Micrometer SignalFx

SignalFx is a dimensional monitoring system SaaS with a full UI that operates on a push model. It has a rich set of alert “detectors”.

1. Installing

For Gradle, add the following implementation:

implementation 'io.micrometer:micrometer-registry-signalfx:latest.release'

For Maven, add the following dependency:


2. Configuring

The following example configures SignalFx:

SignalFxConfig signalFxConfig = new SignalFxConfig() {
    public String accessToken() {
        return "MYTOKEN";

    public String get(String k) {
        return null; // accept the rest of the defaults

MeterRegistry registry = new SignalFxMeterRegistry(signalFxConfig, Clock.SYSTEM);

There are two distinct sources of API keys in SignalFx.

SignalFxConfig is an interface with a set of default methods. If, in the implementation of get(String k), rather than returning null, you instead bind it to a property source, you can override the default configuration. For example, Micrometer’s Spring Boot support binds properties that are prefixed with management.metrics.export.signalfx directly to the SignalFxConfig:

    access-token: MYTOKEN

    # The interval at which metrics are sent to Ganglia. See Duration.parse for the expected format.
    # The default is 1 minute.
    step: 1m

3. Graphing

This section serves as a quick start to rendering useful representations in SignalFx for metrics originating in Micrometer. See the SignalFx docs for a far more complete reference of what is possible in SignalFx.

3.1. Timers

At each publishing interval, the SignalFx Timer produces several time series in SignalFx:

  • ${name}.avg: A mean latency for the publishing interval.

  • ${name}.count: Throughput per second over the publishing interval.

  • ${name}.totalTime: Total time per second over the publishing interval (used with count) to create aggregable means.

  • ${name}.percentiles: Micrometer calculated percentiles for the publishing interval. One time series is produced for each percentile, with a tag of phi in the range of [0,1].

  • ${name}.histogram: One event is produced for each SLO boundary with a tag of 'le', indicating that it represents a cumulative count of events less than or equal to SLO boundaries over the publishing interval.

To generate an aggregable view of latency in SignalFx, divide totalTime by count:

SignalFx-aggregable latency query

This is accomplished by adding signals for ${name}.totalTime and ${name}.count, adding a formula that divides them, and hiding the inputs to the formula.

SignalFx-rendered timer
Figure 1. Timer latency.

To generate a throughput chart, use the ${name}.count signal:

SignalFx-rendered timer throughput
Figure 2. Timer throughput.

To generate a plot of client-side percentiles, use the ${name}.percentiles signal:

SignalFx-rendered percentiles
Figure 3. Timer Percentiles.

Note that these percentiles are not aggregable. The more dimensions you add to a timer, the less useful these values become.

Finally, if you define SLO boundaries with the fluent builder for Timer, you can view throughput below certain SLO boundaries by using the ${name}.histogram signal. In this example, we set SLO boundaries at 275 (green), 300 (blue), and 500 (purple) milliseconds for a simulated Timer that is recording samples normally distributed around 250 ms. These counts represent the rate/second of samples less than or equal to each SLO boundary.

SignalFx-rendered SLO boundaries
Figure 4. Timer SLO boundaries.

Where the lines converge at various points, it is evident that no sample exceeded the 275 ms SLO boundary.