Adding a new metric type

Data in the Glean SDK is stored in so-called metrics. You can find the full list of implemented metric types in the user overview.

Adding a new metric type involves defining the metric type's API, its persisted and in-memory storage as well as its serialization into the ping payload.


In order for your metric to be usable, you must add it to glean_parser so that instances of your new metric can be instantiated and available to our users.

The documentation for how to do this should live in the glean_parser repository, but in short:

  • Your metric type must be added to the metrics schema.
  • Your metric type must be added as a type in the object model
  • Any new parameters outside of the common metric data must also be added to the schema, and be stored in the object model.
  • You must add tests.

The metric type's API

A new metric type is defined in glean-core/src/glean.udl. Each metric type is its own interface with a constructor and all recording and testing functions defined. It supports built-in types as well as new custom types. See the UniFFI documentation for more.

interface CounterMetric {
    constructor(CommonMetricData meta);

    void add(optional i32 amount = 1);

The implementation of this metric type is defined in its own file under glean-core/src/metrics/, e.g. glean-core/src/metrics/ for a Counter.

Start by defining a structure to hold the metric's metadata:

#[derive(Clone, Debug)]
pub struct CounterMetric {
    meta: Arc<CommonMetricData>,

Implement the MetricType trait to create a metric from the meta data as well as expose the meta data. This also gives you a should_record method on the metric type.

impl MetricType for CounterMetric {
    fn meta(&self) -> &CommonMetricData {

Its implementation should have a way to create a new metric from the common metric data. It should be the similar for all metric types. Additional metric type parameters are passed as additional arguments.

impl CounterMetric {
    pub fn new(meta: CommonMetricData) -> Self {
        Self {
            meta: Arc::new(meta),

Implement each method for the type. The public method should do minimal work synchronously and defer logic & storage functionality to run on the dispatcher. The synchronous implementation should then take glean: &Glean to be able to access the storage.

impl CounterMetric { // same block as above
    pub fn add(&self, amount: i32) {
        let metric = self.clone();
        crate::launch_with_glean(move |glean| metric.add_sync(glean, amount))

    fn add_sync(&self, glean: &Glean, amount: i32) {
        // Always include this check!
        if !self.should_record() {

        // Do error handling here

            .record_with(&self.meta, |old_value| match old_value {
                Some(Metric::Counter(old_value)) => Metric::Counter(old_value + amount),
                _ => Metric::Counter(amount),

Use to record a fixed value or to construct a new value from the currently stored one.

The storage operation makes use of the metric's variant of the Metric enumeration.

The Metric enumeration

Persistence and in-memory serialization as well as ping payload serialization are handled through the Metric enumeration. This is defined in glean-core/src/metrics/ Variants of this enumeration are used in the storage implementation of the metric type.

To add a new metric type, include the metric module and declare its use, then add a new variant to the Metric enum:

mod counter;

// ...

pub use self::counter::CounterMetric;

#[derive(Serialize, Deserialize, Debug, Clone)]
pub enum Metric {
    // ...

Then modify the below implementation and define the right ping section name for the new type. This will be used in the ping payload:

impl Metric {
    pub fn ping_section(&self) -> &'static str {
        match self {
            // ...
            Metric::Counter(_) => "counter",

Finally, define the ping payload serialization (as JSON). In the simple cases where the in-memory representation maps to its JSON representation it is enough to call the json! macro.

impl Metric { // same block as above
    pub fn as_json(&self) -> JsonValue {
        match self {
            // ...
            Metric::Counter(c) => json!(c),

For more complex serialization consider implementing serialization logic as a function returning a serde_json::Value or another object that can be serialized.

For example, the DateTime serializer has the following entry, where get_iso_time_string is a function to convert from the DateTime metric representation to a string:

Metric::Datetime(d, time_unit) => json!(get_iso_time_string(*d, *time_unit)),


Documentation for the new metric type must be added to the user book.

  • Add a new file for your new metric in docs/user/reference/metrics/. Its contents should follow the form and content of the other examples in that folder.
  • Reference that file in docs/user/ so it will be included in the build.
  • Follow the Documentation Contribution Guide.

You must also update the payload documentation with how the metric looks in the payload.


Tests are written in the Language Bindings and tend to just cover basic functionality:

  • The metric returns the correct value when it has no value
  • The metric correctly reports errors
  • The metric returns the correct value when it has value

At this point the metric type will have an auto-generated API in all target languages. This needs to be re-exported in the target language. The following chapters have details on how to do that for the different languages.

Sometimes a metric type needs some additional modifications to expose a language-specific type, apply additional type conversions or add additional functionality. For how to implement additional modifications to the API see the following chapters.