open-requests as counters.
  3. Finally, we construct a new snapshot with our custom metrics.

Last, we just need to be sure to deal with the updates:

def updateStats(newStats: Stats): Unit = {
  connections.increment(newStats.totalConnections - stats.totalConnections)
  requests.increment(newStats.totalRequests - stats.totalRequests)
  requestTimeouts.increment(newStats.requestTimeouts - stats.requestTimeouts)
  stats = newStats

The implementation here should not come as a surprise.

  1. Both open-connections and open-requests are histograms, so we just record their new values.
  2. For connections, requests, and request-timeouts, we simply record how much each of these has increased since the last time.
  3. Finally, we keep a copy of the statistics. Note that we do not have to do anything for the other values as those metrics are generated during collect.

And that is how we take the statistics from Spray to report them from a custom Kamon entity recorder.

Moving forward

We are generally pretty happy with our work in spray-kamon-metrics, but that is not to say there is not room for improvement. A few of ideas come to mind:

  1. Figure out if there is a better way to handle request timeouts.
  2. See if it is possible to implement any of this using AspectJ, making it as simple to use as kamon-spray.
  3. Especially if we can acheive the last goal, perhaps it would be good to merge this into kamon-spray itself.

Can you think of anything else you like the library to do?

posted on September 24, 2015 by
Daniel Solano Gómez