Author RelationalAI-oss
7 Stars
Updated Last
1 Year Ago
Started In
September 2021


Server metrics allow engineers to instrument their code to expose useful information about what is happening within the server (a program running on a single machine).

Metrics are primarily numeric representations of either current state (e.g. how much disk are we using at the moment) or event counters (how many requests did we handle so far).

Currently, two flavors of metrics are supported:

  1. Gauges represent current state of some measure, such as "bytes stored on disk". The value can fluctuate and change over time.
  2. Counters count events of interest or aggregations of values over time. The guarantee is that the value of a counter never decreases and what usually matters is not the value itself but rather how it changes over time.

And two backends are supported: DataDog and Prometheus. See the examples directory for how to set up in either configuration.

Basic operation

The library allows user to construct Counters and Gauges either individually or (preferably) wrapped in MetricCollection structures. Metrics or collections can be registered with either default registry or with custom registries. Upon registration, metrics need to have unique names assigned to them.

Metric registries can be set up to publish the state of the registered metrics to a variety of backends, specifically the library offers:

  1. StatsdExporter that will periodically send UDP messages to the statsd backend
  2. Prometheus compatible http request handler that can serve the state of registered metric on /metrics (or custom) http endpoint.

Metric labels

Metrics can either be scalars (a single number) or they can have one or more labels. This can be thought of as having sparse matrices where many related measures are tracked.

An example of this is http response tracking where we may want to break this down by action (GET/PUT) and http response code. To do this, simply create Gauge or Counter and list all the labels and the value type:

responses_total = Counter(; action=String, response_code=Int64)

To manipulate these metrics, you will need to pass label assignments:

inc!(responses_total; action="GET", response_code=500)
inc!(responses_total; action="PUT", response_code=200)

Keep in mind the following rules:

  • If invalid labels are set when manipulating metrics (e.g. some labels are missing, unkown labels are set or label value is of a wrong type), the library will log an error and drop the operation on the floor. This is to ensure that bugs in the instrumentation won't crash the service in production.
  • Each metric is limited to 200 distinct values (distinguished by unique label-value assignments). When this limit is reached, least recently used cell is deleted. This limit is in place to ensure that poorly written instrumentation code will not be able to leak memory, incur excessive datadog costs (we pay for each cell) or cause performance issues in the instrumentation code.

Instrumenting your code

To instrument your module with server metrics you will first need to create a subclass of ServerMetrics.AbstractMetricCollection that will hold your metrics.

using ServerMetrics

Base.@kwdef struct MyModuleMetrics <: AbstractMetricCollection
    lunches_consumed = Counter()
    hunger_level = Gauge(5.0)  # Hunger starts at 5.0

If you want these metrics to be exposed to production monitoring systems by default, you will want to create a global const instance of this structure and add it to the default metric registry at the module __init__() using the following:

const metrics = MyModuleMetrics()

function __init__()

And then you can instrument your code by manipulating metrics within the metrics instance:

function eat_lunch()
    # ... eat lunch here ...

function exercise_little()
    # ... do the exercises ...

function exercise_a_lot()
    inc!(metrics.hunger_level, 3.0)
    # ... do the exercises ...

Naming conventions

See as a starting point.

Important points:

  • Units should be included as part of the metric name. For example, a gauge metric tracking memory might be called memory_usage_bytes.
  • The first word should indicate what part of the system is exporting the metric. It may make sense to make these multi-tiered (e.g., pager_memory_usage_bytes, julia_memory_usage_bytes)
  • You should never change the type of an existing metric, for backward compatibility reasons. (Imagine a rollout where multiple versions are running and exporting different types to the same metric; not a good look). It's better to create a metric with a new name to change types.

Testing locally

You can use Prometheus and Grafana to test metrics locally.

Depending on platform (assuming linux), follow the default installation instructions for both:

Then configure prometheus to scrape the local instance by adding the following section into prometheus.yml:

  - job_name: 'your-local-job'
    scrape_interval: 1s
      - targets: ['localhost:<your-port>']

Upon starting grafana, you can connect it to prometheus data source which should give you access to all exported metrics.

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