Observability Metrics Reporting

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    Individual request tracing presents a very specific (though isolated) view of the system. In addition, it also makes sense to capture information that aggregates request data (i.e. requests per second), but also data which is not tied to a specific request at all (i.e. resource utilization).

    The deployment situation itself is similar to the request tracer: either applications already have a metrics infrastructure in place or they don’t. The difference is that exposing some kind of metrics is much more common than request based tracing, because most production deployments at least monitor CPU and memory usage (e.g. through JMX).

    Metrics broadly fall into the following categories:

    • Request/Response Metrics (such as requests per second).

    • SDK Metrics (such as how many open collections, various queue lengths).

    • System Metrics (such as cpu usage or garbage collection performance).

    Right now only the first category is implemented by the SDK, more are planned.

    The Default AggregatingMeter

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    The default implementation aggregates and logs request and response metrics. Since the feature is in developer preview, you need to manually enable it on the environment:

    CoreEnvironment environment = CoreEnvironment.builder()

    By default the metrics will be emitted every 10 minutes, but you can customize the emit interval as well:

    CoreEnvironment environment = CoreEnvironment.builder()

    Once enabled, there is no further configuration needed. The AggregatingMeter will emit the collected request statistics every interval. A possible report looks like this (prettified for better readability):


    Each report contains one object for each service that got used and is further separated on a per-node basis so they can be analyzed in isolation.

    For each service / host combination, a total amount of recorded requests is reported, as well as percentiles from a histogram in microseconds. The meta section on top contains information such as the emit interval in seconds so tooling can later calculate numbers like requests per second.

    The AggregatingMeter can be configured on the environment as shown above. The following table shows the currently available properties:

    Table 1. AggregatingMeterConfig Properties
    Property Default Description



    If the AggregatingMeter should be enabled.


    600 seconds

    The interval where found orphans are emitted.

    OpenTelemetry Integration

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    The SDK supports plugging in any OpenTelemetry metrics consumer instead of using the default AggregatingMeter. To do this, first you need to add an additional dependency to your application:


    In addition, you need to add the OpenTelemetry exporter of your choice. As an example this could be the Prometheus exporter:


    Next, you need to initialize your OpenTelemetry Meter. Again, the following example uses prometheus:

    // Build the OpenTelemetry Meter
    MeterSdkProvider meterSdkProvider = OpenTelemetrySdk.getGlobalMeterProvider();
    Meter meter = meterSdkProvider.get("OpenTelemetryMetricsSample");
    // Start the Prometheus HTTP Server
    HTTPServer server = server = new HTTPServer(19090);
    // Register the Prometheus Collector

    Once your meter is initialized, it needs to be wrapped and supplied to the environment:

    ClusterEnvironment environment = ClusterEnvironment.builder().meter(OpenTelemetryMeter.wrap(meter)).build();

    At this point the SDK is hooked up with the OpenTelemetry metrics and will emit them to the exporter. The specific output format is still evolving, but look out for metrics with the cb. prefix: cb.requests and cb.responses. The cb.requests is a counter while the cb.responses is a ValueRecorder which also collects latency information for each request. Each metric contains tags that allow you to group them in different ways, including the service type (e.g. query) or the server hostname.

    Micrometer Integration

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    In addition to OpenTelemetry, we also provide a module so you can hook up the SDK metrics to Micrometer.


    In addition to the facade you also need to include your micrometer implementation of choice. Once you’ve created the a Micrometer MeterRegistry, you need to wrap it and pass it into the environment:

    ClusterEnvironment environment = ClusterEnvironment

    At this point the metrics are hooked up to Micrometer and will be reported as cb.requests (as a Counter) and cb.responses (as a DistributionSummary).