Introduction

Glean is a modern approach for a telemetry library and is part of the Glean project.

There are two implementations of Glean, with support for 5 different programming languages in total. Both implementations strive to contain the same features with similar, but idiomatic APIs.

Unless clearly stated otherwise, regard the text in this book as valid for both clients and all the supported programming languages and environments.

The Glean SDK

The Glean SDK is an implementation of Glean in Rust, with language bindings for Kotlin, Python, Rust and Swift.

For development documentation on the Glean SDK, refer to the Glean SDK development book.

To report issues or request changes on the Glean SDK, file a bug in Bugzilla in Data Platform & Tools :: Glean: SDK.

Glean.js

Glean.js is an implementation of Glean in JavaScript. Currently, it only has support for usage in web extensions.

For development documentation on Glean.js, refer to the Glean.js development documentation.

To report issues or request changes on Glean.js, file a bug in Bugzilla in Data Platform & Tools :: Glean.js.

Note Glean.js is still in development and does not provide all the features the Glean SDK does. Feature parity will be worked on after initial validation. Do not hesitate to file a bug if you want to use Glean.js and is missing some key Glean feature.

Sections

User Guides

This section of the book contains mostly step-by-step guides and essays detailing how to achieve specific tasks with Glean.

It contains guides on the first steps of integrating Glean into your project, choosing the right metric type for you, debugging products that use Glean and Glean's built-in error reporting mechanism.

If you want to start using Glean to report data, this is the section you should read.

API Reference

This section of the book contains reference pages for Glean’s user facing APIs.

If you are looking for information a specific Glean API, this is the section you should check out.

Language Binding Information

This section contains guides and essays regarding specific usage information and possibilities in each of Glean's language bindings.

Check out this section for information on the language binding you are using.

Appendix

Glossary

In this book we use a lot of Glean specific terminology. In the glossary, we go through many of the terms used throughout this book and describe exactly what we mean when we use them.

Changelog

This section contains detailed notes about changes in Glean, per release.

This Week in Glean

“This Week in Glean” is a series of blog posts that the Glean Team at Mozilla is using to try to communicate better about our work. They could be release notes, documentation, hopes, dreams, or whatever: so long as it is inspired by Glean.

Contribution Guidelines

This section contains detailed information on where and how to include new content to this book.

Contact

To contact the Glean team you can:

• Find us in the #glean channel on chat.mozilla.org.
• Send an email to glean-team@mozilla.com.
• The Glean SDK team is: :janerik, :dexter, :travis, :mdroettboom, :gfritzsche, :chutten, :brizental.

License

Glean.js and the Glean SDK Source Code is subject to the terms of the Mozilla Public License v2.0. You can obtain a copy of the MPL at https://mozilla.org/MPL/2.0/.

Adding Glean to your project

This page describes the steps for adding Glean to a project. This does not include the steps for adding a new metrics or pings to an existing Glean integration. If that is what your are looking for, refer to the Adding new metrics or the Adding new pings guide.

Glean integration checklist

The Glean integration checklist can help to ensure your Glean SDK-using product is meeting all of the recommended guidelines.

Products (applications or libraries) using the Glean SDK to collect telemetry must:

1. Integrate the Glean SDK into the build system. Since the Glean SDK does some code generation for your metrics at build time, this requires a few more steps than just adding a library.

2. Go through data review process for all newly collected data.

3. Ensure that telemetry coming from automated testing or continuous integration is either not sent to the telemetry server or tagged with the automation tag using the sourceTag feature.

4. At least one week before releasing your product, file a data engineering bug to enable your product's application id and have your metrics be indexed by the Glean Dictionary.

Additionally, applications (but not libraries) must:

1. Request a data review to add Glean to your application (since it can send data out of the box).

2. Initialize Glean as early as possible at application startup.

3. Provide a way for users to turn data collection off (e.g. providing settings to control Glean.setUploadEnabled()). The exact method used is application-specific.

Looking for an integration guide?

Step-by-step tutorials for each supported language/platform, can be found on the specific integration guides:

Adding Glean to your Kotlin project

This page provides a step-by-step guide on how to integrate the Glean library into a Kotlin project.

Nevertheless this is just one of the required steps for integrating Glean successfully into a project. Check you the full Glean integration checklist for a comprehensive list of all the steps involved in doing so.

Currently, these bindings only support the Android platform.

Setting up the dependency

The Glean SDK is published on maven.mozilla.org. To use it, you need to add the following to your project's top-level build file, in the allprojects block (see e.g. Glean SDK's own build.gradle):

repositories {
maven {
url "https://maven.mozilla.org/maven2"
}
}


Each module that uses Glean SDK needs to specify it in its build file, in the dependencies block. Add this to your Gradle configuration:

implementation "org.mozilla.components:service-glean:{latest-version}"

Pick the correct version

The {latest-version} placeholder in the above link should be replaced with the version of Android Components used by the project.

The Glean SDK is released as part of android-components. Therefore, it follows android-components' versions. The android-components release page can be used to determine the latest version.

For example, if version 33.0.0 is used, then the include directive becomes:

implementation "org.mozilla.components:service-glean:33.0.0"

Size impact on the application APK

The Glean SDK APK ships binary libraries for all the supported platforms. Each library file measures about 600KB. If the final APK size of the consuming project is a concern, please enable ABI splits.

Dependency for local testing

Due to its use of a native library you will need additional setup to allow local testing.

First add a new configuration to your build.gradle, just before your dependencies:

configurations {
jnaForTest
}


Then add the following lines to your dependencies block:

jnaForTest "net.java.dev.jna:jna:5.6.0@jar"
testImplementation files(configurations.jnaForTest.copyRecursive().files)
testImplementation "org.mozilla.telemetry:glean-forUnitTests:${project.ext.glean_version}"  Note: Always use org.mozilla.telemetry:glean-forUnitTests. This package is standalone and its version will be exported from the main Glean package automatically. Setting up metrics and pings code generation In order for the Glean SDK to generate an API for your metrics, two Gradle plugins must be included in your build: The Glean Gradle plugin is distributed through Mozilla's Maven, so we need to tell your build where to look for it by adding the following to the top of your build.gradle: buildscript { repositories { // Include the next clause if you are tracking snapshots of android components maven { url "https://snapshots.maven.mozilla.org/maven2" } maven { url "https://maven.mozilla.org/maven2" } dependencies { classpath "org.mozilla.components:tooling-glean-gradle:{android-components-version}" } } }  Important As above, the {android-components-version} placeholder in the above link should be replaced with the version number of android components used in your project. The JetBrains Python plugin is distributed in the Gradle plugin repository, so it can be included with: plugins { id "com.jetbrains.python.envs" version "0.0.26" }  Right before the end of the same file, we need to apply the Glean Gradle plugin. Set any additional parameters to control the behavior of the Glean Gradle plugin before calling apply plugin. // Optionally, set any parameters to send to the plugin. ext.gleanGenerateMarkdownDocs = true apply plugin: "org.mozilla.telemetry.glean-gradle-plugin"  Earlier versions Note: Earlier versions of Glean used a Gradle script (sdk_generator.gradle) rather than a Gradle plugin. Its use is deprecated and projects should be updated to use the Gradle plugin as described above. Offline builds The Glean Gradle plugin has limited support for offline builds of applications that use the Glean SDK. Adding Glean to your Swift project This page provides a step-by-step guide on how to integrate the Glean library into a Swift project. Nevertheless this is just one of the required steps for integrating Glean successfully into a project. Check you the full Glean integration checklist for a comprehensive list of all the steps involved in doing so. Currently, these bindings only support the iOS platform. Requirements • Python >= 3.6. Setting up the dependency The Glean SDK can be consumed through Carthage, a dependency manager for macOS and iOS. For consuming the latest version of the Glean SDK, add the following line to your Cartfile: github "mozilla/glean" "{latest-version}"  Pick the correct version The {latest-version} placeholder should be replaced with the version number of the latest Glean SDK release. You can find the version number on the release page. Then check out and build the new dependency: carthage update --platform iOS  Integrating with the build system For integration with the build system you can follow the Carthage Quick Start steps. 1. After building the dependency one drag the built .framework binaries from Carthage/Build/iOS into your application's Xcode project. 2. On your application targets' Build Phases settings tab, click the + icon and choose New Run Script Phase. If you already use Carthage for other dependencies, extend the existing step. Create a Run Script in which you specify your shell (ex: /bin/sh), add the following contents to the script area below the shell: /usr/local/bin/carthage copy-frameworks  3. Add the path to the Glean framework under "Input Files": $(SRCROOT)/Carthage/Build/iOS/Glean.framework

4. Add the paths to the copied framework to the "Output Files":

$(BUILT_PRODUCTS_DIR)/$(FRAMEWORKS_FOLDER_PATH)/Glean.framework


Combined usage with application-services

If your application uses both the Glean SDK and application-services you can use a combined release to reduce the memory usage and startup time impact.

In your Cartfile require only application-services, e.g.:

github "mozilla/application-services" ~> "{latest-version}"

Pick the correct version

The {latest-version} placeholder should be replaced with the version number of the latest application-services release. You can find the version number on the release page.

Then check out and build the new dependency:

carthage update --platform iOS


Setting up metrics and pings code generation

The metrics.yaml file is parsed at build time and Swift code is generated. Add a new metrics.yaml file to your Xcode project.

Follow these steps to automatically run the parser at build time:

1. Download the sdk_generator.sh script from the Glean repository:
https://raw.githubusercontent.com/mozilla/glean/{latest-release}/glean-core/ios/sdk_generator.sh

Pick the correct version

As above, the {latest-version} placeholder should be replaced with the version number of Glean SDK release used in this project.

1. Add the sdk_generator.sh file to your Xcode project.

2. On your application targets' Build Phases settings tab, click the + icon and choose New Run Script Phase. Create a Run Script in which you specify your shell (ex: /bin/sh), add the following contents to the script area below the shell:

bash $PWD/sdk_generator.sh  Using with application-services If you are using the combined release of application-services and the Glean SDK you need to set the namespace to MozillaAppServices, e.g.: bash$PWD/sdk_generator.sh --glean-namespace MozillaAppServices

1. Add the path to your metrics.yaml and (optionally) pings.yaml under "Input files":

$(SRCROOT)/{project-name}/metrics.yaml$(SRCROOT)/{project-name}/pings.yaml

2. Add the path to the generated code file to the "Output Files":

$(SRCROOT)/{project-name}/Generated/Metrics.swift  The generated API The parser now generates a single file called Metrics.swift (since Glean v31.0.0). 1. If you are using Git, add the following lines to your .gitignore file: .venv/ {project-name}/Generated  This will ignore files that are generated at build time by the sdk_generator.sh script. They don't need to be kept in version control, as they can be re-generated from your metrics.yaml and pings.yaml files. Glean and embedded extensions Metric collection is a no-op in application extensions and Glean will not run. Since extensions run in a separate sandbox and process from the application, Glean would run in an extension as if it were a completely separate application with different client ids and storage. This complicates things because Glean doesn’t know or care about other processes. Because of this, Glean is purposefully prevented from running in an application extension and if metrics need to be collected from extensions, it's up to the integrating application to pass the information to the base application to record in Glean. Adding Glean to your Python project This page provides a step-by-step guide on how to integrate the Glean library into a Python project. Nevertheless this is just one of the required steps for integrating Glean successfully into a project. Check you the full Glean integration checklist for a comprehensive list of all the steps involved in doing so. Setting up the dependency We recommend using a virtual environment for your work to isolate the dependencies for your project. There are many popular abstractions on top of virtual environments in the Python ecosystem which can help manage your project dependencies. The Glean SDK Python bindings currently have prebuilt wheels on PyPI for Windows (i686 and x86_64), Linux/glibc (x86_64) and macOS (x86_64). For other platforms, including BSD or Linux distributions that don't use glibc, such as Alpine Linux, the glean_sdk package will be built from source on your machine. This requires that Cargo and Rust are already installed. The easiest way to do this is through rustup. Once you have your virtual environment set up and activated, you can install the Glean SDK into it using: $ python -m pip install glean_sdk

Important

Installing Python wheels is still a rapidly evolving feature of the Python package ecosystem. If the above command fails, try upgrading pip:

python -m pip install --upgrade pip

Important

The Glean SDK Python bindings make extensive use of type annotations to catch type related errors at build time. We highly recommend adding mypy to your continuous integration workflow to catch errors related to type mismatches early.

Consuming YAML registry files

For Python, the metrics.yaml file must be available and loaded at runtime.

If your project is a script (i.e. just Python files in a directory), you can load the metrics.yaml using:

from glean import load_metrics

metrics = load_metrics("metrics.yaml")

# Use a metric on the returned object
metrics.your_category.your_metric.set("value")


If your project is a distributable Python package, you need to include the metrics.yaml file using one of the myriad ways to include data in a Python package and then use pkg_resources.resource_filename() to get the filename at runtime.

from glean import load_metrics
from pkg_resources import resource_filename

metrics = load_metrics(resource_filename(__name__, "metrics.yaml"))

# Use a metric on the returned object
metrics.your_category.your_metric.set("value")


Automation steps

Documentation

The documentation for your application or library's metrics and pings are written in metrics.yaml and pings.yaml.

For Mozilla projects, this SDK documentation is automatically published on the Glean Dictionary. For non-Mozilla products, it is recommended to generate markdown-based documentation of your metrics and pings into the repository. For most languages and platforms, this transformation can be done automatically as part of the build. However, for some language bindings the integration to automatically generate docs is an additional step.

The Glean SDK provides a commandline tool for automatically generating markdown documentation from your metrics.yaml and pings.yaml files. To perform that translation, run glean_parser's translate command:

python3 -m glean_parser translate -f markdown -o docs metrics.yaml pings.yaml


To get more help about the commandline options:

python3 -m glean_parser translate --help


We recommend integrating this step into your project's documentation build. The details of that integration is left to you, since it depends on the documentation tool being used and how your project is set up.

Metrics linting

Glean includes a "linter" for metrics.yaml and pings.yaml files called the glinter that catches a number of common mistakes in these files.

As part of your continuous integration, you should run the following on your metrics.yaml and pings.yaml files:

python3 -m glean_parser glinter metrics.yaml pings.yaml


Parallelism

All of the Glean SDK's target languages use a separate worker thread to do most of its work, including any I/O. This thread is fully managed by the Glean SDK as an implementation detail. Therefore, users should feel free to use the Glean SDK wherever it is most convenient, without worrying about the performance impact of updating metrics and sending pings.

Since the Glean SDK performs disk and networking I/O, it tries to do as much of its work as possible on separate threads and processes. Since there are complex trade-offs and corner cases to support Python parallelism, it is hard to design a one-size-fits-all approach.

Default behavior

When using the Python bindings, most of the Glean SDK's work is done on a separate thread, managed by the Glean SDK itself. The Glean SDK releases the Global Interpreter Lock (GIL) for most of its operations, therefore your application's threads should not be in contention with the Glean SDK's worker thread.

The Glean SDK installs an atexit handler so that its worker thread can cleanly finish when your application exits. This handler will wait up to 30 seconds for any pending work to complete.

By default, ping uploading is performed in a separate child process. This process will continue to upload any pending pings even after the main process shuts down. This is important for commandline tools where you want to return control to the shell as soon as possible and not be delayed by network connectivity.

Cases where subprocesses aren't possible

The default approach may not work with applications built using PyInstaller or similar tools which bundle an application together with a Python interpreter making it impossible to spawn new subprocesses of that interpreter. For these cases, there is an option to ensure that ping uploading occurs in the main process. To do this, set the allow_multiprocessing parameter on the glean.Configuration object to False.

Using the multiprocessing module

Additionally, the default approach does not work if your application uses the multiprocessing module for parallelism. The Glean SDK can not wait to finish its work in a multiprocessing subprocess, since atexit handlers are not supported in that context.
Therefore, if the Glean SDK detects that it is running in a multiprocessing subprocess, all of its work that would normally run on a worker thread will run on the main thread. In practice, this should not be a performance issue: since the work is already in a subprocess, it will not block the main process of your application.

Adding Glean to your JavaScript project

This page provides a step-by-step guide on how to integrate the Glean.js library into a JavaScript project.

Nevertheless this is just one of the required steps for integrating Glean successfully into a project. Check you the full Glean integration checklist for a comprehensive list of all the steps involved in doing so.

Currently, these bindings support collecting data from Browser Extensions (cross-browser), Node.js1 applications or scripts and Qt/QML applications2.

1

The Node.js SDK does not have persistent storage yet. This means, Glean does not persist state throughout application runs. For updates on the implementation of this feature in Node.js, follow Bug 1728807.

2

For information on adding Glean to a Qt/QML application, refer to the Qt specific documentation.

Requirements

• Node.js >= 12.20.0
• npm >= 7.0.0
• Webpack >= 5.34.0
• Python >= 3.6

Browser extension specific requirements

• webextension-polyfill >= 0.8.0
• Glean.js assumes a Promise-based browser API: Firefox provides such an API by default. Other browsers may require using a polyfill library such us webextension-polyfill when using Glean in browser extensions
• Host permissions to the telemetry server
• Only necessary if the defined server endpoint denies cross-origin requests
• The default incoming.telemetry.mozilla.org server does require this type of permission. Follow Bug 1676676 for updates on this requirement
• "storage" API permissions

Browser extension example configuration

The manifest.json file of the sample browser extension available on the mozilla/glean.js repository provides an example on how to define the above permissions as well as how and where to load the webextension-polyfill script.

Setting up the dependency

The Glean.js package is distributed as an npm package @mozilla/glean.

Install Glean.js in your JavaScript project, by running:

npm install @mozilla/glean


Then import Glean into your project:

// Glean.js for browser extensions
//
// esm
import Glean from "@mozilla/glean/webext";
// cjs
const { default: Glean } = require("@mozilla/glean/webext");

// Glean.js for Node.js
//
// esm
import Glean from "@mozilla/glean/node";
// cjs
const { default: Glean } = require("@mozilla/glean/node");


The currently available entry points are:

• @mozilla/glean/webext
• @mozilla/glean/node

Browser extension security considerations

In case of privilege-escalation attack into the context of the web extension using Glean, the malicious scripts would be able to call Glean APIs or use the browser.storage.local APIs directly. That would be a risk to Glean data, but not caused by Glean. Glean-using extensions should be careful not to relax the default Content-Security-Policy that generally prevents these attacks.

Common import errors

"Cannot find module '@mozilla/glean'"

Glean.js does not have a main package entry point. Instead it relies on a series of entry points depending on the platform you are targeting.

In order to import Glean use:

import Glean from '@mozilla/glean/{your-platform}'


"Module not found: Error: Can't resolve '@mozilla/glean/webext' in '...'"

Glean.js relies on Node.js' subpath exports feature to define multiple package entry points.

Please make sure that you are using a supported Node.js runtime and also make sure the tools you are using support this Node.js feature.

Setting up metrics and pings code generation

In JavaScript, the metrics and pings definitions must be parsed at build time. The @mozilla/glean package exposes glean_parser through the glean script.

To parse your YAML registry files using this script, define a new script in your package.json file:

{
// ...
"scripts": {
// ...
"build:glean": "glean translate path/to/metrics.yaml path/to/pings.yaml -f javascript -o path/to/generated",
// Or, if you are building for a Typescript project
"build:glean": "glean translate path/to/metrics.yaml path/to/pings.yaml -f typescript -o path/to/generated"
}
}


Then run this script by calling:

npm run build:glean


Automation steps

Documentation

Prefer using the Glean Dictionary

While it is still possible to generate Markdown documentation, if working on a public Mozilla project rely on the Glean Dictionary for documentation. Your product will be automatically indexed by the Glean Dictionary after it gets enabled in the pipeline.

One of the commands provided by glean_parser allows users to generate Markdown documentation based on the contents of their YAML registry files. To perform that translation, use the translate command with a different output format, as shown below.

In your package.json, define the following script:

{
// ...
"scripts": {
// ...
"docs:glean": "glean translate path/to/metrics.yaml path/to/pings.yaml -f markdown -o path/to/docs",
}
}


Then run this script by calling:

npm run docs:glean


YAML registry files linting

Glean includes a "linter" for the YAML registry files called the glinter that catches a number of common mistakes in these files. To run the linter use the glinter command.

In your package.json, define the following script:

{
// ...
"scripts": {
// ...
"lint:glean": "glean glinter path/to/metrics.yaml path/to/pings.yaml",
}
}


Then run this script by calling:

npm run lint:glean


Adding Glean to your Qt/QML project

This page provides a step-by-step guide on how to integrate the Glean.js library into a Qt/QML project.

Nevertheless this is just one of the required steps for integrating Glean successfully into a project. Check you the full Glean integration checklist for a comprehensive list of all the steps involved in doing so.

Requirements

• Python >= 3.6
• Qt >= 5.15.2

Setting up the dependency

Glean.js' Qt/QML build is distributed as an asset with every Glean.js release. In order to download the latest version visit https://github.com/mozilla/glean.js/releases/latest.

Glean.js is a QML module, so extract the contents of the downloaded file wherever you keep your other modules. Make sure that whichever directory that module is placed in, is part of the QML Import Path.

After doing that, import Glean like so:

import org.mozilla.Glean <version>


Picking the correct version

The <version> number is the version of the release you downloaded minus its patch version. For example, if you downloaded Glean.js version 0.15.0 your import statement will be:

import org.mozilla.Glean 0.15


Consuming YAML registry files

Qt/QML projects need to setup metrics and pings code generation manually.

First install the glean_parser CLI tool.

pip install glean_parser


Make sure you have the correct glean_parser version!

Qt/QML support was added to glean_parser in version 3.5.0.

Then call glean_parser from the command line:

glean_parser translate path/to/metrics.yaml path/to/pings.yaml \
-f javascript \
-o path/to/generated/files \
--option platform=qt \
--option version=0.15


The translate command will takes a list of YAML registry file paths and an output path and parse the given YAML registry files into QML JavaScript files.

The generated folder will be a QML module. Make sure wherever the generated module is placed is also part of the QML Import Path.

Notice that when building for Qt/QML it is mandatory to give the translate command two extra options.

--option platform=qt

This option is what changes the output file from standard JavaScript to QML JavaScript.

--option version=<version>

The version passed to this option will be the version of the generated QML module.

Automation steps

Documentation

Prefer using the Glean Dictionary

While it is still possible to generate Markdown documentation, if working on a public Mozilla project rely on the Glean Dictionary for documentation. Your product will be automatically indexed by the Glean Dictionary after it gets enabled in the pipeline.

One of the commands provided by glean_parser allows users to generate Markdown documentation based on the contents of their YAML registry files. To perform that translation, use the translate command with a different output format, as shown below.

glean_parser translate path/to/metrics.yaml path/to/pings.yaml \
-f markdown \
-o path/to/docs


YAML registry files linting

glean_parser includes a "linter" for the YAML registry files called the glinter that catches a number of common mistakes in these files. To run the linter use the glinter command.

glean_parser glinter path/to/metrics.yaml path/to/pings.yaml


Debugging

By default, the Glean.js QML module uses a minified version of the Glean.js library. It may be useful to use the unminified version of the library in order to get proper line numbers and function names when debugging crashes.

The bundle provided contains the unminified version of the library. In order to use it, open the glean.js file inside the included module and change the line:

.import "glean.lib.js" as Glean


to

.import "glean.dev.js" as Glean


Troubleshooting

submitPing may cause crashes when debugging iOS devices

The submitPing function hits a known bug in the Qt JavaScript interpreter.

This bug is only reproduced in iOS devices, it does not happen in emulators. It also only happens when using the Qt debug library for iOS.

There is no way around this bug other than avoiding the Qt debug library for iOS altogether until it is fixed. Refer to the the Qt debugging documentation on how to do that.

Integrating Glean for product managers

This chapter provides guidance for planning the work involved in integrating Glean into your product, for internal Mozilla customers. For a technical coding perspective, see adding Glean to your project.

Glean is the standard telemetry platform required for all new Mozilla products. While there are some upfront costs to integrating Glean in your product, this pays off in easier long-term maintenance and a rich set of self-serve analysis tools. The Glean SDK team is happy to support your telemetry integration and make it successful. Find us in #glean or email glean-team@mozilla.com.

Building a telemetry plan

The Glean SDK provides support for answering basic product questions out-of-the-box, such as daily active users, product version and platform information.
However, it is also a good idea to have a sense of any additional product-specific questions you are trying to answer with telemetry, and, when possible, in collaboration with a data scientist.
This of course helps for your own planning, but is also invaluable for the Glean SDK team to support you, since we will understand the ultimate goals of your product's telemetry and ensure the design will meet those goals and we can identify any new features that may be required. It is best to frame this document in the form of questions and use cases rather than as specific data points and schemas.

Integrating the Glean SDK into your product

The technical steps for integrating the Glean SDK in your product are documented in its own chapter for supported platforms. We recommend having a member of the Glean SDK team review this integration to catch any potential pitfalls.

(Optional) Adapting Glean to your platform

The Glean SDK is a collection of cross platform libraries and tools that facilitate collection of Glean conforming telemetry from applications.
Consult the list of the currently supported platforms and languages. If your product's tech stack isn't currently supported, please reach out to the Glean team: significant work will be required to create a new integration.
In previous efforts, this has ranged from 1 to 3 months FTE of work, so it is important to plan for this work well in advance. While the first phase of this work generally requires the specialized expertise of the Glean SDK team, the second half can benefit from outside developers to move faster.

(Optional) Designing ping submission

The Glean SDK periodically sends telemetry to our servers in a bundle known as a "ping".
For mobile applications with common interaction models, such as web browsers, the Glean SDK provides basic pings out-of-the-box. For other kinds of products, it may be necessary to carefully design what triggers the submission of a ping. It is important to have a solid telemetry plan (see above) so we can make sure the ping submission will be able to answer the telemetry questions required of the product.

(Optional) New metric types

The Glean SDK has a number of different metric types that it can collect.
Metric types provide "guardrails" to make sure that telemetry is being collected correctly, and to present the data at analysis time more automatically. Occasionally, products need to collect data that doesn't fit neatly into one of the available metric types. Glean has a process to request and introduce more metric types and we will work with you to design something appropriate. This design and implementation work is at least 4 weeks, though we are working on the foundation to accelerate that. Having a telemetry plan (see above) will help to identify this work early.

Adding new metrics

Process overview

When adding a new metric, the process is:

• Consider the question you are trying to answer with this data, and choose the metric type and parameters to use.
• Add a new entry to metrics.yaml.
• Add code to your project to record into the metric by calling the Glean SDK.

Important: Any new data collection requires documentation and data-review. This is also required for any new metric automatically collected by the Glean SDK.

Choosing a metric type

The following is a set of questions to ask about the data being collected to help better determine which metric type to use.

Is it a single measurement?

If the value is true or false, use a boolean metric.

If the value is a string, use a string metric. For example, to record the name of the default search engine.

Beware: string metrics are exceedingly general, and you are probably best served by selecting the most specific metric for the job, since you'll get better error checking and richer analysis tools for free. For example, avoid storing a number in a string metric --- you probably want a counter metric instead.

If you need to store multiple string values in a metric, use a string list metric. For example, you may want to record the list of other Mozilla products installed on the device.

For all of the metric types in this section that measure single values, it is especially important to consider how the lifetime of the value relates to the ping it is being sent in. Since these metrics don't perform any aggregation on the client side, when a ping containing the metric is submitted, it will contain only the "last known" value for the metric, potentially resulting in data loss. There is further discussion of metric lifetimes below.

Are you measuring user behavior?

For tracking user behavior, it is usually meaningful to know the over of events that lead to the use of a feature. Therefore, for user behavior, an event metric is usually the best choice.

Be aware, however, that events can be particularly expensive to transmit, store and analyze, so should not be used for higher-frequency measurements.

Are you counting things?

If you want to know how many times something happened, use a counter metric. If you are counting a group of related things, or you don't know what all of the things to count are at build time, use a labeled counter metric.

If you need to know how many times something happened relative to the number of times something else happened, use a rate metric.

If you need to know when the things being counted happened relative to other things, consider using an event.

Are you measuring time?

If you need to record an absolute time, use a datetime metric. Datetimes are recorded in the user's local time, according to their device's real time clock, along with a timezone offset from UTC. Datetime metrics allow specifying the resolution they are collected at, and to stay lean, they should only be collected at the minimum resolution required to answer your question.

If you need to record how long something takes you have a few options.

If you need to measure the total time spent doing a particular task, look to the timespan metric. Timespan metrics allow specifying the resolution they are collected at, and to stay lean, they should only be collected at the minimum resolution required to answer your question. Note that this metric should only be used to measure time on a single thread. If multiple overlapping timespans are measured for the same metric, an invalid state error is recorded.

If you need to measure the relative occurrences of many timings, use a timing distribution. It builds a histogram of timing measurements, and is safe to record multiple concurrent timespans on different threads.

If you need to know the time between multiple distinct actions that aren't a simple "begin" and "end" pair, consider using an event.

For how long do you need to collect this data?

Think carefully about how long the metric will be needed, and set the expires parameter to disable the metric at the earliest possible time. This is an important component of Mozilla's lean data practices.

When the metric passes its expiration date (determined at build time), it will automatically stop collecting data.

When a metric's expiration is within in 14 days, emails will be sent from telemetry-alerts@mozilla.com to the notification_emails addresses associated with the metric. At that time, the metric should be removed, which involves removing it from the metrics.yaml file and removing uses of it in the source code. Removing a metric does not affect the availability of data already collected by the pipeline.

If the metric is still needed after its expiration date, it should go back for another round of data review to have its expiration date extended.

Important: Ensure that telemetry alerts are received and are reviewed in a timely manner. Expired metrics don't record any data, extending or removing a metric should be done in time. Consider adding both a group email address and an individual who is responsible for this metric to the notification_emails list.

When should the Glean SDK automatically clear the measurement?

The lifetime parameter of a metric defines when its value will be cleared. There are three lifetime options available:

ping (default)

The metric is cleared each time it is submitted in the ping. This is the most common case, and should be used for metrics that are highly dynamic, such as things computed in response to the user's interaction with the application.

application

The metric is related to an application run, and is cleared after the application restarts and any Glean-owned ping, due at startup, is submitted. This should be used for things that are constant during the run of an application, such as the operating system version. In practice, these metrics are generally set during application startup. A common mistake--- using the ping lifetime for these type of metrics---means that they will only be included in the first ping sent during a particular run of the application.

user

Reach out to the Glean team before using this.

The metric is part of the user's profile and will live as long as the profile lives. This is often not the best choice unless the metric records a value that really needs to be persisted for the full lifetime of the user profile, e.g. an identifier like the client_id, the day the product was first executed. It is rare to use this lifetime outside of some metrics that are built in to the Glean SDK.

While lifetimes are important to understand for all metric types, they are particularly important for the metric types that record single values and don't aggregate on the client (boolean, string, labeled_string, string_list, datetime and uuid), since these metrics will send the "last known" value and missing the earlier values could be a form of unintended data loss.

A lifetime example

Let's work through an example to see how these lifetimes play out in practice. Let's suppose we have a user preference, "turbo mode", which defaults to false, but the user can turn it to true at any time. We want to know when this flag is true so we can measure its affect on other metrics in the same ping. In the following diagram, we look at a time period that sends 4 pings across two separate runs of the application. We assume here, that like the Glean SDK's built-in metrics ping, the developer writing the metric isn't in control of when the ping is submitted.

In this diagram, the ping measurement windows are represented as rectangles, but the moment the ping is "submitted" is represented by its right edge. The user changes the "turbo mode" setting from false to true in the first run, and then toggles it again twice in the second run.

• A. Ping lifetime, set on change: The value isn't included in Ping 1, because Glean doesn't know about it yet. It is included in the first ping after being recorded (Ping 2), which causes it to be cleared.

• B. Ping lifetime, set on init and change: The default value is included in Ping 1, and the changed value is included in Ping 2, which causes it to be cleared. It therefore misses Ping 3, but when the application is started, it is recorded again and it is included in Ping 4. However, this causes it to be cleared again and it is not in Ping 5.

• C. Application lifetime, set on change: The value isn't included in Ping 1, because Glean doesn't know about it yet. After the value is changed, it is included in Pings 2 and 3, but then due to application restart it is cleared, so it is not included until the value is manually toggled again.

• D. Application, set on init and change: The default value is included in Ping 1, and the changed value is included in Pings 2 and 3. Even though the application startup causes it to be cleared, it is set again, and all subsequent pings also have the value.

• E. User, set on change: The default value is missing from Ping 1, but since user lifetime metrics aren't cleared unless the user profile is reset (e.g. on Android, when the product is uninstalled), it is included in all subsequent pings.

• F. User, set on init and change: Since user lifetime metrics aren't cleared unless the user profile is reset, it is included in all subsequent pings. This would be true even if the "turbo mode" preference were never changed again.

Note that for all of the metric configurations, the toggle of the preference off and on during Ping 4 is completely missed. If you need to create a ping containing one, and only one, value for this metric, consider using a custom ping to create a ping whose lifetime matches the lifetime of the value.

What if none of these lifetimes are appropriate?

If the timing at which the metric is sent in the ping needs to closely match the timing of the metrics value, the best option is to use a custom ping to manually control when pings are sent.

This is especially useful when metrics need to be tightly related to one another, for example when you need to measure the distribution of frame paint times when a particular rendering backend is in use. If these metrics were in different pings, with different measurement windows, it is much harder to do that kind of reasoning with much certainty.

What should this new metric be called?

Metric names have a maximum length of 30 characters.

Reuse names from other applications

There's a lot of value using the same name for analogous metrics collected across different products. For example, BigQuery makes it simple to join columns with the same name across multiple tables. Therefore, we encourage you to investigate if a similar metric is already being collected by another product. If it is, there may be an opportunity for code reuse across these products, and if all the projects are using the Glean SDK, it's easy for libraries to send their own metrics. If sharing the code doesn't make sense, at a minimum we recommend using the same metric name for similar actions and concepts whenever possible.

Make names unique within an application

Metric identifiers (the combination of a metric's category and name) must be unique across all metrics that are sent by a single application. This includes not only the metrics defined in the app's metrics.yaml, but the metrics.yaml of any Glean SDK-using library that the application uses, including the Glean SDK itself. Therefore, care should be taken to name things specifically enough so as to avoid namespace collisions. In practice, this generally involves thinking carefully about the category of the metric, more than the name.

Note: Duplicate metric identifiers are not currently detected at build time. See bug 1578383 for progress on that. However, the probe_scraper process, which runs nightly, will detect duplicate metrics and e-mail the notification_emails associated with the given metrics.

Be as specific as possible

More broadly, you should choose the names of metrics to be as specific as possible. It is not necessary to put the type of the metric in the category or name, since this information is retained in other ways through the entire end-to-end system.

For example, if defining a set of events related to search, put them in a category called search, rather than just events or search_events. The events word here would be redundant.

What if none of these metric types is the right fit?

The current set of metrics the Glean SDK supports is based on known common use cases, but new use cases are discovered all the time.

Please reach out to us on #glean:mozilla.org. If you think you need a new metric type, we have a process for that.

How do I make sure my metric is working?

The Glean SDK has rich support for writing unit tests involving metrics. Writing a good unit test is a large topic, but in general, you should write unit tests for all new telemetry that does the following:

• Performs the operation being measured.

• Asserts that metrics contain the expected data, using the testGetValue API on the metric.

• Where applicable, asserts that no errors are recorded, such as when values are out of range, using the testGetNumRecordedErrors API.

In addition to unit tests, it is good practice to validate the incoming data for the new metric on a pre-release channel to make sure things are working as expected.

Adding the metric to the metrics.yaml file

The metrics.yaml file defines the metrics your application or library will send. They are organized into categories. The overall organization is:

# Required to indicate this is a metrics.yaml file
$schema: moz://mozilla.org/schemas/glean/metrics/2-0-0 toolbar: click: type: event description: | Event to record toolbar clicks. notification_emails: - CHANGE-ME@example.com bugs: - https://bugzilla.mozilla.org/123456789/ data_reviews: - http://example.com/path/to/data-review expires: 2019-06-01 # <-- Update to a date in the future double_click: ...  Refer to the metrics YAML registry format for a full reference on the metrics.yaml file structure. Using the metric from your code The reference documentation for each metric type goes into detail about using each metric type from your code. Note that all Glean metrics are write-only. Outside of unit tests, it is impossible to retrieve a value from the Glean SDK's database. While this may seem limiting, this is required to: • enforce the semantics of certain metric types (e.g. that Counters can only be incremented). • ensure the lifetime of the metric (when it is cleared or reset) is correctly handled. Capitalization One thing to note is that we try to adhere to the coding conventions of each language wherever possible, so the metric name and category in the metrics.yaml (which is in snake_case) may be changed to some other case convention, such as camelCase, when used from code. Event extras and labels are never capitalized, no matter the target language. Category and metric names in the metrics.yaml are in snake_case, but given the Kotlin coding standards defined by ktlint, these identifiers must be camelCase in Kotlin. For example, the metric defined in the metrics.yaml as: views: login_opened: ...  is accessible in Kotlin as: import org.mozilla.yourApplication.GleanMetrics.Views GleanMetrics.Views.loginOpened...  Category and metric names in the metrics.yaml are in snake_case, but given the Swift coding standards defined by swiftlint, these identifiers must be camelCase in Swift. For example, the metric defined in the metrics.yaml as: views: login_opened: ...  is accessible in Kotlin as: GleanMetrics.Views.loginOpened...  Category and metric names in the metrics.yaml are in snake_case, which matches the PEP8 standard, so no translation is needed for Python. Given the Rust coding standards defined by clippy, identifiers should all be snake_case. This includes category names which in the metrics.yaml are dotted.snake_case: compound.category: metric_name: ...  In Rust this becomes: use fog::metrics; metrics::compound_category::metric_name...  JavaScript identifiers are customarily camelCase. This requires transforming a metric defined in the metrics.yaml as: compound.category: metric_name: ...  to a form useful in JS as: import * as compoundCategory from "./path/to/generated/files/compoundCategory.js"; compoundCategory.metricName...  Firefox Desktop has Coding Style Guidelines for both C++ and JS. This results in, for a metric defined in the metrics.yaml as: compound.category: metric_name: ...  an identifier that looks like: C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::compound_category::metric_name...  JavaScript Glean.compoundCategory.metricName...  Adding new metrics Table of Contents Process overview When adding a new metric, the process is: • Consider the question you are trying to answer with this data, and choose the metric type and parameters to use. • Add a new entry to metrics.yaml. • Add code to your project to record into the metric by calling the Glean SDK. Important: Any new data collection requires documentation and data-review. This is also required for any new metric automatically collected by the Glean SDK. Choosing a metric type The following is a set of questions to ask about the data being collected to help better determine which metric type to use. Is it a single measurement? If the value is true or false, use a boolean metric. If the value is a string, use a string metric. For example, to record the name of the default search engine. Beware: string metrics are exceedingly general, and you are probably best served by selecting the most specific metric for the job, since you'll get better error checking and richer analysis tools for free. For example, avoid storing a number in a string metric --- you probably want a counter metric instead. If you need to store multiple string values in a metric, use a string list metric. For example, you may want to record the list of other Mozilla products installed on the device. For all of the metric types in this section that measure single values, it is especially important to consider how the lifetime of the value relates to the ping it is being sent in. Since these metrics don't perform any aggregation on the client side, when a ping containing the metric is submitted, it will contain only the "last known" value for the metric, potentially resulting in data loss. There is further discussion of metric lifetimes below. Are you measuring user behavior? For tracking user behavior, it is usually meaningful to know the over of events that lead to the use of a feature. Therefore, for user behavior, an event metric is usually the best choice. Be aware, however, that events can be particularly expensive to transmit, store and analyze, so should not be used for higher-frequency measurements. Are you counting things? If you want to know how many times something happened, use a counter metric. If you are counting a group of related things, or you don't know what all of the things to count are at build time, use a labeled counter metric. If you need to know how many times something happened relative to the number of times something else happened, use a rate metric. If you need to know when the things being counted happened relative to other things, consider using an event. Are you measuring time? If you need to record an absolute time, use a datetime metric. Datetimes are recorded in the user's local time, according to their device's real time clock, along with a timezone offset from UTC. Datetime metrics allow specifying the resolution they are collected at, and to stay lean, they should only be collected at the minimum resolution required to answer your question. If you need to record how long something takes you have a few options. If you need to measure the total time spent doing a particular task, look to the timespan metric. Timespan metrics allow specifying the resolution they are collected at, and to stay lean, they should only be collected at the minimum resolution required to answer your question. Note that this metric should only be used to measure time on a single thread. If multiple overlapping timespans are measured for the same metric, an invalid state error is recorded. If you need to measure the relative occurrences of many timings, use a timing distribution. It builds a histogram of timing measurements, and is safe to record multiple concurrent timespans on different threads. If you need to know the time between multiple distinct actions that aren't a simple "begin" and "end" pair, consider using an event. For how long do you need to collect this data? Think carefully about how long the metric will be needed, and set the expires parameter to disable the metric at the earliest possible time. This is an important component of Mozilla's lean data practices. When the metric passes its expiration date (determined at build time), it will automatically stop collecting data. When a metric's expiration is within in 14 days, emails will be sent from telemetry-alerts@mozilla.com to the notification_emails addresses associated with the metric. At that time, the metric should be removed, which involves removing it from the metrics.yaml file and removing uses of it in the source code. Removing a metric does not affect the availability of data already collected by the pipeline. If the metric is still needed after its expiration date, it should go back for another round of data review to have its expiration date extended. Important: Ensure that telemetry alerts are received and are reviewed in a timely manner. Expired metrics don't record any data, extending or removing a metric should be done in time. Consider adding both a group email address and an individual who is responsible for this metric to the notification_emails list. When should the Glean SDK automatically clear the measurement? The lifetime parameter of a metric defines when its value will be cleared. There are three lifetime options available: ping (default) The metric is cleared each time it is submitted in the ping. This is the most common case, and should be used for metrics that are highly dynamic, such as things computed in response to the user's interaction with the application. application The metric is related to an application run, and is cleared after the application restarts and any Glean-owned ping, due at startup, is submitted. This should be used for things that are constant during the run of an application, such as the operating system version. In practice, these metrics are generally set during application startup. A common mistake--- using the ping lifetime for these type of metrics---means that they will only be included in the first ping sent during a particular run of the application. user Reach out to the Glean team before using this. The metric is part of the user's profile and will live as long as the profile lives. This is often not the best choice unless the metric records a value that really needs to be persisted for the full lifetime of the user profile, e.g. an identifier like the client_id, the day the product was first executed. It is rare to use this lifetime outside of some metrics that are built in to the Glean SDK. While lifetimes are important to understand for all metric types, they are particularly important for the metric types that record single values and don't aggregate on the client (boolean, string, labeled_string, string_list, datetime and uuid), since these metrics will send the "last known" value and missing the earlier values could be a form of unintended data loss. A lifetime example Let's work through an example to see how these lifetimes play out in practice. Let's suppose we have a user preference, "turbo mode", which defaults to false, but the user can turn it to true at any time. We want to know when this flag is true so we can measure its affect on other metrics in the same ping. In the following diagram, we look at a time period that sends 4 pings across two separate runs of the application. We assume here, that like the Glean SDK's built-in metrics ping, the developer writing the metric isn't in control of when the ping is submitted. In this diagram, the ping measurement windows are represented as rectangles, but the moment the ping is "submitted" is represented by its right edge. The user changes the "turbo mode" setting from false to true in the first run, and then toggles it again twice in the second run. • A. Ping lifetime, set on change: The value isn't included in Ping 1, because Glean doesn't know about it yet. It is included in the first ping after being recorded (Ping 2), which causes it to be cleared. • B. Ping lifetime, set on init and change: The default value is included in Ping 1, and the changed value is included in Ping 2, which causes it to be cleared. It therefore misses Ping 3, but when the application is started, it is recorded again and it is included in Ping 4. However, this causes it to be cleared again and it is not in Ping 5. • C. Application lifetime, set on change: The value isn't included in Ping 1, because Glean doesn't know about it yet. After the value is changed, it is included in Pings 2 and 3, but then due to application restart it is cleared, so it is not included until the value is manually toggled again. • D. Application, set on init and change: The default value is included in Ping 1, and the changed value is included in Pings 2 and 3. Even though the application startup causes it to be cleared, it is set again, and all subsequent pings also have the value. • E. User, set on change: The default value is missing from Ping 1, but since user lifetime metrics aren't cleared unless the user profile is reset (e.g. on Android, when the product is uninstalled), it is included in all subsequent pings. • F. User, set on init and change: Since user lifetime metrics aren't cleared unless the user profile is reset, it is included in all subsequent pings. This would be true even if the "turbo mode" preference were never changed again. Note that for all of the metric configurations, the toggle of the preference off and on during Ping 4 is completely missed. If you need to create a ping containing one, and only one, value for this metric, consider using a custom ping to create a ping whose lifetime matches the lifetime of the value. What if none of these lifetimes are appropriate? If the timing at which the metric is sent in the ping needs to closely match the timing of the metrics value, the best option is to use a custom ping to manually control when pings are sent. This is especially useful when metrics need to be tightly related to one another, for example when you need to measure the distribution of frame paint times when a particular rendering backend is in use. If these metrics were in different pings, with different measurement windows, it is much harder to do that kind of reasoning with much certainty. What should this new metric be called? Metric names have a maximum length of 30 characters. Reuse names from other applications There's a lot of value using the same name for analogous metrics collected across different products. For example, BigQuery makes it simple to join columns with the same name across multiple tables. Therefore, we encourage you to investigate if a similar metric is already being collected by another product. If it is, there may be an opportunity for code reuse across these products, and if all the projects are using the Glean SDK, it's easy for libraries to send their own metrics. If sharing the code doesn't make sense, at a minimum we recommend using the same metric name for similar actions and concepts whenever possible. Make names unique within an application Metric identifiers (the combination of a metric's category and name) must be unique across all metrics that are sent by a single application. This includes not only the metrics defined in the app's metrics.yaml, but the metrics.yaml of any Glean SDK-using library that the application uses, including the Glean SDK itself. Therefore, care should be taken to name things specifically enough so as to avoid namespace collisions. In practice, this generally involves thinking carefully about the category of the metric, more than the name. Note: Duplicate metric identifiers are not currently detected at build time. See bug 1578383 for progress on that. However, the probe_scraper process, which runs nightly, will detect duplicate metrics and e-mail the notification_emails associated with the given metrics. Be as specific as possible More broadly, you should choose the names of metrics to be as specific as possible. It is not necessary to put the type of the metric in the category or name, since this information is retained in other ways through the entire end-to-end system. For example, if defining a set of events related to search, put them in a category called search, rather than just events or search_events. The events word here would be redundant. What if none of these metric types is the right fit? The current set of metrics the Glean SDK supports is based on known common use cases, but new use cases are discovered all the time. Please reach out to us on #glean:mozilla.org. If you think you need a new metric type, we have a process for that. How do I make sure my metric is working? The Glean SDK has rich support for writing unit tests involving metrics. Writing a good unit test is a large topic, but in general, you should write unit tests for all new telemetry that does the following: • Performs the operation being measured. • Asserts that metrics contain the expected data, using the testGetValue API on the metric. • Where applicable, asserts that no errors are recorded, such as when values are out of range, using the testGetNumRecordedErrors API. In addition to unit tests, it is good practice to validate the incoming data for the new metric on a pre-release channel to make sure things are working as expected. Adding the metric to the metrics.yaml file The metrics.yaml file defines the metrics your application or library will send. They are organized into categories. The overall organization is: # Required to indicate this is a metrics.yaml file$schema: moz://mozilla.org/schemas/glean/metrics/2-0-0

toolbar:
click:
type: event
description: |
Event to record toolbar clicks.
notification_emails:
- CHANGE-ME@example.com
bugs:
- https://bugzilla.mozilla.org/123456789/
data_reviews:
- http://example.com/path/to/data-review
expires: 2019-06-01  # <-- Update to a date in the future

double_click:
...


Refer to the metrics YAML registry format for a full reference on the metrics.yaml file structure.

Using the metric from your code

The reference documentation for each metric type goes into detail about using each metric type from your code.

Note that all Glean metrics are write-only. Outside of unit tests, it is impossible to retrieve a value from the Glean SDK's database. While this may seem limiting, this is required to:

• enforce the semantics of certain metric types (e.g. that Counters can only be incremented).
• ensure the lifetime of the metric (when it is cleared or reset) is correctly handled.

Capitalization

One thing to note is that we try to adhere to the coding conventions of each language wherever possible, so the metric name and category in the metrics.yaml (which is in snake_case) may be changed to some other case convention, such as camelCase, when used from code.

Event extras and labels are never capitalized, no matter the target language.

Category and metric names in the metrics.yaml are in snake_case, but given the Kotlin coding standards defined by ktlint, these identifiers must be camelCase in Kotlin. For example, the metric defined in the metrics.yaml as:

views:
login_opened:
...


is accessible in Kotlin as:

import org.mozilla.yourApplication.GleanMetrics.Views
GleanMetrics.Views.loginOpened...


Category and metric names in the metrics.yaml are in snake_case, but given the Swift coding standards defined by swiftlint, these identifiers must be camelCase in Swift. For example, the metric defined in the metrics.yaml as:

views:
login_opened:
...


is accessible in Kotlin as:

GleanMetrics.Views.loginOpened...


Category and metric names in the metrics.yaml are in snake_case, which matches the PEP8 standard, so no translation is needed for Python.

Given the Rust coding standards defined by clippy, identifiers should all be snake_case. This includes category names which in the metrics.yaml are dotted.snake_case:

compound.category:
metric_name:
...


In Rust this becomes:

use fog::metrics;

metrics::compound_category::metric_name...


JavaScript identifiers are customarily camelCase. This requires transforming a metric defined in the metrics.yaml as:

compound.category:
metric_name:
...


to a form useful in JS as:

import * as compoundCategory from "./path/to/generated/files/compoundCategory.js";

compoundCategory.metricName...


Firefox Desktop has Coding Style Guidelines for both C++ and JS. This results in, for a metric defined in the metrics.yaml as:

compound.category:
metric_name:
...


an identifier that looks like:

C++

#include "mozilla/glean/GleanMetrics.h"

mozilla::glean::compound_category::metric_name...


JavaScript

Glean.compoundCategory.metricName...


Unit testing Glean metrics

In order to support unit testing inside of client applications using the Glean SDK, a set of testing API functions have been included. The intent is to make the Glean SDK easier to test 'out of the box' in any client application it may be used in. These functions expose a way to inspect and validate recorded metric values within the client application but are restricted to test code only through visibility annotations (@VisibleForTesting(otherwise = VisibleForTesting.NONE) for Kotlin, internal methods for Swift). (Outside of a testing context, Glean APIs are otherwise write-only so that it can enforce semantics and constraints about data).

To encourage using the testing API, it is also possible to generate testing coverage reports to show which metrics in your project are tested.

General test API method semantics

Using the Glean SDK's unit testing API requires adding Robolectric 4.0 or later as a testing dependency. In Gradle, this can be done by declaring a testImplementation dependency:

dependencies {
testImplementation "org.robolectric:robolectric:4.3.1"
}


In order to prevent issues with async calls when unit testing the Glean SDK, it is important to put the Glean SDK into testing mode by applying the JUnit GleanTestRule to your test class. When the Glean SDK is in testing mode, it enables uploading and clears the recorded metrics at the beginning of each test run. The rule can be used as shown below:

@RunWith(AndroidJUnit4::class)
class ActivityCollectingDataTest {
// Apply the GleanTestRule to set up a disposable Glean instance.
// Please note that this clears the Glean data across tests.
@get:Rule
val gleanRule = GleanTestRule(ApplicationProvider.getApplicationContext())

@Test
fun checkCollectedData() {
// The Glean SDK testing API can be called here.
}
}


This will ensure that metrics are done recording when the other test functions are used.

To check if a value exists (i.e. it has been recorded), there is a testHasValue() function on each of the metric instances:

assertTrue(GleanMetrics.Search.defaultSearchEngineUrl.testHasValue())


To check the actual values, there is a testGetValue() function on each of the metric instances. It is important to check that the values are recorded as expected, since many of the metric types may truncate or error-correct the value. This function will return a datatype appropriate to the specific type of the metric it is being used with:

assertEquals("https://example.com/search?", GleanMetrics.Search.defaultSearchEngineUrl.testGetValue())


Note that each of these functions has its visibility limited to the scope of unit tests by making use of the @VisibleForTesting annotation, so the IDE should complain if you attempt to use them inside of client code.

NOTE: There's no automatic test rule for Glean tests implemented.

In order to prevent issues with async calls when unit testing the Glean SDK, it is important to put the Glean SDK into testing mode. When the Glean SDK is in testing mode, it enables uploading and clears the recorded metrics at the beginning of each test run.

Activate it by resetting Glean in your test's setup:

@testable import Glean
import XCTest

class GleanUsageTests: XCTestCase {
override func setUp() {
Glean.shared.resetGlean(clearStores: true)
}

// ...
}


This will ensure that metrics are done recording when the other test functions are used.

To check if a value exists (i.e. it has been recorded), there is a testHasValue() function on each of the metric instances:

XCTAssertTrue(GleanMetrics.Search.defaultSearchEngineUrl.testHasValue())


To check the actual values, there is a testGetValue() function on each of the metric instances. It is important to check that the values are recorded as expected, since many of the metric types may truncate or error-correct the value. This function will return a datatype appropriate to the specific type of the metric it is being used with:

XCTAssertEqual("https://example.com/search?", try GleanMetrics.Search.defaultSearchEngineUrl.testGetValue())


Note that each of these functions is marked as internal, you need to import Glean explicitly in test mode:

@testable import Glean


It is generally a good practice to "reset" the Glean SDK prior to every unit test that uses the Glean SDK, to prevent side effects of one unit test impacting others. The Glean SDK contains a helper function glean.testing.reset_glean() for this purpose. It has two required arguments: the application ID, and the application version. Each reset of the Glean SDK will create a new temporary directory for Glean to store its data in. This temporary directory is automatically cleaned up the next time the Glean SDK is reset or when the testing framework finishes.

The instructions below assume you are using pytest as the test runner. Other test-running libraries have similar features, but are different in the details.

Create a file conftest.py at the root of your test directory, and add the following to reset Glean at the start of every test in your suite:

import pytest
from glean import testing

@pytest.fixture(name="reset_glean", scope="function", autouse=True)
def fixture_reset_glean():
testing.reset_glean(application_id="my-app-id", application_version="0.1.0")


To check if a value exists (i.e. it has been recorded), there is a test_has_value() function on each of the metric instances:

from glean import load_metrics
metrics = load_metrics("metrics.yaml")

# ...

assert metrics.search.search_engine_url.test_has_value()


To check the actual values, there is a test_get_value() function on each of the metric instances. It is important to check that the values are recorded as expected, since many of the metric types may truncate or error-correct the value. This function will return a datatype appropriate to the specific type of the metric it is being used with:

assert (
"https://example.com/search?" ==
metrics.search.default_search_engine_url.test_get_value()
)


TODO. To be implemented in bug 1648448.

Testing metrics for custom pings

In order to test metrics where the metric is included in more than one ping, the test functions take an optional pingName argument (ping_name in Python). This is the name of the ping that the metric is being sent in, such as "events" for the events ping, or "metrics" for the metrics ping. This could also be a custom ping name that the metric is being sent in. In most cases you should not have to supply the ping name to the test function and can just use the default which is the "default" ping that this metric is sent in. You should only need to provide a pingName if the metric is being sent in more than one ping in order to identify the correct metric store.

You can call the testHasValue() and testGetValue() functions with pingName like this:

GleanMetrics.Foo.uriCount.testHasValue("customPing")
GleanMetrics.Foo.uriCount.testGetValue("customPing")


Example of using the test API

Here is a longer example to better illustrate the intended use of the test API:

// Record a metric value with extra to validate against
GleanMetrics.BrowserEngagement.click.record(
mapOf(
BrowserEngagement.clickKeys.font to "Courier"
)
)

// Record more events without extras attached
BrowserEngagement.click.record()
BrowserEngagement.click.record()

// Check if we collected any events into the 'click' metric
assertTrue(BrowserEngagement.click.testHasValue())

// Retrieve a snapshot of the recorded events
val events = BrowserEngagement.click.testGetValue()

// Check if we collected all 3 events in the snapshot
assertEquals(3, events.size)

// Check extra key/value for first event in the list
assertEquals("Courier", events.elementAt(0).extra["font"])


Here is a longer example to better illustrate the intended use of the test API:

// Record a metric value with extra to validate against
GleanMetrics.BrowserEngagement.click.record([.font: "Courier"])

// Record more events without extras attached
BrowserEngagement.click.record()
BrowserEngagement.click.record()

// Check if we collected any events into the 'click' metric
XCTAssertTrue(BrowserEngagement.click.testHasValue())

// Retrieve a snapshot of the recorded events
let events = try! BrowserEngagement.click.testGetValue()

// Check if we collected all 3 events in the snapshot
XCTAssertEqual(3, events.count)

// Check extra key/value for first event in the list
XCTAssertEqual("Courier", events[0].extra?["font"])


Here is a longer example to better illustrate the intended use of the test API:

from glean import load_metrics
metrics = load_metrics("metrics.yaml")

# Record a metric value with extra to validate against
metrics.url.visit.add(1)

# Check if we collected any events into the 'click' metric
assert metrics.url.visit.test_has_value()

# Retrieve a snapshot of the recorded events
assert 1 == metrics.url.visit.test_get_value()


TODO. To be implemented in bug 1648448.

Generating testing coverage reports

Glean can generate coverage reports to track which metrics are tested in your unit test suite.

There are three steps to integrate it into your continuous integration workflow: recording coverage, post-processing the results, and uploading the results.

Recording coverage

Glean testing coverage is enabled by setting the GLEAN_TEST_COVERAGE environment variable to the name of a file to store results. It is good practice to set it to the absolute path to a file, since some testing harnesses (such as cargo test) may change the current working directory.

GLEAN_TEST_COVERAGE=$(realpath glean_coverage.txt) make test  Post-processing the results A post-processing step is required to convert the raw output in the file specified by GLEAN_TEST_COVERAGE into usable output for coverage reporting tools. Currently, the only coverage reporting tool supported is codecov.io. This post-processor is available in the coverage subcommand in the glean_parser tool. For some build systems, glean_parser is already installed for you by the build system integration at the following locations: • On Android/Gradle, $GRADLE_HOME/glean/bootstrap-4.5.11/Miniconda3/bin/glean_parser
• On iOS/Carthage, $PROJECT_ROOT/.venv/bin/glean_parser • For other systems, install glean_parser using pip install glean_parser The glean_parser coverage command requires the following parameters: • -f: The output format to produce, for example codecovio to produce codecov.io's custom format. • -o: The path to the output file, for example codecov.json. • -c: The input raw coverage file. glean_coverage.txt in the example above. • A list of the metrics.yaml files in your repository. For example, to produce output for codecov.io: glean_parser coverage -f codecovio -o glean_coverage.json -c glean_coverage.txt app/metrics.yaml  In this example, the glean_coverage.json file is now ready for uploading to codecov.io. Uploading coverage If using codecov.io, the uploader doesn't send coverage results for YAML files by default. Pass the -X yaml option to the uploader to make sure they are included: bash <(curl -s https://codecov.io/bash) -X yaml  Error reporting The Glean SDK records the number of errors that occur when metrics are passed invalid data or are otherwise used incorrectly. This information is reported back in special labeled counter metrics in the glean.error category. Error metrics are included in the same pings as the metric that caused the error. Additionally, error metrics are always sent in the metrics ping ping. The following categories of errors are recorded: • invalid_value: The metric value was invalid. • invalid_label: The label on a labeled metric was invalid. • invalid_state: The metric caught an invalid state while recording. • invalid_overflow: The metric value to be recorded overflows the metric-specific upper range. For example, if you had a string metric and passed it a string that was too long: MyMetrics.stringMetric.set("this_string_is_longer_than_the_limit_for_string_metrics")  The following error metric counter would be incremented: Glean.error.invalidOverflow["my_metrics.string_metric"].add(1)  Resulting in the following keys in the ping: { "metrics": { "labeled_counter": { "glean.error.invalid_overflow": { "my_metrics.string_metric": 1 } } } }  If you have a debug build of the Glean SDK, details about the errors being recorded are included in the logs. This detailed information is not included in Glean pings. The Glean JavaScript SDK provides a slightly different set of metrics and pings If you are looking for the metrics collected by Glean.js, refer to the documentation over on the @mozilla/glean.js repository. Metrics This document enumerates the metrics collected by this project using the Glean SDK. This project may depend on other projects which also collect metrics. This means you might have to go searching through the dependency tree to get a full picture of everything collected by this project. Pings all-pings These metrics are sent in every ping. All Glean pings contain built-in metrics in the ping_info and client_info sections. In addition to those built-in metrics, the following metrics are added to the ping: NameTypeDescriptionData reviewsExtrasExpirationData Sensitivity glean.error.invalid_labellabeled_counterCounts the number of times a metric was set with an invalid label. The labels are the category.name identifier of the metric.Bug 1499761never1 glean.error.invalid_overflowlabeled_counterCounts the number of times a metric was set a value that overflowed. The labels are the category.name identifier of the metric.Bug 1591912never1 glean.error.invalid_statelabeled_counterCounts the number of times a timing metric was used incorrectly. The labels are the category.name identifier of the metric.Bug 1499761never1 glean.error.invalid_valuelabeled_counterCounts the number of times a metric was set to an invalid value. The labels are the category.name identifier of the metric.Bug 1499761never1 baseline This is a built-in ping that is assembled out of the box by the Glean SDK. See the Glean SDK documentation for the baseline ping. This ping is sent if empty. This ping includes the client id. Data reviews for this ping: Bugs related to this ping: Reasons this ping may be sent: • active: The ping was submitted when the application became active again, which includes when the application starts. In earlier versions, this was called foreground. *Note*: this ping will not contain the glean.baseline.duration metric.  • dirty_startup: The ping was submitted at startup, because the application process was killed before the Glean SDK had the chance to generate this ping, before becoming inactive, in the last session. *Note*: this ping will not contain the glean.baseline.duration metric.  • inactive: The ping was submitted when becoming inactive. In earlier versions, this was called background. All Glean pings contain built-in metrics in the ping_info and client_info sections. In addition to those built-in metrics, the following metrics are added to the ping: NameTypeDescriptionData reviewsExtrasExpirationData Sensitivity glean.baseline.durationtimespanThe duration of the last foreground session.Bug 1512938never1, 2 glean.validation.first_run_hourdatetimeThe hour of the first run of the application.Bug 1680783never1 glean.validation.pings_submittedlabeled_counterA count of the pings submitted, by ping type. This metric appears in both the metrics and baseline pings. - On the metrics ping, the counts include the number of pings sent since the last metrics ping (including the last metrics ping) - On the baseline ping, the counts include the number of pings send since the last baseline ping (including the last baseline ping)Bug 1586764never1 deletion-request This is a built-in ping that is assembled out of the box by the Glean SDK. See the Glean SDK documentation for the deletion-request ping. This ping is sent if empty. This ping includes the client id. Data reviews for this ping: Bugs related to this ping: Reasons this ping may be sent: • at_init: The ping was submitted at startup. Glean discovered that between the last time it was run and this time, upload of data has been disabled. • set_upload_enabled: The ping was submitted between Glean init and Glean shutdown. Glean was told after init but before shutdown that upload has changed from enabled to disabled. All Glean pings contain built-in metrics in the ping_info and client_info sections. This ping contains no metrics. metrics This is a built-in ping that is assembled out of the box by the Glean SDK. See the Glean SDK documentation for the metrics ping. This ping includes the client id. Data reviews for this ping: Bugs related to this ping: Reasons this ping may be sent: • overdue: The last ping wasn't submitted on the current calendar day, but it's after 4am, so this ping submitted immediately • reschedule: A ping was just submitted. This ping was rescheduled for the next calendar day at 4am. • today: The last ping wasn't submitted on the current calendar day, but it is still before 4am, so schedule to send this ping on the current calendar day at 4am. • tomorrow: The last ping was already submitted on the current calendar day, so schedule this ping for the next calendar day at 4am. • upgrade: This ping was submitted at startup because the application was just upgraded. All Glean pings contain built-in metrics in the ping_info and client_info sections. In addition to those built-in metrics, the following metrics are added to the ping: NameTypeDescriptionData reviewsExtrasExpirationData Sensitivity glean.database.sizememory_distributionThe size of the database file at startup.Bug 1656589never1 glean.error.iocounterThe number of times we encountered an IO error when writing a pending ping to disk.Bug 1686233never1 glean.error.preinit_tasks_overflowcounterThe number of tasks queued in the pre-initialization buffer. Only sent if the buffer overflows.Bug 1609482never1 glean.time.invalid_timezone_offsetcounterCounts the number of times we encountered an invalid timezone offset when trying to get the current time. A timezone offset is invalid if it is outside [-24h, +24h]. If invalid a UTC offset is used (+0h).Bug 1611770, Bug 17174022021-12-311 glean.upload.deleted_pings_after_quota_hitcounterThe number of pings deleted after the quota for the size of the pending pings directory or number of files is hit. Since quota is only calculated for the pending pings directory, and deletion request ping live in a different directory, deletion request pings are never deleted.Bug 1601550never1 glean.upload.discarded_exceeding_pings_sizememory_distributionThe size of pings that exceeded the maximum ping size allowed for upload.Bug 1597761never1 glean.upload.pending_pingscounterThe total number of pending pings at startup. This does not include deletion-request pings.Bug 1665041never1 glean.upload.pending_pings_directory_sizememory_distributionThe size of the pending pings directory upon initialization of Glean. This does not include the size of the deletion request pings directory.Bug 1601550never1 glean.upload.ping_upload_failurelabeled_counterCounts the number of ping upload failures, by type of failure. This includes failures for all ping types, though the counts appear in the next successfully sent metrics ping.Bug 1589124 • status_code_4xx • status_code_5xx • status_code_unknown • unrecoverable • recoverable never1 glean.validation.first_run_hourdatetimeThe hour of the first run of the application.Bug 1680783never1 glean.validation.foreground_countcounterOn mobile, the number of times the application went to foreground.Bug 1683707never1 glean.validation.pings_submittedlabeled_counterA count of the pings submitted, by ping type. This metric appears in both the metrics and baseline pings. - On the metrics ping, the counts include the number of pings sent since the last metrics ping (including the last metrics ping) - On the baseline ping, the counts include the number of pings send since the last baseline ping (including the last baseline ping)Bug 1586764never1 Data categories are defined here. Pings A ping is a bundle of related metrics, gathered in a payload to be transmitted. The ping payload is encoded in JSON format and contains one or more of the common sections with shared information data. If data collection is enabled, the chosen Glean SDK may provide a set of built-in pings that are assembled out of the box without any developer intervention. Table of contents Payload structure Every ping payload has the following keys at the top-level: • The ping_info section contains core metadata that is included in every ping. • The client_info section contains information that identifies the client. It is included in most pings (including all built-in pings), but may be excluded from pings where we don't want to connect client information with the other metrics in the ping. The following keys are only present if any metrics or events were recorded for the given ping: • The metrics section contains the submitted values for all metric types except for events. It has keys for each of the metric types, under which is data for each metric. • The events section contains the events recorded in the ping. See the payload documentation for more details for each metric type in the metrics and events section. The ping_info section The following fields are included in the ping_info section, for every ping. Optional fields are marked accordingly. seq Type: Counter, Lifetime: User A running counter of the number of times pings of this type have been sent. start_time Type: Datetime, Lifetime: User The time of the start of collection of the data in the ping, in local time and with minute precision, including timezone information. end_time Type: Datetime, Lifetime: Ping The time of the end of collection of the data in the ping, in local time and with minute precision, including timezone information. This is also the time this ping was generated and is likely well before ping transmission time. reason (optional) The reason the ping was submitted. The specific set of values and their meanings are defined for each metric type in the reasons field in the pings.yaml file. experiments (optional) A dictionary of active experiments. This object contains experiment annotations keyed by the experiment id. Each annotation contains the experiment branch the client is enrolled in and may contain a string to string map with additional data in the extra key. Both the id and branch are truncated to 30 characters. See Using the Experiments API on how to record experiments data. { "<id>": { "branch": "branch-id", "extra": { "some-key": "a-value" } } }  The client_info section The following fields are included in the client_info section. Optional fields are marked accordingly. app_build Type: String, Lifetime: Ping The build identifier generated by the CI system (e.g. "1234/A"). If the value was not provided through configuration, this metric gets set to Unknown. app_channel (optional) Type: String, Lifetime: Ping The product-provided release channel (e.g. "beta"). app_display_version Type: String, Lifetime: Ping The user-visible version string (e.g. "1.0.3"). The meaning of the string (e.g. whether semver or a git hash) is application-specific. If the value was not provided through configuration, this metric gets set to Unknown. architecture Type: String, Lifetime: Ping The architecture of the device (e.g. "arm", "x86"). client_id (optional) Type: String, Lifetime: User A UUID identifying a profile and allowing user-oriented correlation of data. device_manufacturer (optional) Type: String, Lifetime: Application The manufacturer of the device the application is running on. Not set if the device manufacturer can't be determined (e.g. on Desktop). device_model (optional) Type: String, Lifetime: Application The model of the device the application is running on. On Android, this is Build.MODEL, the user-visible marketing name, like "Pixel 2 XL". Not set if the device model can't be determined (e.g. on Desktop). first_run_date Type: Datetime, Lifetime: User The date of the first run of the application, in local time and with day precision, including timezone information. os Type: String, Lifetime: Application The name of the operating system (e.g. "Linux", "Android", "iOS"). os_version Type: String, Lifetime: Application The user-visible version of the operating system (e.g. "1.2.3"). If the version detection fails, this metric gets set to Unknown. android_sdk_version (optional) Type: String, Lifetime: Application The Android specific SDK version of the software running on this hardware device (e.g. "23"). telemetry_sdk_build Type: String, Lifetime: Application The version of the Glean SDK. locale (optional) Type: String, Lifetime: Application The locale of the application during initialization (e.g. "es-ES"). If the locale can't be determined on the system, the value is "und", to indicate "undetermined". Ping submission The pings that the Glean SDK generates are submitted to the Mozilla servers at specific paths, in order to provide additional metadata without the need to unpack the ping payload. URL A typical submission URL looks like "<server-address>/submit/<application-id>/<doc-type>/<glean-schema-version>/<document-id>"  where: • <server-address>: the address of the server that receives the pings; • <application-id>: a unique application id, automatically detected by the Glean SDK; this is the value returned by Context.getPackageName(); • <doc-type>: the name of the ping; this can be one of the pings available out of the box with the Glean SDK, or a custom ping; • <glean-schema-version>: the version of the Glean ping schema; • <document-id>: a unique identifier for this ping. Limitations To keep resource usage in check, the Glean SDK enforces some limitations on ping uploading and ping storage. Rate limiting Only up to 15 ping submissions every 60 seconds are allowed. There are no exposed methods to change these rate limiting defaults, follow Bug 1647630 and Bug 1727069 for updates. Request body size limiting The body of a ping request may have up to 1MB (after compression). Pings that exceed this size are discarded and don't get uploaded. Size and number of discarded pings are recorded on the internal Glean metric glean.upload.discarded_exceeding_pings_size. Storage quota Pending pings are stored on disk. Storage is scanned every time Glean is initialized and upon scanning Glean checks its size. If it exceeds a size of 10MB or 250 pending pings, pings are deleted to get the storage back to an accepted size. Pings are deleted oldest first, until the storage size is below the quota. The number of deleted pings due to exceeding storage quota is recorded on the metric glean.upload.deleted_pings_after_quota_hit and the size of the pending pings directory is recorded (regardless on whether quota has been reached) on the metric glean.upload.pending_pings_directory_size. Deletion request pings are not subject to this limitation and never get deleted. Submitted headers A pre-defined set of headers is additionally sent along with the submitted ping. Content-Type Describes the data sent to the server. Value is always application/json; charset=utf-8. Date Submission date/time in GMT/UTC+0 offset, e.g. Mon, 23 Jan 2019 10:10:10 GMT+00:00. User-Agent The Glean SDKs do not set this header1, so it will contain whatever value was set by the underlying uploading mechanism. For example, when sending pings from browsers it will contain the characteristic browser UA string. This header is parsed by the Glean pipeline and can be queried at analysis time through the metadata.user_agent.* fields in the ping tables. X-Telemetry-Agent The Glean SDK version and platform this ping is sent from. Useful for debugging purposes when pings are sent to the error stream. as it describes the application and the Glean SDK used for sending the ping. It's looks like Glean/40.0.0 (Kotlin on Android), where 40.0.0 is the Glean SDK version number and Kotlin on Android is the name of the language used by the SDK that sent the request plus the name of the platform it is running on. This header is currently only sent by the Glean JavaScript SDK. See note. X-Client-Type Custom header to support handling of Glean pings in the legacy pipeline. Values is always Glean. X-Client-Version The Glean SDK version e.g. 0.40.0, sent as a custom header to support handling of Glean pings in the legacy pipeline. X-Debug-Id (optional) Debug header attached to Glean pings by using the debug APIs, e.g. test-tag. When this header is present, the ping is redirected to the Glean Debug View. X-Source-Tags (optional) A list of tags to associate with the ping, useful for clustering pings at analysis time, for example to tell data generated from CI from other data e.g. automation, perf. This header is attached to Glean pings by using the debug APIs. 1 This is only true for the Glean JavaScript SDK at the moment. For the other SDKs this header is overwritten with the value that is described on the X-Telemetry-Agent header section. However, this feature will be implemented soon on all SDKs. Follow Bug 1711928 for updates. Custom pings Applications can define metrics that are sent in custom pings. Unlike the built-in pings, custom pings are sent explicitly by the application. This is useful when the scheduling of the built-in pings (metrics, baseline and events) are not appropriate for your data. Since the timing of the submission of custom pings is handled by the application, the measurement window is under the application's control. This is especially useful when metrics need to be tightly related to one another, for example when you need to measure the distribution of frame paint times when a particular rendering backend is in use. If these metrics were in different pings, with different measurement windows, it is much harder to do that kind of reasoning with much certainty. Defining a custom ping Custom pings must be defined in a pings.yaml file, placed in the same directory alongside your app's metrics.yaml file. For example, to define a custom ping called search specifically for search information: $schema: moz://mozilla.org/schemas/glean/pings/2-0-0

search:
description: >
A ping to record search data.
include_client_id: false
notification_emails:
- CHANGE-ME@example.com
bugs:
- http://bugzilla.mozilla.org/123456789/
data_reviews:
- http://example.com/path/to/data-review


Refer to the pings YAML registry format for a full reference on the pings.yaml file structure.

Sending metrics in a custom ping

To send a metric on a custom ping, you add the custom ping's name to the send_in_pings parameter in the metrics.yaml file.

Ping metadata must be loaded before sending!

After defining a custom ping, before it can be used for sending data, its metadata must be loaded into your application or library.

For example, to define a new metric to record the default search engine, which is sent in a custom ping called search, put search in the send_in_pings parameter. Note that it is an error to specify a ping in send_in_pings that does not also have an entry in pings.yaml.

search.default:
name:
type: string
description: >
The name of the default search engine.
send_in_pings:
- search


If this metric should also be sent in the default ping for the given metric type, you can add the special value default to send_in_pings:

    send_in_pings:
- search
- default


The glean.restarted event

For custom pings that contain event metrics, the glean.restarted event is injected by Glean on every application restart that may happen during the pings measurement window.

Only applies to the Glean JavaScript SDK

The behavior described in this section only applies to custom pings sent by the Glean JavaScript SDK. Follow the implementation of this feature on the other SDKs through Bug 1716725.

Event timestamps throughout application restarts

Event timestamps are always calculated relative to the first event in a ping. The first event will always have timestamp 0 and subsequent events will have timestamps corresponding to the elapsed amount of milliseconds since that first event.

That is also the case for events recorded throughout restarts.

Example

In the below example payload, there were two events recorded on the first application run. The first event is timestamp 0 and the second event happens one second after the first one, so it has timestamp 1000.

The application is restarted one hour after the first event and a glean.restarted event is recorded, timestamp 3600000. Finally, an event is recorded during the second application run two seconds after restart, timestamp 3800000.

{
...
"events": [
{
"timestamp": 0,
"category": "examples",
"name": "event_example",
},
{
"timestamp": 1000,
"category": "examples",
"name": "event_example"
},
{
"timestamp": 3600000,
"category": "glean",
"name": "restarted"
},
{
"timestamp": 3800000,
"category": "examples",
"name": "event_example"
},
]
}


Caveat: Handling decreasing time offsets

For events recorded in a single application run, Glean relies on a monotonically increasing timer to calculate event timestamps, while for calculating the time elapsed between application runs Glean has to rely on the computer clock, which is not necessarily monotonically increasing.

In the case that timestamps in between application runs are not monotonically increasing, Glean will take the value of the previous timestamp and add one millisecond, thus guaranteeing that timestamps are always increasing.

Checking for decreasing time offsets between restarts

When this edge case is hit, Glean records an InvalidValue error for the glean.restarted metric. This metric may be consulted at analysis time. It is sent in the same ping where the error happened.

In the below example payload, the first and second application runs go exactly like in the example above.

The only difference is that when the restart happens, the offset between the absolute time of the first event and the absolute time of the restart is not enough to keep the timestamps increasing. That may happen for many reasons, such as a change in timezones or simply a manual change in the clock by the user.

In this case, Glean will ignore the incorrect timestamp and add one millisecond to the last timestamp of the previous run, in order to keep the monotonically increasing nature of the timestamps.

{
...
"events": [
{
"timestamp": 0,
"category": "examples",
"name": "event_example",
},
{
"timestamp": 1000,
"category": "examples",
"name": "event_example"
},
{
"timestamp": 1001,
"category": "glean",
"name": "restarted"
},
{
"timestamp": 3001,
"category": "examples",
"name": "event_example"
},
]
}


Testing custom pings

Applications defining custom pings can use use the ping testing API to test these pings in unit tests.

General testing strategy

The schedule of custom pings depends on the specific application implementation, since it is up to the SDK user to define the ping semantics. This makes the testing strategy a bit more complex, but usually boiling down to:

1. Triggering the code path that accumulates/records the data.
2. Defining a callback validation function using the ping testing API.
3. Finally triggering the code path that submits the custom ping or submitting the ping using the submit API.

Pings sent by Glean

If data collection is enabled, the Glean SDK provides a set of built-in pings that are assembled out of the box without any developer intervention. The following is a list of these built-in pings:

Applications can also define and send their own custom pings when the schedules of these pings is not suitable.

There is also a high-level overview of how the metrics and baseline pings relate and the timings they record.

Available pings per platform

Language Bindingbaselinedeletion-requesteventsmetrics
Kotlin
Swift
Python
Rust
JavaScript
Firefox Desktop

Defining foreground and background state

These docs refer to application 'foreground' and 'background' state in several places.

Foreground

For Android, this specifically means the activity becomes visible to the user, it has entered the Started state, and the system invokes the onStart() callback.

Background

This specifically means when the activity is no longer visible to the user, it has entered the Stopped state, and the system invokes the onStop() callback.

This may occur, if the user uses Overview button to change to another app, the user presses the Back button and navigates to a previous application or the home screen, or if the user presses the Home button to return to the home screen. This can also occur if the user navigates away from the application through some notification or other means.

The system may also call onStop() when the activity has finished running, and is about to be terminated.

Foreground

For iOS, the Glean SDK attaches to the willEnterForegroundNotification. This notification is posted by the OS shortly before an app leaves the background state on its way to becoming the active app.

Background

For iOS, this specifically means when the app is no longer visible to the user, or when the UIApplicationDelegate receives the applicationDidEnterBackground event.

This may occur if the user opens the task switcher to change to another app, or if the user presses the Home button to show the home screen. This can also occur if the user navigates away from the app through a notification or other means.

Note: Glean does not currently support Scene based lifecycle events that were introduced in iOS 13.

The baseline ping

Description

This ping is intended to provide metrics that are managed by the Glean SDK itself, and not explicitly set by the application or included in the application's metrics.yaml file.

Platform availability

Language BindingKotlinSwiftPythonRustJavaScriptFirefox Desktop
baseline ping

Scheduling

The baseline ping is automatically submitted with a reason: active when the application becomes active (on mobile it means getting to foreground). These baseline pings do not contain duration.

The baseline ping is automatically submitted with a reason: inactive when the application becomes inactive (on mobile it means getting to background). If no baseline ping is triggered when becoming inactive (e.g. the process is abruptly killed) a baseline ping with reason dirty_startup will be submitted on the next application startup. This only happens from the second application start onward.

See also the ping schedules and timing overview.

Contents

The baseline ping also includes the common ping sections found in all pings.

It also includes a number of metrics defined in the Glean SDK itself.

Querying ping contents

A quick note about querying ping contents (i.e. for sql.telemetry.mozilla.org): Each metric in the baseline ping is organized by its metric type, and uses a namespace of glean.baseline. For instance, in order to select duration you would use metrics.timespan['glean.baseline.duration']. If you were trying to select a String based metric such as os, then you would use metrics.string['glean.baseline.os']

Example baseline ping

{
"ping_info": {
"experiments": {
"third_party_library": {
"branch": "enabled"
}
},
"seq": 0,
"start_time": "2019-03-29T09:50-04:00",
"end_time": "2019-03-29T09:53-04:00",
"reason": "foreground"
},
"client_info": {
"telemetry_sdk_build": "0.49.0",
"first_run_date": "2019-03-29-04:00",
"os": "Android",
"android_sdk_version": "27",
"os_version": "8.1.0",
"device_manufacturer": "Google",
"device_model": "Android SDK built for x86",
"architecture": "x86",
"app_build": "1",
"app_display_version": "1.0",
"client_id": "35dab852-74db-43f4-8aa0-88884211e545"
},
"metrics": {
"timespan": {
"glean.baseline.duration": {
"value": 52,
"time_unit": "second"
}
}
}
}


The deletion-request ping

Description

This ping is submitted when a user opts out of sending technical and interaction data.

This ping contains the client id.

This ping is intended to communicate to the Data Pipeline that the user wishes to have their reported Telemetry data deleted. As such it attempts to send itself at the moment the user opts out of data collection, and continues to try and send itself.

Adding secondary ids

It is possible to send secondary ids in the deletion request ping. For instance, if the application is migrating from legacy telemetry to Glean, the legacy client ids can be added to the deletion request ping by creating a metrics.yaml entry for the id to be added with a send_in_pings value of deletion_request.

An example metrics.yaml entry might look like this:

legacy_client_id:
type: uuid
description:
A UUID uniquely identifying the legacy client.
send_in_pings:
- deletion_request
...


Platform availability

Language BindingKotlinSwiftPythonRustJavaScriptFirefox Desktop
deletion-request ping

Scheduling

The deletion-request ping is automatically submitted when upload is disabled in Glean. If upload fails, it is retried after Glean is initialized.

Contents

The deletion-request does not contain additional metrics aside from secondary ids that have been added.

Example deletion-request ping

{
"ping_info": {
"seq": 0,
"start_time": "2019-12-06T09:50-04:00",
"end_time": "2019-12-06T09:53-04:00"
},
"client_info": {
"telemetry_sdk_build": "22.0.0",
"first_run_date": "2019-03-29-04:00",
"os": "Android",
"android_sdk_version": "28",
"os_version": "9",
"device_manufacturer": "Google",
"device_model": "Android SDK built for x86",
"architecture": "x86",
"app_build": "1",
"app_display_version": "1.0",
"client_id": "35dab852-74db-43f4-8aa0-88884211e545"
},
"metrics": {
"uuid": {
"legacy_client_id": "5faffa6d-6147-4d22-a93e-c1dbd6e06171"
}
}
}


The events ping

Description

The events ping's purpose is to transport all of the event metric information. If the application crashes, an events ping is generated next time the application starts with events that were not sent before the crash.

Platform availability

Language BindingKotlinSwiftPythonRustJavaScriptFirefox Desktop
events ping

Scheduling

The events ping is collected under the following circumstances:

1. Normally, it is collected when the application becomes inactive (on mobile, this means going to background), if there are any recorded events to send.

2. When the queue of events exceeds Glean.configuration.maxEvents (default 500).

3. If there are any unsent events found on disk when starting the application. (This results in this ping never containing the glean.restarted event.)

All of these cases are handled automatically, with no intervention or configuration required by the application.

Python and JavaScript caveats

Since the Glean Python and JavaScript SDKs don't have a concept of "going to background", case (1) above does not apply.

Contents

At the top-level, this ping contains the following keys:

• ping_info: The information common to all pings.

• events: An array of all of the events that have occurred since the last time the events ping was sent.

Each entry in the events array is an object with the following properties:

• "timestamp": The milliseconds relative to the first event in the ping.

• "category": The category of the event, as defined by its location in the metrics.yaml file.

• "name": The name of the event, as defined in the metrics.yaml file.

• "extra" (optional): A mapping of strings to strings providing additional data about the event. The keys are restricted to 40 characters and values in this map will never exceed 100 characters.

Example event JSON

{
"ping_info": {
"experiments": {
"third_party_library": {
"branch": "enabled"
}
},
"seq": 0,
"start_time": "2019-03-29T09:50-04:00",
"end_time": "2019-03-29T10:02-04:00"
},
"client_info": {
"telemetry_sdk_build": "0.49.0",
"first_run_date": "2019-03-29-04:00",
"os": "Android",
"android_sdk_version": "27",
"os_version": "8.1.0",
"device_manufacturer": "Google",
"device_model": "Android SDK built for x86",
"architecture": "x86",
"app_build": "1",
"app_display_version": "1.0",
"client_id": "35dab852-74db-43f4-8aa0-88884211e545"
},
"events": [
{
"timestamp": 0,
"category": "examples",
"name": "event_example",
"extra": {
"metadata1": "extra",
"metadata2": "more_extra"
}
},
{
"timestamp": 1000,
"category": "examples",
"name": "event_example"
}
]
}


The metrics ping

Description

The metrics ping is intended for all of the metrics that are explicitly set by the application or are included in the application's metrics.yaml file (except events). The reported data is tied to the ping's measurement window, which is the time between the collection of two metrics pings. Ideally, this window is expected to be about 24 hours, given that the collection is scheduled daily at 04:00. However, the metrics ping is only submitted while the application is actually running, so in practice, it may not meet the 04:00 target very frequently. Data in the ping_info section of the ping can be used to infer the length of this window and the reason that triggered the ping to be submitted. If the application crashes, unsent recorded metrics are sent along with the next metrics ping.

Additionally, it is undesirable to mix metric recording from different versions of the application. Therefore, if a version upgrade is detected, the metrics ping is collected immediately before further metrics from the new version are recorded.

Platform availability

Language BindingKotlinSwiftPythonRustJavaScriptFirefox Desktop
metrics ping

Scheduling

The desired behavior is to collect the ping at the first available opportunity after 04:00 local time on a new calendar day, but given constraints of the platform, it can only be submitted while the application is running. This breaks down into three scenarios:

1. the application was just installed;
2. the application was just upgraded (the version of the app is different from the last time the app was run);
3. the application was just started (after a crash or a long inactivity period);
4. the application was running at 04:00.

In the first case, since the application was just installed, if the due time for the current calendar day has passed, a metrics ping is immediately generated and scheduled for sending (reason code overdue). Otherwise, if the due time for the current calendar day has not passed, a ping collection is scheduled for that time (reason code today).

In the second case, if a version change is detected at startup, the metrics ping is immediately submitted so that metrics from one version are not aggregated with metrics from another version (reason code upgrade).

In the third case, if the metrics ping was not already collected on the current calendar day, and it is before 04:00, a collection is scheduled for 04:00 on the current calendar day (reason code today). If it is after 04:00, a new collection is scheduled immediately (reason code overdue). Lastly, if a ping was already collected on the current calendar day, the next one is scheduled for collecting at 04:00 on the next calendar day (reason code tomorrow).

In the fourth and last case, the application is running during a scheduled ping collection time. The next ping is scheduled for 04:00 the next calendar day (reason code reschedule).

More scheduling examples are included below.

See also the ping schedules and timing overview.

Contents

The metrics ping contains all of the metrics defined in metrics.yaml (except events) that don't specify a ping or where default is specified in their send in pings property.

Additionally, error metrics in the glean.error category are included in the metrics ping.

The metrics ping shall also include the common ping_info and 'client_info' sections. It also includes a number of metrics defined in the Glean SDK itself.

Querying ping contents

Information about query ping contents is available in Accessing Glean data in the Firefox data docs.

Scheduling Examples

Crossing due time with the application closed

1. The application is opened on Feb 7 on 15:00, closed on 15:05.

• Glean records one metric A (say startup time in ms) during this measurement window MW1.
2. The application is opened again on Feb 8 on 17:00.

• Glean notes that we passed local 04:00 since MW1.

• Glean closes MW1, with:

• start_time=Feb7/15:00;
• end_time=Feb8/17:00.
• Glean records metric A again, into MW2, which has a start_time of Feb8/17:00.

Crossing due time and changing timezones

1. The application is opened on Feb 7 on 15:00 in timezone UTC, closed on 15:05.

• Glean records one metric A (say startup time in ms) during this measurement window MW1.
2. The application is opened again on Feb 8 on 17:00 in timezone UTC+1.

• Glean notes that we passed local 04:00 UTC+1 since MW1.

• Glean closes MW1, with:

• start_time=Feb7/15:00/UTC;
• end_time=Feb8/17:00/UTC+1.
• Glean records metric A again, into MW2.

The application doesn’t run in a week

1. The application is opened on Feb 7 on 15:00 in timezone UTC, closed on 15:05.

• Glean records one metric A (say startup time in ms) during this measurement window MW1.
2. The application is opened again on Feb 16 on 17:00 in timezone UTC.

• Glean notes that we passed local 04:00 UTC since MW1.

• Glean closes MW1, with:

• start_time=Feb7/15:00/UTC;
• end_time=Feb16/17:00/UTC.
• Glean records metric A again, into MW2.

The application doesn’t run for a week, and when it’s finally re-opened the timezone has changed

1. The application is opened on Feb 7 on 15:00 in timezone UTC, closed on 15:05.

• Glean records one metric A (say startup time in ms) during this measurement window MW1.
2. The application is opened again on Feb 16 on 17:00 in timezone UTC+1.

• Glean notes that we passed local 04:00 UTC+1 since MW1.

• Glean closes MW1, with:

• start_time=Feb7/15:00/UTC
• end_time=Feb16/17:00/UTC+1.
• Glean records metric A again, into MW2.

The user changes timezone in an extreme enough fashion that they cross 04:00 twice on the same date

1. The application is opened on Feb 7 at 15:00 in timezone UTC+11, closed at 15:05.

• Glean records one metric A (say startup time in ms) during this measurement window MW1.
2. The application is opened again on Feb 8 at 04:30 in timezone UTC+11.

• Glean notes that we passed local 04:00 UTC+11.

• Glean closes MW1, with:

• start_time=Feb7/15:00/UTC+11;
• end_time=Feb8/04:30/UTC+11.
• Glean records metric A again, into MW2.

3. The user changes to timezone UTC-10 and opens the application at Feb 7 at 22:00 in timezone UTC-10

• Glean records metric A again, into MW2 (not MW1, which was already sent).
4. The user opens the application at Feb 8 05:00 in timezone UTC-10

• Glean notes that we have not yet passed local 04:00 on Feb 9
• Measurement window MW2 remains the current measurement window
5. The user opens the application at Feb 9 07:00 in timezone UTC-10

• Glean notes that we have passed local 04:00 on Feb 9

• Glean closes MW2 with:

• start_time=Feb8/04:30/UTC+11;
• end_time=Feb9/19:00/UTC-10.
• Glean records metric A again, into MW3.

Ping schedules and timings overview

Full reference details about the metrics and baseline ping schedules are detailed elsewhere.

The following diagram shows a typical timeline of a mobile application, when pings are sent and what timing-related information is included.

There are two distinct runs of the application, where the OS shutdown the application at the end of Run 1, and the user started it up again at the beginning of Run 2.

There are three distinct foreground sessions, where the application was visible on the screen and the user was able to interact with it.

The rectangles for the baseline and metrics pings represent the measurement windows of those pings, which always start exactly at the end of the preceding ping. The ping_info.start_time and ping_info.end_time metrics included in these pings correspond to these beginning and the end of their measurement windows.

The baseline.duration metric (included only in baseline pings) corresponds to amount of time the application spent on the foreground, which, since measurement window always extend to the next ping, is not always the same thing as the baseline ping's measurement window.

The submission_timestamp is the time the ping was received at the telemetry endpoint, added by the ingestion pipeline. It is not exactly the same as ping_info.end_time, since there may be various networking and system latencies both on the client and in the ingestion pipeline (represented by the dotted horizontal line, not to scale). Also of note is that start_time/end_time are measured using the client's real-time clock in its local timezone, which is not a fully reliable source of time.

The "Baseline 4" ping illustrates an important corner case. When "Session 2" ended, the OS also shut down the entire process, and the Glean SDK did not have an opportunity to send a baseline ping immediately. In this case, it is sent at the next available opportunity when the application starts up again in "Run 2". This baseline ping is annotated with the reason code dirty_startup.

The "Metrics 2" ping likewise illustrates another important corner case. "Metrics 1" was able to be sent at the target time of 04:00 (local device time) because the application was currently running. However, the next time 04:00 came around, the application was not active, so the Glean SDK was unable to send a metrics ping. It is sent at the next available opportunity, when the application starts up again in "Run 2". This metrics ping is annotated with the reason code overdue.

Debugging products using the Glean SDK

Glean provides a few debugging features to assist with debugging a product using Glean.

Features

Log Pings

Print the ping payload upon sending a ping.

Debug View Tag

Tags all outgoing pings as debug pings to make them available for real-time validation, on the Glean Debug View.

Glean Debug View

The Glean Debug View enables you to easily see in real-time what data your application is sending.

This data is what actually arrives in our data pipeline, shown in a web interface that is automatically updated when new data arrives. Any data sent from a Glean-instrumented application usually shows up within 10 seconds, updating the pages automatically. Pings are retained for 3 weeks.

Troubleshooting

If nothing is showing up on the dashboard after you set a debugViewTag and you see Glean must be enabled before sending pings. in the logs, Glean is disabled. Check with the application author on how to re-enable it.

Source Tags

Tags outgoing pings with a maximum of 5 comma-separated tags.

Send Ping

Sends a ping on demand.

Debugging methods

Each language binding or platform supported may expose one or more of the following methods to interact with and enable these debugging functionalities.

1. Enable debugging features through APIs exposed through the Glean singleton;
2. Enable debugging features through environment variables set at runtime;
3. Enable debugging features through platform specific tooling.

For methods 1. and 2., refer to the API reference section "Debugging" for detailed information on how to use them.

For method 3. please refer to the platform specific pages on how to debug products using Glean.

Available debugging methods per platform

Glean APIEnvironment VariablesPlatform Specific Tooling
Kotlin1
Swift2
Python
Rust
JavaScript
Firefox Desktop3
1

In Kotlin, the Glean SDK exposes the GleanDebugActivity for interacting with debug features. Although it is technically possible to also use environment variables in Android, the Glean team is not aware of a proper way to set environment variables in Android devices or emulators.

2

In Swift, the Glean SDK exposes a custom URL format for interacting with debug features.

3

In Firefox Desktop, developers may use the interface exposed through about:glean to log, tag or send pings.

Debugging Android applications using the Glean SDK

The Glean SDK exports the GleanDebugActivity that can be used to toggle debugging features on or off. Users can invoke this special activity, at run-time, using the following adb command:

adb shell am start -n [applicationId]/mozilla.telemetry.glean.debug.GleanDebugActivity [extra keys]


In the above:

• [applicationId] is the product's application id as defined in the manifest file and/or build script. For the Glean sample application, this is org.mozilla.samples.gleancore for a release build and org.mozilla.samples.gleancore.debug for a debug build.

• [extra keys] is a list of extra keys to be passed to the debug activity. See the documentation for the command line switches used to pass the extra keys. These are the currently supported keys:

keytypedescription
logPingsboolean (--ez)If set to true, pings are dumped to logcat; defaults to false
debugViewTagstring (--es)Tags all outgoing pings as debug pings to make them available for real-time validation, on the Glean Debug View. The value must match the pattern [a-zA-Z0-9-]{1,20}. Important: in older versions of the Glean SDK, this was named tagPings
sourceTagsstring array (--esa)Tags outgoing pings with a maximum of 5 comma-separated tags. The tags must match the pattern [a-zA-Z0-9-]{1,20}. The automation tag is meant for tagging pings generated on automation: such pings will be specially handled on the pipeline (i.e. discarded from non-live views). Tags starting with glean are reserved for future use. Subsequent calls of this overwrite any previously stored tag
sendPingstring (--es)Sends the ping with the given name immediately
startNextstring (--es)The name of an exported Android Activity, as defined in the product manifest file, to start right after the GleanDebugActivity completes. All the options provided are propagated to this next activity as well. When omitted, the default launcher activity for the product is started instead.

All the options provided to start the activity are passed over to the main activity for the application to process. This is useful if SDK users wants to debug telemetry while providing additional options to the product to enable specific behaviors.

Note: Due to limitations on Android logcat message size, pings larger than 4KB are broken into multiple log messages when using logPings.

For example, to direct a release build of the Glean sample application to (1) dump pings to logcat, (2) tag the ping with the test-metrics-ping tag, and (3) send the "metrics" ping immediately, the following command can be used:

adb shell am start -n org.mozilla.samples.gleancore/mozilla.telemetry.glean.debug.GleanDebugActivity \
--ez logPings true \
--es sendPing metrics \
--es debugViewTag test-metrics-ping


The logPings command doesn't trigger ping submission and you won't see any output until a ping has been sent. You can use the sendPing command to force a ping to be sent, but it could be more desirable to trigger the pings submission on their normal schedule. For instance, the baseline and events pings can be triggered by moving the app out of the foreground and the metrics ping can be triggered normally if it is overdue for the current calendar day.

Note: The device or emulator must be connected to the internet for this to work. Otherwise the job that sends the pings won't be triggered.

If no metrics have been collected, no pings will be sent unless send_if_empty is set on your ping. See the ping documentation for more information on ping scheduling to learn when pings are sent.

Options that are set using the adb flags are not immediately reset and will persist until the application is closed or manually reset.

Glean SDK Log messages

When running a Glean SDK-powered app in the Android emulator or on a device connected to your computer via cable, there are several ways to read the log output.

Android Studio

Android Studio can show the logs of a connected emulator or device. To display the log messages for an app:

1. Run an app on your device.
2. Click View > Tool Windows > Logcat (or click Logcat in the tool window bar).

The Logcat window will show all log messages and allows to filter those by the application ID. Select the application ID of the product you're debugging. You can also filter by Glean only.

More information can be found in the View Logs with Logcat help article.

Command line

On the command line you can show all of the log output using:

adb logcat


This is the unfiltered output of all log messages. You can match for glean using grep:

adb logcat | grep -i glean


A simple way to filter for only the application that is being debugged is by using pidcat, a wrapper around adb, which adds colors and proper filtering by application ID and log level. Run it like this to filter for an application:

pidcat [applicationId]


In the above [applicationId] is the product's application id as defined in the manifest file and/or build script. For the Glean sample application, this is org.mozilla.samples.gleancore for a release build and org.mozilla.samples.gleancore.debug for a debug build.

Debugging iOS applications using the Glean SDK

Enabling debugging features in iOS through environment variables

Debugging features in iOS can be enabled using environment variables. For more information on the available features accessible through this method and how to enable them, see Debugging API reference.

These environment variables must be set on the device that is running the application.

Enabling debugging features in iOS through a custom URL scheme

For debugging and validation purposes on iOS, the Glean SDK makes use of a custom URL scheme which is implemented within the application that is consuming the Glean SDK. The Glean SDK provides some convenience functions to facilitate this, but it's up to the consuming application to enable this functionality. Applications that enable this Glean SDK feature will be able to launch the application from a URL with the Glean debug commands embedded in the URL itself.

Available commands and query format

All 4 Glean debugging features are available through the custom URL scheme tool.

• logPings: This is either true or false and will cause pings that are submitted to also be echoed to the device's log.
• debugViewTag: This command will tag outgoing pings with the provided value, in order to identify them in the Glean Debug View.
• sourceTags: This command tags outgoing pings with a maximum of 5 comma-separated tags.
• sendPing: This command expects a string name of a ping to force immediate collection and submission of.

The structure of the custom URL uses the following format:

<protocol>://glean?<command 1>=<paramter 1>&<command 2>=<parameter 2> ...

Where:

• <protocol> is the "URL Scheme" that has been added for your app (see Instrumenting the application below), such as glean-sample-app.
• This is followed by :// and then glean which is required for the Glean SDK to recognize the command is meant for it to process.
• Following standard URL query format, the next character after glean is the ? indicating the beginning of the query.
• This is followed by one or more queries in the form of <command>=<parameter>, where the command is one of the commands listed above, followed by an = and then the value or parameter to be used with the command.

There are a few things to consider when creating the custom URL:

• Invalid commands will log an error and cause the entire URL to be ignored.
• Not all commands are required to be encoded in the URL, you can mix and match the commands that you need.
• Multiple instances of commands are not allowed in the same URL and, if present, will cause the entire URL to be ignored.
• The logPings command doesn't trigger ping submission and you won't see any output until a ping has been submitted. You can use the sendPing command to force a ping to be sent, but it could be more desirable to trigger the pings submission on their normal schedule. For instance, the baseline and events pings can be triggered by moving the app out of the foreground and the metrics ping can be triggered normally if it is overdue for the current calendar day. See the ping documentation for more information on ping scheduling to learn when pings are sent.
• Enabling debugging features through custom URLs overrides any debugging features set through environment variables.

Instrumenting the application for Glean SDK debug functionality

In order to enable the debugging features in a Glean SDK consuming iOS application, it is necessary to add some information to the application's Info.plist, and add a line and possibly an override for a function in the AppDelegate.swift.

Register custom URL scheme in Info.plist

Note: If your application already has a custom URL scheme implemented, there is no need to implement a second scheme, you can simply use that and skip to the next section about adding the convenience method. If the app doesn't have a custom URL scheme implemented, then you will need to perform the following instructions to register your app to receive custom URLs.

Find and open the application's Info.plist and right click any blank area and select Add Row to create a new key.

You will be prompted to select a key from a drop-down menu, scroll down to and select URL types. This creates an array item, which can be expanded by clicking the triangle disclosure icon.

Select Item 0, click on it and click the disclosure icon to expand it and show the URL identifier line. Double-click the value field and fill in your identifier, typically the same as the bundle ID.

Right-click on Item 0 and select Add Row from the context menu. In the dropdown menu, select URL Schemes to add the item.

Click on the disclosure icon of URL Schemes to expand the item, double-click the value field of Item 0 and key in the value for your application's custom scheme. For instance, the Glean sample app uses glean-sample-app, which allows for custom URLs to be crafted using that as a protocol, for example: glean-sample-app://glean?logPings=true

Add the Glean.handleCustomUrl() convenience function and necessary overrides

In order to handle the incoming Glean SDK debug commands, it is necessary to implement the override in the application's AppDelegate.swift file. Within that function, you can make use of the convenience function provided in Glean handleCustomUrl(url: URL).

An example of a simple implementation of this would look like this:

func application(_: UIApplication,
open url: URL,
options _: [UIApplication.OpenURLOptionsKey: Any] = [:]) -> Bool {
// ...

// This does nothing if the url isn't meant for Glean.
Glean.shared.handleCustomUrl(url: url)

// ...

return true
}


If you need additional help setting up a custom URL scheme in your application, please refer to Apple's documentation.

Invoking the Glean-iOS debug commands

Now that the app has the Glean SDK debug functionality enabled, there are a few ways in which we can invoke the debug commands.

Using a web browser

Perhaps the simplest way to invoke the Glean SDK debug functionality is to open a web browser and type/paste the custom URL into the address bar. This is especially useful on an actual device because there isn't a good way to launch from the command line and process the URL for an actual device.

Using the glean-sample-app as an example: to activate ping logging, tag the pings to go to the Glean Debug View, and force the events ping to be sent, enter the following URL in a web browser on the iOS device:

glean-sample-app://glean?logPings=true&debugViewTag=My-ping-tag&sendPing=events


This should cause iOS to prompt you with a dialog asking if you want to open the URL in the Glean Sample App, and if you select "Okay" then it will launch (or resume if it's already running) the application with the indicated commands and parameters and immediately force the collection and submission of the events ping.

Note: This method does not work if the browser you are using to input the command is the same application you are attempting to pass the Glean debug commands to. So, you couldn't use Firefox for iOS to trigger commands within Firefox for iOS.

It is also possible to encode the URL into a 2D barcode or QR code and launch the app via the camera app. After scanning the encoded URL, the dialog prompting to launch the app should appear as if the URL were entered into the browser address bar.

Using the command line

This method is useful for testing via the Simulator, which typically requires a Mac with Xcode installed, including the Xcode command line tools. In order to perform the same command as above with using the browser to input the URL, you can use the following command in the command line terminal of the Mac:

xcrun simctl openurl booted "glean-sample-app://glean?logPings=true&debugViewTag=My-ping-tag&sendPing=events"


This will launch the simulator and again prompt the user with a dialog box asking if you want to open the URL in the Glean Sample App (or whichever app you are instrumenting and testing).

Glean log messages

The Glean SDK integrates with the unified logging system available on iOS. There are various ways to retrieve log information, see the official documentation.

If debugging in the simulator, the logging messages can be seen in the console window within Xcode.

When running a Glean-powered app in the iOS Simulator or on a device connected to your computer via cable you can use Console.app to view the system log. You can filter the logs with category:glean to only see logs from the Glean SDK.

You can also use the command line utility log to stream the log output. Run the following in a shell:

log stream --predicate 'category contains "glean"'


See Diagnosing Issues Using Crash Reports and Device Logs for more information about debugging deployed iOS apps.

Debugging Python applications using the Glean SDK

Debugging features in Python can be enabled using environment variables. For more information on the available features and how to enable them, see the Debugging API reference.

Sending pings

Unlike other platforms, Python doesn't expose convenience methods to send pings on demand.

In case that is necessary, calling the submit function for a given ping, such as pings.custom_ping.submit(), will send it.

Logging pings

If the GLEAN_LOG_PINGS environment variable is set to true, pings are logged to the console on DEBUG level whenever they are submitted.

Make sure that when you configure logging in your application, you set the level for the Python logging library to DEBUG or higher. Otherwise pings won't be logged even if GLEAN_LOG_PINGS is set to true.

You can set the logging level for the Python logging to DEBUG as follows:

import logging

logging.basicConfig(level=logging.DEBUG)


All log messages from the Glean SDK are on the glean logger, so if you need to control it independently, you can set a level for just the Glean SDK (but note that the global Python logging level also needs to be set as above):

logging.getLogger("glean").setLevel(logging.DEBUG)


See the Python logging documentation for more information.

Debugging JavaScript applications using Glean.js

Debugging features in JavaScript can be enabled through APIs exposed on the Glean object. For more information on the available features and how to enable them, see the Debugging API reference.

Sending pings

Unlike other platforms, JavaScript doesn't expose convenience methods to send pings on demand.

In case that is necessary, calling the submit function for a given ping, such as pings.customPing.submit(), will send it.

Note that this method is only effective for custom pings. Glean internal pings are not exposed to users.

Logging pings

By calling Glean.logPings(true) all subsequent pings sent will be logged to the console.

To access the logs for web extensions on

Firefox

1. Go to about:debugging#/runtime/this-firefox;
2. Find the extension you want to see the logs for;
3. Click on Inspect.

Chromium-based browsers

1. Go to chrome://extensions;
2. Find the extension you want to see the logs for;
3. Click on background page.

YAML Registry Format

User defined Glean pings and metrics are declared in YAML files, which must be parsed by glean_parser to generate public APIs for said metrics and pings.

These files also serve the purpose of documenting metrics and pings. They are consumed by the probe-scraper tool, which generates a REST API to access metrics and pings information consumed by most other tools in the Glean ecosystem, such as GLAM and the Glean Dictionary.

Moreover, for products that do not wish to use the Glean Dictionary as their metrics and pings documentation source, glean_parser provides an option to generate Markdown documentation for metrics and pings based on these files. For more information of that, refer to the help output of the translate command, by running in your terminal:

$glean_parser translate --help  metrics.yaml file For a full reference on the metrics.yaml format, refer to the Metrics YAML Registry Format page. pings.yaml file For a full reference on the pings.yaml format, refer to the Pings YAML Registry Format page. Metrics YAML Registry Format Metrics sent by an application or library are defined in YAML files which follow the metrics.yaml JSON schema. This files must be parsed by glean_parser at build time in order to generate code in the target language (e.g. Kotlin, Swift, ...). The generated code is what becomes the public API to access the project's metrics. For more information on how to introduce the glean_parser build step for a specific language / environment, refer to the "Adding Glean to your project" section of this book. Note on the naming of these files Although we refer to metrics definitions YAML files as metrics.yaml throughout Glean documentation this files may be named whatever makes the most sense for each project and may even be broken down into multiple files, if necessary. File structure --- # Schema$schema: moz://mozilla.org/schemas/glean/metrics/2-0-0

# Category
toolbar:
# Name
click:
# Metric Parameters
type: event
description: |
Event to record toolbar clicks.
notification_emails:
- CHANGE-ME@example.com
bugs:
- https://bugzilla.mozilla.org/123456789/
data_reviews:
- http://example.com/path/to/data-review
expires: 2019-06-01

double_click:
...


Schema

Declaring the schema at the top of a metrics definitions file is required, as it is what indicates that the current file is a metrics definitions file.

Category

Categories are the top-level keys on metrics definition files. One single definition file may contain multiple categories grouping multiple metrics. They serve the purpose of grouping related metrics in a project.

Categories can contain alphanumeric lower case characters as well as the . and _ characters which can be used to provide extra structure, for example category.subcategory is a valid category. Category lengths may not exceed 40 characters.

Categories may not start with the string glean. That prefix is reserved for Glean internal metrics.

See the "Capitalization" note to understand how the category is formatted in generated code.

Name

Metric names are the second-level keys on metrics definition files.

Names may contain alphanumeric lower case characters as well as the _ character. Metric name lengths may not exceed 30 characters.

"Capitalization" rules also apply to metric names on generated code.

Metric parameters

Specific metric types may have special required parameters in their definition, these parameters are documented in each "Metric Type" reference page.

Following are the parameters common to all metric types.

Required parameters

type

Specifies the type of a metric, like "counter" or "event". This defines which operations are valid for the metric, how it is stored and how data analysis tooling displays it. See the list of supported metric types.

Types should not be changed after release

Once a metric is released in a product, its type should not be changed. If any data was collected locally with the older type, and hasn't yet been sent in a ping, recording data with the new type may cause any old persisted data to be lost for that metric. See this comment for an extended explanation of the different scenarios.

This does not apply to users on the Glean JavaScript SDK. The issues on changing a metrics type described above, do not apply for that SDK.

description

A textual description of the metric for humans. It should describe what the metric does, what it means for analysts, and its edge cases or any other helpful information.

The description field may contain markdown syntax.

Imposed limits on line length

The Glean linter uses a line length limit of 80 characters. If your description is longer, e.g. because it includes longer links, you can disable yamllint using the following annotations (and make sure to enable yamllint again as well):

# yamllint disable
description: |
Your extra long description, that's longer than 80 characters by far.
# yamllint enable


notification_emails

A list of email addresses to notify for important events with the metric or when people with context or ownership for the metric need to be contacted.

For example when a metric's expiration is within in 14 days, emails will be sent from telemetry-alerts@mozilla.com to the notification_emails addresses associated with the metric.

Consider adding both a group email address and an individual who is responsible for this metric.

bugs

A list of bugs (e.g. Bugzilla or GitHub) that are relevant to this metric. For example, bugs that track its original implementation or later changes to it.

Each entry should be the full URL to the bug in an issue tracker. The use of numbers alone is deprecated and will be an error in the future.

data_reviews

A list of URIs to any data collection review responses relevant to the metric.

expires

When the metric is set to expire.

After a metric expires, an application will no longer collect or send data related to it. May be one of the following values:

• <build date>: An ISO date yyyy-mm-dd in UTC on which the metric expires. For example, 2019-03-13. This date is checked at build time. Except in special cases, this form should be used so that the metric automatically "sunsets" after a period of time. Emails will be sent to the notification_emails addresses when the metric is about to expire. Generally, when a metric is no longer needed, it should simply be removed. This does not affect the availability of data already collected by the pipeline.
• never: This metric never expires.
• expired: This metric is manually expired.

Optional parameters

lifetime

default: ping

Defines the lifetime of the metric. Different lifetimes affect when the metrics value is reset.

ping (default)

The metric is cleared each time it is submitted in the ping. This is the most common case, and should be used for metrics that are highly dynamic, such as things computed in response to the user's interaction with the application.

application

The metric is related to an application run, and is cleared after the application restarts and any Glean-owned ping, due at startup, is submitted. This should be used for things that are constant during the run of an application, such as the operating system version. In practice, these metrics are generally set during application startup. A common mistake--- using the ping lifetime for these type of metrics---means that they will only be included in the first ping sent during a particular run of the application.

user

Reach out to the Glean team before using this.

The metric is part of the user's profile and will live as long as the profile lives. This is often not the best choice unless the metric records a value that really needs to be persisted for the full lifetime of the user profile, e.g. an identifier like the client_id, the day the product was first executed. It is rare to use this lifetime outside of some metrics that are built in to the Glean SDK.

send_in_pings

default: events|metrics

Defines which pings the metric should be sent on. If not specified, the metric is sent on the "default ping", which is the events ping for events and the metrics ping for everything else.

Most metrics don't need to specify this unless they are sent on custom pings.

disabled

default: false

Data collection for this metric is disabled.

This is useful when you want to temporarily disable the collection for a specific metric without removing references to it in your source code.

Generally, when a metric is no longer needed, it should simply be removed. This does not affect the availability of data already collected by the pipeline.

version

default: 0

The version of the metric. A monotonically increasing integer value. This should be bumped if the metric changes in a backward-incompatible way.

data_sensitivity

default: []

A list of data sensitivity categories that the metric falls under. There are four data collection categories related to data sensitivity defined in Mozilla's data collection review process:

Category 1: Technical Data (technical)

Information about the machine or Firefox itself. Examples include OS, available memory, crashes and errors, outcome of automated processes like updates, safe browsing, activation, versions, and build id. This also includes compatibility information about features and APIs used by websites, add-ons, and other 3rd-party software that interact with Firefox during usage.

Category 2: Interaction Data (interaction)

Information about the user’s direct engagement with Firefox. Examples include how many tabs, add-ons, or windows a user has open; uses of specific Firefox features; session length, scrolls and clicks; and the status of discrete user preferences.

Category 3: Web activity data (web_activity)

Information about user web browsing that could be considered sensitive. Examples include users’ specific web browsing history; general information about their web browsing history (such as TLDs or categories of webpages visited over time); and potentially certain types of interaction data about specific webpages visited.

Category 4: Highly sensitive data (highly_sensitive)

Information that directly identifies a person, or if combined with other data could identify a person. Examples include e-mail, usernames, identifiers such as google ad id, apple id, Firefox account, city or country (unless small ones are explicitly filtered out), or certain cookies. It may be embedded within specific website content, such as memory contents, dumps, captures of screen data, or DOM data.

Pings YAML Registry Format

Custom pings sent by an application or library are defined in YAML files which follow the pings.yaml JSON schema.

This files must be parsed by glean_parser at build time in order to generate code in the target language (e.g. Kotlin, Swift, ...). The generated code is what becomes the public API to access the project's custom pings.

For more information on how to introduce the glean_parser build step for a specific language / environment, refer to the "Adding Glean to your project" section of this book.

Note on the naming of these files

Although we refer to pings definitions YAML files as pings.yaml throughout Glean documentation this files may be named whatever makes the most sense for each project and may even be broken down into multiple files, if necessary.

File structure

---
# Schema
$schema: moz://mozilla.org/schemas/glean/pings/2-0-0 # Name search: # Ping parameters description: > A ping to record search data. include_client_id: false notification_emails: - CHANGE-ME@example.com bugs: - http://bugzilla.mozilla.org/123456789/ data_reviews: - http://example.com/path/to/data-review  Schema Declaring the schema at the top of a pings definitions file is required, as it is what indicates that the current file is a pings definitions file. Name Ping names are the top-level keys on pings definitions files. One single definition file may contain multiple ping declarations. Ping names are limited to lowercase letters from the ISO basic Latin alphabet and hyphens and a maximum of 30 characters. Pings may not contain the words custom or ping in their names. These are considered redundant words and will trigger a REDUNDANT_PING lint failure on glean_parser. "Capitalization" rules apply to ping names on generated code. Reserved ping names The names baseline, metrics, events, deletion-request and all-pings are reserved and may not be used as the name of a custom ping. Ping parameters Required parameters description A textual description of the purpose of the ping. It may contain markdown syntax. include_client_id A boolean indicating whether to include the client_id in the client_info section of the ping. notification_emails A list of email addresses to notify for important events with the ping or when people with context or ownership for the ping need to be contacted. Consider adding both a group email address and an individual who is responsible for this ping. bugs A list of bugs (e.g. Bugzilla or GitHub) that are relevant to this ping. For example, bugs that track its original implementation or later changes to it. Each entry should be the full URL to the bug in an issue tracker. The use of numbers alone is deprecated and will be an error in the future. data_reviews A list of URIs to any data collection review responses relevant to the metric. Optional parameters send_if_empty default: false A boolean indicating if the ping is sent if it contains no metric data. reasons default: {} The reasons that this ping may be sent. The keys are the reason codes, and the values are a textual description of each reason. The ping payload will (optionally) contain one of these reasons in the ping_info.reason field. The General API The Glean SDK has a minimal API available on its top-level Glean object called the General API. This API allows, among other things, to enable and disable upload, register custom pings and set experiment data. Only initialize in the main application! The Glean SDK should only be initialized from the main application, not individual libraries. If you are adding Glean SDK support to a library, you can safely skip this section. The API The Glean SDK provides a general API that supports the following operations. See below for language-specific details. OperationDescriptionNotes initializeConfigure and initialize the Glean SDK.Initializing the Glean SDK setUploadEnabledEnable or disable Glean collection and upload.Toggling upload status registerPingsRegister custom pings generated from pings.yaml.Custom pings setExperimentActiveIndicate that an experiment is running.Using the Experiments API setExperimentInactiveIndicate that an experiment is no longer running..Using the Experiments API Initializing The following steps are required for applications using the Glean SDK, but not libraries. Note The initialize function must be called, even if telemetry upload is disabled. Glean needs to perform maintenance tasks even when telemetry is disabled, and because Glean does this as part of its initialization, it is required to always call the initialize function. Otherwise, Glean won't be able to clean up collected data, disable queuing of pre-init tasks, or perform other required operations. This does not apply to special builds where telemetry is disabled at build time. In that case, it is acceptable to not call initialize at all. Initialize Glean with the correct value for uploadEnabled! Glean.initialize must always be called with real values. Always pass the user preference, e.g. Glean.initialize(upload=userSettings.telemetry_enabled) or the equivalent for your application. Never call Glean.initialize(upload=true) if true is a placeholder value that later gets reset by Glean.setUploadEnabled(false). Depending on the provided placeholder value, this might trigger the generation of new client ids or the submission of bogus deletion-request pings. Multiple processes support The Glean SDK does not support use across multiple processes, and must only be initialized on the application's main process. Initializing in other processes is a no-op. Additionally, Glean must be initialized on the main (UI) thread of the applications main process. Failure to do so will throw an IllegalThreadStateException. An excellent place to initialize Glean is within the onCreate method of the class that extends Android's Application class. import org.mozilla.yourApplication.GleanMetrics.GleanBuildInfo import org.mozilla.yourApplication.GleanMetrics.Pings class SampleApplication : Application() { override fun onCreate() { super.onCreate() // If you have custom pings in your application, you must register them // using the following command. This command should be omitted for // applications not using custom pings. Glean.registerPings(Pings) // Initialize the Glean library. Glean.initialize( applicationContext, // Here, settings() is a method to get user preferences, specific to // your application and not part of the Glean SDK API. uploadEnabled = settings().isTelemetryEnabled, buildInfo = GleanBuildInfo.buildInfo ) } }  The Glean SDK should be initialized as soon as possible, and importantly, before any other libraries in the application start using Glean. Library code should never call Glean.initialize, since it should be called exactly once per application. Uploads when using Android Components When the Glean SDK is consumed through Android Components, it is required to configure an HTTP client to be used for upload. For example: // Requires org.mozilla.components:concept-fetch import mozilla.components.concept.fetch.Client // Requires org.mozilla.components:lib-fetch-httpurlconnection. // This can be replaced by other implementations, e.g. lib-fetch-okhttp // or an implementation from browser-engine-gecko. import mozilla.components.lib.fetch.httpurlconnection.HttpURLConnectionClient import mozilla.components.service.glean.config.Configuration import mozilla.components.service.glean.net.ConceptFetchHttpUploader val httpClient = ConceptFetchHttpUploader(lazy { HttpURLConnectionClient() as Client }) val config = Configuration(httpClient = httpClient) Glean.initialize( context, uploadEnabled = true, configuration = config, buildInfo = GleanBuildInfo.buildInfo )  Multiple processes support The Glean SDK does not support use across multiple processes, and must only be initialized on the application's main process. An excellent place to initialize Glean is within the application(_:) method of the class that extends the UIApplicationDelegate class. import Glean import UIKit @UIApplicationMain class AppDelegate: UIResponder, UIApplicationDelegate { func application(_: UIApplication, didFinishLaunchingWithOptions _: [UIApplication.LaunchOptionsKey: Any]?) -> Bool { // If you have custom pings in your application, you must register them // using the following command. This command should be omitted for // applications not using custom pings. Glean.shared.registerPings(GleanMetrics.Pings) // Initialize the Glean library. Glean.shared.initialize( // Here, Settings is a method to get user preferences specific to // your application, and not part of the Glean SDK API. uploadEnabled = Settings.isTelemetryEnabled ) } }  The Glean SDK should be initialized as soon as possible, and importantly, before any other libraries in the application start using Glean. Library code should never call Glean.shared.initialize, since it should be called exactly once per application. The main control for the Glean SDK is on the glean.Glean singleton. The Glean SDK should be initialized as soon as possible, and importantly, before any other libraries in the application start using Glean. Library code should never call Glean.initialize, since it should be called exactly once per application. from glean import Glean Glean.initialize( application_id="my-app-id", application_version="0.1.0", upload_enabled=True, )  Additional configuration is available on the glean.Configuration object, which can be passed into Glean.initialize(). Unlike Android and Swift, the Python bindings do not automatically send any pings. See the custom pings documentation about adding custom pings and sending them. Multiple processes support The Glean SDK does not support use across multiple processes, and must only be initialized on the application's main process. The Glean SDK should be initialized as soon as possible, and importantly, before any other libraries in the application start using Glean. Library code should never call Glean.initialize, since it should be called exactly once per application. use glean::{ClientInfoMetrics, Configuration}; let cfg = Configuration { data_path, application_id: "my-app-id".into(), upload_enabled: true, max_events: None, delay_ping_lifetime_io: false, channel: None, server_endpoint: Some("https://incoming.telemetry.mozilla.org".into()), uploader: None, use_core_mps: true, }; let client_info = ClientInfoMetrics { app_build: env!("CARGO_PKG_VERSION").to_string(), app_display_version: env!("CARGO_PKG_VERSION").to_string(), }; glean::initialize(cfg, client_info);  Unlike in other implementations, the Rust language bindings do not provide a default uploader. See PingUploader for details. The main control for Glean is on the Glean singleton. The Glean.js library should be initialized as soon as possible when the product using it is started. // Glean.js for webextensions: the following code // runs in the background script. import Glean from "@mozilla/glean/webext"; Glean.initialize( "my-app-id", true, // uploadEnabled { appDisplayVersion: "0.1.0" } );  What happens after Glean is initialized? Once initialized, if upload is enabled, the Glean SDK will automatically start collecting baseline metrics. If upload is disabled, any persisted metrics, events and pings (other than first_run_date and first_run_hour) are cleared, and subsequent calls to record metrics will be no-ops. Release channels If the application has the concept of release channels and knows which channel it is on at run-time, then it can provide the Glean SDK with this information by setting it as part of the Configuration object parameter of the initialize method. Behavior when uninitialized Metric recording that happens before the Glean SDK is initialized is queued and applied at initialization. To avoid unbounded memory growth the queue is bounded (currently to a maximum of 100 tasks), and further recordings are dropped. The number of recordings dropped, if any, is recorded in the glean.error.preinit_tasks_overflow metric. Custom ping submission will not fail before initialization. Collection and upload of the custom ping is delayed until the Glean SDK is initialized. Built-in pings are only available after initialization. Toggling upload status Glean.setUploadEnabled() should be called in response to the user enabling or disabling telemetry. Do not call setUploadEnabled before initializing If called before Glean.initialize() the call to Glean.setUploadEnabled() will be ignored. Set the initial state using uploadEnabled on Glean.initialize(). Glean.shared.setUploadEnabled() should be called in response to the user enabling or disabling telemetry. Do not call setUploadEnabled before initializing If called before Glean.shared.initialize() the call to Glean.shared.setUploadEnabled() will be ignored. Set the initial state using uploadEnabled on Glean.shared.initialize(). Glean.set_upload_enabled() should be called in response to the user enabling or disabling telemetry. Do not call set_upload_enabled before initializing If called before Glean.initialize() the call to Glean.set_upload_enabled() will be ignored. Set the initial state using upload_enabled on Glean.initialize(). Glean.setUploadEnabled() should be called in response to the user enabling or disabling telemetry. Do not call setUploadEnabled before initializing If called before Glean.initialize() the call to Glean.setUploadEnabled() will be ignored. Set the initial state using uploadEnabled on Glean.initialize(). The application should provide some form of user interface to call this method. When going from enabled to disabled, all pending events, metrics and pings are cleared, except for first_run_date and first_run_hour. When re-enabling, core Glean metrics will be recomputed at that time. Using the experiments API The Glean SDK supports tagging all its pings with experiments annotations. The annotations are useful to report that experiments were active at the time the measurement were collected. The annotations are reported in the optional experiments entry in the ping_info section of all the Glean SDK pings. Experiment annotations are not persisted The experiment annotations set through this API are not persisted by the Glean SDK. The application or consuming library is responsible for setting the relevant experiment annotations at each run. It's not required to define experiment IDs and branches Experiment IDs and branches don't need to be pre-defined in the Glean SDK registry files. Please also note that the extra map is a non-nested arbitrary String to String map. It also has limits on the size of the keys and values defined below. Recording API setExperimentActive Annotates Glean pings with experiment data. // Annotate Glean pings with experiments data. Glean.setExperimentActive( experimentId = "blue-button-effective", branch = "branch-with-blue-button", extra: mapOf( "buttonLabel" to "test" ) )  // Annotate Glean pings with experiments data. Glean.shared.setExperimentActive( experimentId: "blue-button-effective", branch: "branch-with-blue-button", extra: ["buttonLabel": "test"] )  from glean import Glean Glean.set_experiment_active( experiment_id="blue-button-effective", branch="branch-with-blue-button", extra={ "buttonLabel": "test" } )  let mut extra = HashMap::new(); extra.insert("buttonLabel".to_string(), "test".to_string()); glean::set_experiment_active( "blue-button-effective".to_string(), "branch-with-blue-button".to_string(), Some(extra), );  C++ At present there is no dedicated C++ Experiments API for Firefox Desktop If you require one, please file a bug. JavaScript let FOG = Cc["@mozilla.org/toolkit/glean;1"].createInstance(Ci.nsIFOG); FOG.setExperimentActive( "blue-button-effective", "branch-with-blue-button", {"buttonLabel": "test"} );  Limits • experimentId, branch, and the keys and values of the extra field are fixed at a maximum length of 100 bytes. Longer strings are truncated. (Specifically, length is measured in the number of bytes when the string is encoded in UTF-8.) • extra map is limited to 20 entries. If passed a map which contains more elements than this, it is truncated to 20 elements. WARNING Which items are truncated is nondeterministic due to the unordered nature of maps. What's left may not necessarily be the first elements added. Recorded errors • invalid_value: If the values of experimentId or branch are truncated for length, if the keys or values in the extra map are truncated for length, or if the extra map is truncated for the number of elements. setExperimentInactive Removes the experiment annotation. Should be called when the experiment ends. Glean.setExperimentInactive("blue-button-effective")  Glean.shared.setExperimentInactive(experimentId: "blue-button-effective")  from glean import Glean Glean.set_experiment_inactive("blue-button-effective")  glean::set_experiment_inactive("blue-button-effective".to_string());  C++ At present there is no dedicated C++ Experiments API for Firefox Desktop If you require one, please file a bug. JavaScript let FOG = Cc["@mozilla.org/toolkit/glean;1"].createInstance(Ci.nsIFOG); FOG.setExperimentInactive("blue-button-effective");  Testing API testIsExperimentActive Reveals if the experiment is annotated in Glean pings. assertTrue(Glean.testIsExperimentActive("blue-button-effective"))  XCTAssertTrue(Glean.shared.testIsExperimentActive(experimentId: "blue-button-effective"))  from glean import Glean assert Glean.test_is_experiment_active("blue-button-effective")  assert!(glean::test_is_experiment_active("blue-button-effective".to_string());  testGetExperimentData Returns the recorded experiment data including branch and extras. assertEquals( "branch-with-blue-button", Glean.testGetExperimentData("blue-button-effective")?.branch )  XCTAssertEqual( "branch-with-blue-button", Glean.testGetExperimentData(experimentId: "blue-button-effective")?.branch )  from glean import Glean assert ( "branch-with-blue-button" == Glean.test_get_experiment_data("blue-button-effective").branch )  assert_eq!( "branch-with-blue-button", glean::test_get_experiment_data("blue-button-effective".to_string()).branch, );  C++ At present there is no dedicated C++ Experiments API for Firefox Desktop If you require one, please file a bug. JavaScript let FOG = Cc["@mozilla.org/toolkit/glean;1"].createInstance(Ci.nsIFOG); Assert.equals( "branch-with-blue-button", FOG.testGetExperimentData("blue-button-effective").branch );  Reference Registering custom pings After defining custom pings The Glean SDK build generates code from pings.yaml in a Pings object, which must be instantiated so Glean can send pings by name. API registerPings Loads custom ping metadata into your application or library In Kotlin, this object must be registered with the Glean SDK from your startup code before calling Glean.initialize (such as in your application's onCreate method or a function called from that method). import org.mozilla.yourApplication.GleanMetrics.Pings override fun onCreate() { Glean.registerPings(Pings) Glean.initialize(applicationContext, uploadEnabled = true) }  In Swift, this object must be registered with the Glean SDK from your startup code before calling Glean.shared.initialize (such as in your application's UIApplicationDelegate application(_:didFinishLaunchingWithOptions:) method or a function called from that method). import Glean @UIApplicationMain class AppDelegate: UIResponder, UIApplicationDelegate { func application(_: UIApplication, didFinishLaunchingWithOptions _: [UIApplication.LaunchOptionsKey: Any]?) -> Bool { Glean.shared.registerPings(GleanMetrics.Pings) Glean.shared.initialize(uploadEnabled = true) } }  For Python, the pings.yaml file must be available and loaded at runtime. While the Python language bindings do provide a Glean.register_ping_type function, if your project is a script (i.e. just Python files in a directory), you can load the pings.yaml before calling Glean.initialize using: from glean import load_pings pings = load_pings("pings.yaml") Glean.initialize( application_id="my-app-id", application_version="0.1.0", upload_enabled=True, )  If your project is a distributable Python package, you need to include the pings.yaml file using one of the myriad ways to include data in a Python package and then use pkg_resources.resource_filename() to get the filename at runtime. from glean import load_pings from pkg_resources import resource_filename pings = load_pings(resource_filename(__name__, "pings.yaml"))  In Rust custom pings need to be registered individually. This should be done before calling glean::initialize. use your_glean_metrics::pings; glean::register_ping_type(&pings::custom_ping); glean::register_ping_type(&pings::search); glean::initialize(cfg, client_info);  Shut down Provides a way for users to gracefully shut down Glean, by blocking until it is finished performing pending tasks such as recording metrics and uploading pings. How the Glean SDKs execute tasks Most calls to Glean APIs are dispatched1. This strategy is adopted because most tasks performed by the Glean SDKs involve file system read or write operations, HTTP requests and other time consuming actions. Each Glean SDK has an internal structure called "Dispatcher" which makes sure API calls get executed in the order they were called, while not requiring the caller to block on the completion of each of these tasks. 1 Here, this term indicates the tasks are run asynchronously in JavaScript or in a different thread for all other SDKs. API shutdown fn main() { let cfg = Configuration { // ... }; let client_info = /* ... */; glean::initialize(cfg, client_info); // Ensure the dispatcher thread winds down glean::shutdown(); }  import Glean from "@mozilla/glean/webext"; async function onUninstall() { // Flips Glean upload status to false, // which triggers sending of a deletion-request ping. Glean.setUploadEnabled(false); // Block on shut down to guarantee all pending pings // (including the deletion-request sent above) // are sent before the extension is uninstalled. await Glean.shutdown(); // Uninstall browser extension without asking for user approval before doing so. await browser.management.uninstallSelf({ showConfirmDialog: false }); }  The shutdown API is available for all JavaScript targets, even though the above example is explicitly using the webext target. Reference Debugging Different platforms have different ways to enable each debug functionality. They may be enabled • through APIs exposed on the Glean singleton, • through environment variables set at run time, • or through platform specific debug tools. Platform Specific Information Check out the platform specific guides on how to use Glean's debug functionalities. Features The Glean SDK provides four debugging features. Log Pings This is either true or false and will cause all subsequent pings that are submitted, to also be echoed to the device's log. Debug View Tag This will tag all subsequent outgoing pings with the provided value, in order to identify them in the Glean Debug View. Source Tags This will tag all subsequent outgoing pings with a maximum of 5 comma-separated tags. Send Pings This feature is only available through the Android and iOS debug tools. This expects the name of a ping and forces its immediate submission. Log pings This flag causes all subsequent pings that are submitted to also be echoed to the product's log. Once enabled, the only way to disable this feature is to restart or manually reset the application. On how to access logs The Glean SDK logs warnings and errors through platform-specific logging frameworks. See the platform-specific instructions for information on how to view the logs on the platform you are on. Limits • The accepted values are true or false. Any other value will be ignored. API setLogPings Enables or disables ping logging. This API can safely be called before Glean.initialize. The tag will be applied upon initialization in this case. import Glean Glean.shared.setLogPings(true)  use glean; glean.set_log_pings(true);  import Glean from "@mozilla/glean/<platform>"; Glean.setLogPings(true);  Environment variable GLEAN_LOG_PINGS It is also possible to enable ping logging through the GLEAN_LOG_PINGS environment variable. This variable must be set at runtime, not at compile time. It will be checked upon Glean initialization. To set environment variables to the process running your app in an iOS device or emulator you need to edit the scheme for your app. In the Xcode IDE, use the shortcut Cmd + < to open the scheme editor popup. The environment variables editor is under the Arguments tab on this popup. $ GLEAN_LOG_PINGS=true python my_application.py

$GLEAN_LOG_PINGS=true cargo run  $ GLEAN_LOG_PINGS=true ./mach run


Debug View Tag

Tag all subsequent outgoing pings with a given value, in order to redirect them to the Glean Debug View.

"To tag" a ping with the Debug View Tag means that the ping request will contain the X-Debug-Id header with the given tag.

Once enabled, the only way to disable this feature is to restart or manually reset the application.

Limits

• Any valid HTTP header value is a valid debug view tag (e.g. any value that matches the regex [a-zA-Z0-9-]{1,20}). Invalid values will be ignored.

API

setDebugViewTag

Sets the Debug View Tag to a given value.

This API can safely be called before Glean.initialize. The tag will be applied upon initialization in this case.

import Glean

Glean.shared.setDebugViewTag("my-tag")

use glean;

glean.set_debug_view_tag("my-tag");

import Glean from "@mozilla/glean/<platform>";

Glean.setDebugViewTag("my-tag");


Environment variable

GLEAN_DEBUG_VIEW_TAG

It is also possible to set the debug view tag through the GLEAN_DEBUG_VIEW_TAG environment variable.

This variable must be set at runtime, not at compile time. It will be checked upon Glean initialization.

To set environment variables to the process running your app in an iOS device or emulator you need to edit the scheme for your app. In the Xcode IDE, use the shortcut Cmd + < to open the scheme editor popup. The environment variables editor is under the Arguments tab on this popup.

$GLEAN_DEBUG_VIEW_TAG="my-tag" python my_application.py  $ GLEAN_DEBUG_VIEW_TAG="my-tag" cargo run

$GLEAN_DEBUG_VIEW_TAG="my-tag" ./mach run  Source Tags Tag all subsequent outgoing pings with a maximum of 5 comma-separated tags. "To tag" a ping with Source Tags means that the ping request will contain the X-Source-Tags header with a comma separated list of the given tags. Once enabled, the only way to disable this feature is to restart or manually reset the application. Limits • Any valid HTTP header value is a valid source tag (e.g. any value that matches the regex [a-zA-Z0-9-]{1,20}) and values starting with the substring glean are reserved for internal Glean usage and thus are also considered invalid. If any value in the list of source tags is invalid, the whole list will be ignored. • If the list of tags has more than five members, the whole list will be ignored. • The special value automation is meant for tagging pings generated on automation: such pings will be specially handled on the pipeline (i.e. discarded from non-live views). API setSourceTags Sets the Source Tags to a given value. This API can safely be called before Glean.initialize. The tag will be applied upon initialization in this case. import Glean Glean.shared.setSourceTags(["my-tag", "your-tag", "our-tag"])  use glean; glean.set_source_tags(["my-tag", "your-tag", "our-tag"]);  import Glean from "@mozilla/glean/<platform>"; Glean.setSourceTags(["my-tag", "your-tag", "our-tag"]);  Environment variable GLEAN_SOURCE_TAGS It is also possible to set the debug view tag through the GLEAN_SOURCE_TAGS environment variable. This variable must be set at runtime, not at compile time. It will be checked upon Glean initialization. To set environment variables to the process running your app in an iOS device or emulator you need to edit the scheme for your app. In the Xcode IDE, use the shortcut Cmd + < to open the scheme editor popup. The environment variables editor is under the Arguments tab on this popup. $ GLEAN_SOURCE_TAGS=my-tag,your-tag,our-tag python my_application.py

$GLEAN_SOURCE_TAGS=my-tag,your-tag,our-tag cargo run  $ GLEAN_SOURCE_TAGS=my-tag,your-tag,our-tag ./mach run


Metrics

Not sure which metric type to use? These docs contain a series of questions that can help. Reference information about each metric type is linked below.

The parameters available that apply to any metric type are in the metric parameters page.

There are different metrics to choose from, depending on what you want to achieve:

• Boolean: Records a single truth value, for example "is a11y enabled?"

• Labeled boolean: Records truth values for a set of labels, for example "which a11y features are enabled?"

• Counter: Used to count how often something happens, for example, how often a certain button was pressed.

• Labeled counter: Used to count how often something happens, for example which kind of crash occurred ("uncaught_exception" or "native_code_crash").

• String: Records a single Unicode string value, for example the name of the OS.

• Labeled strings: Records multiple Unicode string values, for example to record which kind of error occurred in different stages of a login process.

• String List: Records a list of Unicode string values, for example the list of enabled search engines.

• Timespan: Used to measure how much time is spent in a single task.

• Timing Distribution: Used to record the distribution of multiple time measurements.

• Memory Distribution: Used to record the distribution of memory sizes.

• UUID: Used to record universally unique identifiers (UUIDs), such as a client ID.

• URL: Used to record URL-like strings.

• Datetime: Used to record an absolute date and time, such as the time the user first ran the application.

• Events: Records events e.g. individual occurrences of user actions, say every time a view was open and from where.

• Custom Distribution: Used to record the distribution of a value that needs fine-grained control of how the histogram buckets are computed. Custom distributions are only available for values that come from Gecko.

• Quantity: Used to record a single non-negative integer value. For example, the width of the display in pixels.

• Rate: Used to record the rate something happens relative to some other thing. For example, the number of HTTP connections that experienced an error relative to the number of total HTTP connections made.

• Text: Records a single long Unicode text, used when the limits on String are too low.

Labeled metrics

There are two types of metrics listed above - labeled and unlabeled metrics. If a metric is labeled, it means that for a single metric entry you define in metrics.yaml, you can record into multiple metrics under the same name, each of the same type and identified by a different string label.

This is useful when you need to break down metrics by a label known at build time or run time. For example:

• When you want to count a different set of sub-views that users interact with, you could use viewCount["view1"].add() and viewCount["view2"].add().

• When you want to count errors that might occur for a feature, you could use errorCount[errorName].add().

Labeled metrics come in two forms:

• Static labels: The labels are specified at build time in the metrics.yaml file, in the labels parameter. If a label that isn't part of this set is used at run time, it is converted to the special label __other__. The number of static labels is limited to 100 per metric.

• Dynamic labels: The labels aren't known at build time, so are set at run time. Only the first 16 labels seen by the Glean SDK will be tracked. After that, any additional labels are converted to the special label __other__.

Note: Be careful with using arbitrary strings as labels and make sure they can't accidentally contain identifying data (like directory paths or user input).

Label format

To ensure maximum support in database columns, labels must be made up of dot-separated identifiers with lowercase ASCII alphanumerics, containing underscores and dashes.

Specifically, they must conform to this regular expression:

^[a-z_][a-z0-9_-]{0,29}(\\.[a-z_][a-z0-9_-]{0,29})*$ Adding or changing metric types Glean has a well-defined process for requesting changes to existing metric types or suggesting the implementation of new metric types: 1. Glean consumers need to file a bug in the Data platforms & tools::Glean Metric Types component, filling in the provided form; 2. The triage owner of the Bugzilla component prioritizes this within 6 business days and kicks off the decision making process. 3. Once the decision process is completed, the bug is closed with a comment outlining the decision that was made. Boolean Boolean metrics are used for reporting simple flags. Recording API set Sets a boolean metric to a specific value. import org.mozilla.yourApplication.GleanMetrics.Flags Flags.a11yEnabled.set(System.isAccesibilityEnabled())  import org.mozilla.yourApplication.GleanMetrics.Flags; Flags.INSTANCE.a11yEnabled.set(System.isAccessibilityEnabled());  Flags.a11yEnabled.set(self.isAccessibilityEnabled)  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.flags.a11y_enabled.set(is_accessibility_enabled())  use glean_metrics; flags::a11y_enabled.set(system.is_accessibility_enabled());  import * as flags from "./path/to/generated/files/flags.js"; flags.a11yEnabled.set(this.isAccessibilityEnabled());  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::flags::a11y_enabled.Set(false);  JavaScript Glean.flags.a11yEnabled.set(false);  Recorded errors N/A Testing API testGetValue Gets the recorded value for a given boolean metric. import org.mozilla.yourApplication.GleanMetrics.Flags assertTrue(Flags.a11yEnabled.testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Flags; assertTrue(Flags.INSTANCE.a11yEnabled.testGetValue());  @testable import Glean XCTAssertTrue(try Flags.a11yEnabled.testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert True is metrics.flags.a11y_enabled.test_get_value()  use glean_metrics; assert!(flags::a11y_enabled.test_get_value(None).unwrap());  import * as flags from "./path/to/generated/files/flags.js"; assert(await flags.a11yEnabled.testGetValue());  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_EQ(false, mozilla::glean::flags::a11y_enabled.TestGetValue().value());  JavaScript Assert.equal(false, Glean.flags.a11yEnabled.testGetValue());  testHasValue Whether or not any value was recorded for a given boolean metric. import org.mozilla.yourApplication.GleanMetrics.Flags assertTrue(Flags.a11yEnabled.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Flags; assertTrue(Flags.INSTANCE.a11yEnabled.testHasValue());  @testable import Glean XCTAssertTrue(try Flags.a11yEnabled.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert True is metrics.flags.a11y_enabled.test_has_value()  Metric parameters Example boolean metric definition: flags: a11y_enabled: type: boolean description: > Records whether a11y is enabled on the device. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters N/A Data questions • Is accessibility enabled? Reference Labeled Booleans Labeled booleans are used to record different related boolean flags. Recording API set Sets one of the labels in a labeled boolean metric to a specific value. import org.mozilla.yourApplication.GleanMetrics.Accessibility Accessibility.features["screen_reader"].set(isScreenReaderEnabled()) Accessibility.features["high_contrast"].set(isHighContrastEnabled())  import org.mozilla.yourApplication.GleanMetrics.Accessibility; Acessibility.INSTANCE.features["screen_reader"].set(isScreenReaderEnabled()); Acessibility.INSTANCE.features["high_contrast"].set(isHighContrastEnabled());  Accessibility.features["screen_reader"].set(isScreenReaderEnabled()) Accessibility.features["high_contrast"].set(isHighContrastEnabled())  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.accessibility.features["screen_reader"].set(is_screen_reader_enabled()) metrics.accessibility.features["high_contrast"].set(is_high_contrast_enabled())   #![allow(unused)] fn main() { use glean_metrics; accessibility::features.get("screen_reader").set(is_screen_reader_enabled()); accessibility::features.get("high_contrast").set(is_high_contrast_enabled()); }  import * as acessibility from "./path/to/generated/files/acessibility.js"; acessibility.features["screen_reader"].set(this.isScreenReaderEnabled()); acessibility.features["high_contrast"].set(this.isHighContrastEnabled());  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::accessibility::features.Get("screen_reader"_ns).Set(true); mozilla::glean::accessibility::features.Get("high_contrast"_ns).Set(false);  JavaScript Glean.accessibility.features.screen_reader.set(true); Glean.accessibility.features["high_contrast"].set(false);  Recorded Errors • invalid_label: • If the label contains invalid characters. Data is still recorded to the special label __other__. • If the label exceeds the maximum number of allowed characters. Data is still recorded to the special label __other__. Testing API testGetValue Gets the recorded value for a given label in a labeled boolean metric. import org.mozilla.yourApplication.GleanMetrics.Accessibility // Do the booleans have the expected values? assertEquals(True, Accessibility.features["screen_reader"].testGetValue()) assertEquals(False, Accessibility.features["high_contrast"].testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Accessibility; // Do the booleans have the expected values? assertEquals(True, Acessibility.INSTANCE.features["screen_reader"].testGetValue()); assertEquals(False, Acessibility.INSTANCE.features["high_contrast"].testGetValue());  @testable import Glean // Do the booleans have the expected values? XCTAssertEqual(true, try Accessibility.features["screen_reader"].testGetValue()) XCTAssertEqual(false, try Accessibility.features["high_contrast"].testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Do the booleans have the expected values? assert metrics.accessibility.features["screen_reader"].test_get_value() assert not metrics.accessibility.features["high_contrast"].test_get_value()   #![allow(unused)] fn main() { use glean_metrics; // Do the booleans have the expected values? assert!(accessibility::features.get("screen_reader").test_get_value(None).unwrap()); assert!(!accessibility::features.get("high_contrast").test_get_value(None).unwrap()); }  import * as accessibility from "./path/to/generated/files/acessibility.js"; assert(await accessibility.features["screen_reader"].testGetValue()); assert(!(await accessibility.features["high_contrast"].testGetValue()));  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_EQ( true, mozilla::glean::accessibility::features.Get("screen_reader"_ns).TestGetValue().unwrap().ref()); ASSERT_EQ( false, mozilla::glean::accessibility::features.Get("high_contrast"_ns).TestGetValue().unwrap().ref());  JavaScript Assert.equal(true, Glean.accessibility.features["screen_reader"].testGetValue()); Assert.equal(false, Glean.accessibility.features.high_contrast.testGetValue());  testHasValue Whether or not any value was recorded for a given label in a labeled boolean metric. import org.mozilla.yourApplication.GleanMetrics.Accessibility // Was anything recorded? assertTrue(Accessibility.features["screen_reader"].testHasValue()) assertTrue(Accessibility.features["high_contrast"].testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Accessibility; // Was anything recorded? assertEquals(True, Acessibility.INSTANCE.features["screen_reader"].testHasValue()); assertEquals(True, Acessibility.INSTANCE.features["high_contrast"].testHasValue());  @testable import Glean // Was anything recorded? XCTAssert(Accessibility.features["screen_reader"].testHasValue()) XCTAssert(Accessibility.features["high_contrast"].testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Was anything recorded? assert metrics.accessibility.features["screen_reader"].test_has_value() assert metrics.accessibility.features["high_contrast"].test_has_value()  testGetNumRecordedErrors Gets the number of errors recorded for a given labeled boolean metric in total. import org.mozilla.yourApplication.GleanMetrics.Accessibility // Did we record any invalid labels? assertEquals(0, Accessibility.features.testGetNumRecordedErrors(ErrorType.InvalidLabel))  import org.mozilla.yourApplication.GleanMetrics.Accessibility; // Did we record any invalid labels? assertEquals(0, Acessibility.INSTANCE.features.testGetNumRecordedErrors());  @testable import Glean // Were there any invalid labels? XCTAssertEqual(0, Accessibility.features.testGetNumRecordedErrors(.invalidLabel))  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Did we record any invalid labels? assert 0 == metrics.accessibility.features.test_get_num_recorded_errors( ErrorType.INVALID_LABEL )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; // Did we record any invalid labels? assert_eq!( 1, accessibility::features.test_get_num_recorded_errors( ErrorType::InvalidLabel ) ); }  import * as accessibility from "./path/to/generated/files/acessibility.js"; import { ErrorType } from "@mozilla/glean/<platform>"; assert( 1, await accessibility.features.testGetNumRecordedErrors(ErrorType.InvalidLabel) );  Metric parameters Example labeled boolean metric definition: accessibility: features: type: labeled_boolean description: > a11y features enabled on the device. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01 labels: - screen_reader - high_contrast ...  Extra metric parameters labels Labeled metrics may have an optional labels parameter, containing a list of known labels. The labels in this list must match the following requirements: Important If the labels are specified in the metrics.yaml, using any label not listed in that file will be replaced with the special value __other__. If the labels are not specified in the metrics.yaml, only 16 different dynamic labels may be used, after which the special value __other__ will be used. Removing or changing labels, including their order in the registry file, is permitted. Avoid reusing labels that were removed in the past. It is best practice to add documentation about removed labels to the description field so that analysts will know of their existence and meaning in historical data. Special care must be taken when changing GeckoView metrics sent through the Glean SDK, as the index of the labels is used to report Gecko data through the Glean SDK. Data questions • Which accessibility features are enabled? Reference Counter Used to count how often something happens, say how often a certain button was pressed. A counter always starts from 0. Each time you record to a counter, its value is incremented. Unless incremented by a positive value, a counter will not be reported in pings, that means: the value 0 is never sent in a ping. If you find that you need to control the actual value sent in the ping, you may be measuring something, not just counting something, and a Quantity metric may be a better choice. Let the Glean metric do the counting When using a counter metric, it is important to let the Glean metric do the counting. Using your own variable for counting and setting the counter yourself could be problematic because it will be difficult to reset the value at the exact moment that the value is sent in a ping. Instead, just use counter.add to increment the value and let Glean handle resetting the counter. Recording API add Increases the counter by a certain amount. If no amount is passed it defaults to 1. import org.mozilla.yourApplication.GleanMetrics.Controls Controls.refreshPressed.add() // Adds 1 to the counter. Controls.refreshPressed.add(5) // Adds 5 to the counter.  import org.mozilla.yourApplication.GleanMetrics.Controls; Controls.INSTANCE.refreshPressed.add(); // Adds 1 to the counter. Controls.INSTANCE.refreshPressed.add(5); // Adds 5 to the counter.  Controls.refreshPressed.add() // Adds 1 to the counter. Controls.refreshPressed.add(5) // Adds 5 to the counter.  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.controls.refresh_pressed.add() # Adds 1 to the counter. metrics.controls.refresh_pressed.add(5) # Adds 5 to the counter.  use glean_metrics; controls::refresh_pressed.add(1); // Adds 1 to the counter. controls::refresh_pressed.add(5); // Adds 5 to the counter.  import * as controls from "./path/to/generated/files/controls.js"; controls.refreshPressed.add(); // Adds 1 to the counter. controls.refreshPressed.add(5); // Adds 5 to the counter.  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::controls::refresh_pressed.Add(1); mozilla::glean::controls::refresh_pressed.Add(5);  JavaScript Glean.controls.refreshPressed.add(1); Glean.controls.refreshPressed.add(5);  Recorded errors Limits • Only increments; • Saturates at the largest value that can be represented as a 32-bit signed integer (2147483647). Testing API testGetValue Gets the recorded value for a given counter metric. import org.mozilla.yourApplication.GleanMetrics.Controls assertEquals(6, Controls.refreshPressed.testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Controls; assertEquals(6, Controls.INSTANCE.refreshPressed.testGetValue());  @testable import Glean XCTAssertEqual(6, try Controls.refreshPressed.testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert 6 == metrics.controls.refresh_pressed.test_get_value()   #![allow(unused)] fn main() { use glean_metrics; assert_eq!(6, controls::refresh_pressed.test_get_value(None).unwrap()); }  import * as controls from "./path/to/generated/files/controls.js"; assert.strictEqual(6, await controls.refreshPressed.testGetValue());  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_TRUE(mozilla::glean::controls::refresh_pressed.TestGetValue().isOk()); ASSERT_EQ(6, mozilla::glean::controls::refresh_pressed.TestGetValue().unwrap().value());  JavaScript // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. Assert.equal(6, Glean.controls.refreshPressed.testGetValue());  testHasValue Whether or not any value was recorded for a given counter metric. import org.mozilla.yourApplication.GleanMetrics.Controls assertTrue(Controls.refreshPressed.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Controls; assertTrue(Controls.INSTANCE.refreshPressed.testHasValue());  @testable import Glean XCTAssert(Controls.refreshPressed.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert metrics.controls.refresh_pressed.test_has_value()  testGetNumRecordedErrors Gets number of errors recorded for a given counter metric. import org.mozilla.yourApplication.GleanMetrics.Controls assertEquals( 1, Controls.refreshPressed.testGetNumRecordedErrors(ErrorType.InvalidValue) )  import org.mozilla.yourApplication.GleanMetrics.Controls; assertEquals( 1, Controls.INSTANCE.refreshPressed.testGetNumRecordedErrors(ErrorType.InvalidValue) );  XCTAssertEqual(1, Controls.refreshPressed.testGetNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") from glean.testing import ErrorType assert 1 == metrics.controls.refresh_pressed.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; assert_eq!( 1, controls::refresh_pressed.test_get_num_recorded_errors( ErrorType::InvalidValue ) ); }  import * as controls from "./path/to/generated/files/controls.js"; import { ErrorType } from "@mozilla/glean/<platform>"; assert.strictEqual( 1, await controls.refreshPressed.testGetNumRecordedErrors(ErrorType.InvalidValue) );  Metric parameters Example counter metric definition: controls: refresh_pressed: type: counter description: > Counts how often the refresh button is pressed. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters N/A Data questions • How often was a certain button pressed? Reference Labeled Counters Labeled counters are used to record different related counts that should sum up to a total. Each counter always starts from 0. Each time you record to a labeled counter, its value is incremented. Unless incremented by a positive value, a counter will not be reported in pings, that means: the value 0 is never sent in a ping. Recording API add Increases one of the labels in a labeled counter metric by a certain amount. If no amount is passed it defaults to 1. import org.mozilla.yourApplication.GleanMetrics.Stability Stability.crashCount["uncaught_exception"].add() // Adds 1 to the "uncaught_exception" counter. Stability.crashCount["native_code_crash"].add(3) // Adds 3 to the "native_code_crash" counter.  import org.mozilla.yourApplication.GleanMetrics.Stability; Stability.INSTANCE.crashCount["uncaught_exception"].add(); // Adds 1 to the "uncaught_exception" counter. Stability.INSTANCE.crashCount["native_code_crash"].add(3); // Adds 3 to the "native_code_crash" counter.  Stability.crashCount["uncaught_exception"].add() // Adds 1 to the "uncaught_exception" counter. Stability.crashCount["native_code_crash"].add(3) // Adds 3 to the "native_code_crash" counter.  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Adds 1 to the "uncaught_exception" counter. metrics.stability.crash_count["uncaught_exception"].add() # Adds 3 to the "native_code_crash" counter. metrics.stability.crash_count["native_code_crash"].add(3)   #![allow(unused)] fn main() { use glean_metrics; stability::crash_count.get("uncaught_exception").add(1); // Adds 1 to the "uncaught_exception" counter. stability::crash_count.get("native_code_crash").add(3); // Adds 3 to the "native_code_crash" counter. }  import * as stability from "./path/to/generated/files/stability.js"; // Adds 1 to the "uncaught_exception" counter. stability.crashCount["uncaught_exception"].add(); // Adds 3 to the "native_code_crash" counter. stability.crashCount["native_code_crash"].add(3);  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::stability::crash_count.Get("uncaught_exception"_ns).Add(1); mozilla::glean::stability::crash_count.Get("native_code_crash"_ns).Add(3);  JavaScript Glean.stability.crashCount.uncaught_exception.add(1); Glean.stability.crashCount["native_code_crash"].add(3);  Errors recorded • invalid_value: If the counter is incremented by 0 or a negative value. • invalid_label: • If the label contains invalid characters. Data is still recorded to the special label __other__. • If the label exceeds the maximum number of allowed characters. Data is still recorded to the special label __other__. Limits • Only increments; • Saturates at the largest value that can be represented as a 32-bit signed integer. Testing API testGetValue Gets the recorded value for a given label in a labeled counter metric. import org.mozilla.yourApplication.GleanMetrics.Stability // Do the counters have the expected values? assertEquals(1, Stability.crashCount["uncaught_exception"].testGetValue()) assertEquals(3, Stability.crashCount["native_code_crash"].testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Stability; // Do the counters have the expected values? assertEquals(1, Stability.INSTANCE.crashCount["uncaught_exception"].testGetValue()); assertEquals(3, Stability.INSTANCE.crashCount["native_code_crash"].testGetValue());  @testable import Glean // Do the counters have the expected values? XCTAssertEqual(1, try Stability.crashCount["uncaught_exception"].testGetValue()) XCTAssertEqual(3, try Stability.crashCount["native_code_crash"].testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Do the counters have the expected values? assert 1 == metrics.stability.crash_count["uncaught_exception"].test_get_value() assert 3 == metrics.stability.crash_count["native_code_crash"].test_get_value()   #![allow(unused)] fn main() { use glean_metrics; // Do the counters have the expected values? assert_eq!(1, stability::crash_count.get("uncaught_exception").test_get_value().unwrap()); assert_eq!(3, stability::crash_count.get("native_code_crash").test_get_value().unwrap()); }  import * as stability from "./path/to/generated/files/stability.js"; // Do the counters have the expected values? assert.strictEqual(1, await stability.crashCount["uncaught_exception"].testGetValue()); assert.strictEqual(3, await stability.crashCount["native_code_crash"].testGetValue());  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_EQ( 1, mozilla::glean::stability::crash_count.Get("uncaught_exception"_ns).TestGetValue().unwrap().ref()); ASSERT_EQ( 3, mozilla::glean::stability::crash_count.Get("native_code_crash"_ns).TestGetValue().unwrap().ref());  JavaScript Assert.equal(1, Glean.stability.crashCount["uncaught_exception"].testGetValue()); Assert.equal(3, Glean.stability.crashCount.native_code_crash.testGetValue());  testHasValue Whether or not any value was recorded for a given label in a labeled counter metric. import org.mozilla.yourApplication.GleanMetrics.Stability // Was anything recorded? assertTrue(Stability.crashCount["uncaught_exception"].testHasValue()) assertTrue(Stability.crashCount["native_code_crash"].testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Stability; // Was anything recorded? assertTrue(Stability.INSTANCE.crashCount["uncaught_exception"].testHasValue()); assertTrue(Stability.INSTANCE.crashCount["native_code_crash"].testHasValue());  @testable import Glean // Was anything recorded? XCTAssert(Stability.crashCount["uncaught_exception"].testHasValue()) XCTAssert(Stability.crashCount["native_code_crash"].testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Was anything recorded? assert metrics.stability.crash_count["uncaught_exception"].test_has_value() assert metrics.stability.crash_count["native_code_crash"].test_has_value()  testGetNumRecordedErrors Gets the number of errors recorded for a given labeled counter metric in total. import org.mozilla.yourApplication.GleanMetrics.Stabilit // Were there any invalid labels? assertEquals(0, Stability.crashCount.testGetNumRecordedErrors(ErrorType.InvalidLabel))  import org.mozilla.yourApplication.GleanMetrics.Stability; // Were there any invalid labels? assertEquals(0, Stability.INSTANCE.crashCount.testGetNumRecordedErrors(ErrorType.InvalidLabel));  @testable import Glean // Were there any invalid labels? XCTAssertEqual(0, Stability.crashCount.testGetNumRecordedErrors(.invalidLabel))  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Were there any invalid labels? assert 0 == metrics.stability.crash_count.test_get_num_recorded_errors( ErrorType.INVALID_LABEL )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; // Were there any invalid labels? assert_eq!( 0, stability::crash_count.test_get_num_recorded_errors( ErrorType::InvalidLabel ) ); }  import * as stability from "./path/to/generated/files/stability.js"; import { ErrorType } from "@mozilla/glean/<platform>"; // Were there any invalid labels? assert( 0, await stability.crashCount.testGetNumRecordedErrors(ErrorType.InvalidLabel) );  Metric parameters Example labeled counter metric definition: accessibility: features: type: labeled_counter description: > Counts the number of crashes that occur in the application. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01 labels: - uncaught_exception - native_code_crash ...  Extra metric parameters labels Labeled metrics may have an optional labels parameter, containing a list of known labels. The labels in this list must match the following requirements: Important If the labels are specified in the metrics.yaml, using any label not listed in that file will be replaced with the special value __other__. If the labels are not specified in the metrics.yaml, only 16 different dynamic labels may be used, after which the special value __other__ will be used. Removing or changing labels, including their order in the registry file, is permitted. Avoid reusing labels that were removed in the past. It is best practice to add documentation about removed labels to the description field so that analysts will know of their existence and meaning in historical data. Special care must be taken when changing GeckoView metrics sent through the Glean SDK, as the index of the labels is used to report Gecko data through the Glean SDK. Data questions • How many times did different types of crashes occur? Reference Strings String metrics allow recording a Unicode string value with arbitrary content. This metric type does not support recording JSON blobs - please get in contact with the Glean SDK team if you're missing a type. Important Be careful using arbitrary strings and make sure they can't accidentally contain identifying data (like directory paths or user input). Recording API set Set a string metric to a specific value. import org.mozilla.yourApplication.GleanMetrics.SearchDefault // Record a value into the metric. SearchDefault.name.set("duck duck go") // If it changed later, you can record the new value: SearchDefault.name.set("wikipedia")  import org.mozilla.yourApplication.GleanMetrics.SearchDefault; // Record a value into the metric. SearchDefault.INSTANCE.name.set("duck duck go"); // If it changed later, you can record the new value: SearchDefault.INSTANCE.name.set("wikipedia");  // Record a value into the metric. SearchDefault.name.set("duck duck go") // If it changed later, you can record the new value: SearchDefault.name.set("wikipedia")  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Record a value into the metric. metrics.search_default.name.set("duck duck go") # If it changed later, you can record the new value: metrics.search_default.name.set("wikipedia")   #![allow(unused)] fn main() { use glean_metrics; // Record a value into the metric. search_default::name.set("duck duck go"); // If it changed later, you can record the new value: search_default::name.set("wikipedia"); }  import * as searchDefault from "./path/to/generated/files/searchDefault.js"; // Record a value into the metric. searchDefault.name.set("duck duck go"); // If it changed later, you can record the new value: searchDefault.name.set("wikipedia");  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::search_default::name.Set("wikipedia"_ns);  JavaScript Glean.searchDefault.name.set("wikipedia");  Limits • Fixed maximum string length: 100. Longer strings are truncated. This is measured in the number of bytes when the string is encoded in UTF-8. Recorded errors Testing API testGetValue Get the recorded value for a given string metric. The recorded value may have been truncated. See "Limits" section above. import org.mozilla.yourApplication.GleanMetrics.SearchDefault // Does the string metric have the expected value? assertEquals("wikipedia", SearchDefault.name.testGetValue())  import org.mozilla.yourApplication.GleanMetrics.SearchDefault // Does the string metric have the expected value? assertEquals("wikipedia", SearchDefault.INSTANCE.name.testGetValue());  @testable import Glean // Does the string metric have the expected value? XCTAssertEqual("wikipedia", try SearchDefault.name.testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Does the string metric have the expected value? assert "wikipedia" == metrics.search_default.name.test_get_value()   #![allow(unused)] fn main() { use glean_metrics; // Does the string metric have the expected value? assert_eq!(6, search_default::name.test_get_value(None).unwrap()); }  import * as searchDefault from "./path/to/generated/files/searchDefault.js"; assert.strictEqual("wikipedia", await searchDefault.name.testGetValue());  C++ #include "mozilla/glean/GleanMetrics.h" // Is it clear of errors? ASSERT_TRUE(mozilla::glean::search_default::name.TestGetValue().isOk()); // Does it have the expected value? ASSERT_STREQ( "wikipedia", mozilla::glean::search_default::name.TestGetValue().unwrap().value().get() );  JavaScript // Does it have the expected value? // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. Assert.equal("wikipedia", Glean.searchDefault.name.testGetValue());  testHasValue Whether or not any value was recorded for a given string metric. import org.mozilla.yourApplication.GleanMetrics.SearchDefault // Was anything recorded? assertTrue(SearchDefault.name.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.SearchDefault // Was anything recorded? assertTrue(SearchDefault.INSTANCE.name.testHasValue());  @testable import Glean // Was anything recorded? XCTAssert(SearchDefault.name.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Was anything recorded? assert metrics.search_default.name.test_has_value()  testGetNumRecordedErrors Gets number of errors recorded for a given string metric. import org.mozilla.yourApplication.GleanMetrics.SearchDefault // Was the string truncated, and an error reported? assertEquals(1, SearchDefault.name.testGetNumRecordedErrors(ErrorType.InvalidOverflow))  import org.mozilla.yourApplication.GleanMetrics.SearchDefault; // Was the string truncated, and an error reported? assertEquals( 1, SearchDefault.INSTANCE.name.testGetNumRecordedErrors( ErrorType.InvalidOverflow ) );  @testable import Glean // Was the string truncated, and an error reported? XCTAssertEqual(1, SearchDefault.name.testGetNumRecordedErrors(.invalidOverflow))  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Was the string truncated, and an error reported? assert 1 == metrics.search_default.name.test_get_num_recorded_errors( ErrorType.INVALID_OVERFLOW )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; // Was the string truncated, and an error reported? assert_eq!( 1, search_default::name.test_get_num_recorded_errors( ErrorType::InvalidOverflow ) ); }  import * as searchDefault from "./path/to/generated/files/searchDefault.js"; import { ErrorType } from "@mozilla/glean/<platform>"; // Was the string truncated, and an error reported? assert.strictEqual( 1, await searchDefault.name.testGetNumRecordedErrors(ErrorType.InvalidOverflow) );  Metric parameters Example string metric definition: controls: refresh_pressed: type: string description: > The name of the default search engine. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters N/A Data questions • Record the operating system name with a value of "android". • Recording the device model with a value of "SAMSUNG-SGH-I997". Reference Labeled Strings Labeled strings record multiple Unicode string values, each under a different label. Recording API set Sets one of the labels in a labeled string metric to a specific value. import org.mozilla.yourApplication.GleanMetrics.Login Login.errorsByStage["server_auth"].set("Invalid password")  import org.mozilla.yourApplication.GleanMetrics.Login; Login.INSTANCE.errorsByStage["server_auth"].set("Invalid password");  Login.errorsByStage["server_auth"].set("Invalid password")  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.login.errors_by_stage["server_auth"].set("Invalid password")   #![allow(unused)] fn main() { use glean_metrics; login::errors_by_stage.get("server_auth").set("Invalid password"); }  import * as login from "./path/to/generated/files/login.js"; login.errorsByStage["server_auth"].set("Invalid password");  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::login::errors_by_stage.Get("server_auth"_ns).Set("Invalid password"_ns);  JavaScript Glean.login.errorsByStage["server_auth"].set("Invalid password");  Limits • Fixed maximum string length: 100. Longer strings are truncated. This is measured in the number of bytes when the string is encoded in UTF-8. Recorded Errors • invalid_overflow: if the string is too long. (Prior to Glean 31.5.0, this recorded an invalid_value). • invalid_label: • If the label contains invalid characters. Data is still recorded to the special label __other__. • If the label exceeds the maximum number of allowed characters. Data is still recorded to the special label __other__. Testing API testGetValue Gets the recorded value for a given label in a labeled string metric. import org.mozilla.yourApplication.GleanMetrics.Login // Does the metric have the expected value? assertTrue(Login.errorsByStage["server_auth"].testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Login; // Does the metric have the expected value? assertTrue(Login.INSTANCE.errorsByStage["server_auth"].testGetValue());  @testable import Glean // Does the metric have the expected value? XCTAssert(Login.errorsByStage["server_auth"].testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Does the metric have the expected value? assert "Invalid password" == metrics.login.errors_by_stage["server_auth"].testGetValue())   #![allow(unused)] fn main() { use glean_metrics; // Does the metric have the expected value? assert!(login::errors_by_stage.get("server_auth").test_get_value()); }  import * as login from "./path/to/generated/files/login.js"; // Does the metric have the expected value? assert.strictEqual("Invalid password", await metrics.login.errorsByStage["server_auth"].testGetValue())  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_STREQ("Invalid password", mozilla::glean::login::errors_by_stage.Get("server_auth"_ns) .TestGetValue() .unwrap() .ref() .get());  JavaScript Assert.equal("Invalid password", Glean.login.errorsByStage["server_auth"].testGetValue());  testHasValue Whether or not any value was recorded for a given label in a labeled string metric. import org.mozilla.yourApplication.GleanMetrics.Login // Was anything recorded? assertTrue(Login.errorsByStage["server_auth"].testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Login; // Was anything recorded? assertTrue(Login.INSTANCE.errorsByStage["server_auth"].testHasValue());  @testable import Glean // Was anything recorded? XCTAssert(Login.errorsByStage["server_auth"].testHasValue())  # Was anything recorded? assert metrics.login.errors_by_stage["server_auth"].test_has_value()  testGetNumRecordedErrors Gets the number of errors recorded for a given labeled string metric in total. import org.mozilla.yourApplication.GleanMetrics.Login // Were there any invalid labels? assertEquals(0, Login.errorsByStage.testGetNumRecordedErrors(ErrorType.InvalidLabel))  import org.mozilla.yourApplication.GleanMetrics.Login; // Were there any invalid labels? assertEquals(0, Login.INSTANCE.errorsByStage.testGetNumRecordedErrors(ErrorType.InvalidLabel));  @testable import Glean // Were there any invalid labels? XCTAssertEqual(0, Login.errorsByStage.testGetNumRecordedErrors(.invalidLabel))  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Were there any invalid labels? assert 0 == metrics.login.errors_by_stage.test_get_num_recorded_errors( ErrorType.INVALID_LABEL )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; // Were there any invalid labels? assert_eq!( 0, login::errors_by_stage.test_get_num_recorded_errors( ErrorType::InvalidLabel ) ); }  import * as login from "./path/to/generated/files/login.js"; import { ErrorType } from "@mozilla/glean/<platform>"; // Were there any invalid labels? assert( 0, await login.errorsByStage.testGetNumRecordedErrors(ErrorType.InvalidLabel) );  Metric parameters Example labeled boolean metric definition: login: errors_by_stage: type: labeled_string description: Records the error type, if any, that occur in different stages of the login process. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01 labels: - server_auth - enter_email ...  Extra metric parameters labels Labeled metrics may have an optional labels parameter, containing a list of known labels. The labels in this list must match the following requirements: Important If the labels are specified in the metrics.yaml, using any label not listed in that file will be replaced with the special value __other__. If the labels are not specified in the metrics.yaml, only 16 different dynamic labels may be used, after which the special value __other__ will be used. Removing or changing labels, including their order in the registry file, is permitted. Avoid reusing labels that were removed in the past. It is best practice to add documentation about removed labels to the description field so that analysts will know of their existence and meaning in historical data. Special care must be taken when changing GeckoView metrics sent through the Glean SDK, as the index of the labels is used to report Gecko data through the Glean SDK. Data questions • What kinds of errors occurred at each step in the login process? Reference String List Strings lists are used for recording a list of Unicode string values, such as the names of the enabled search engines. Important Be careful using arbitrary strings and make sure they can't accidentally contain identifying data (like directory paths or user input). Recording API add Add a new string to the list. import org.mozilla.yourApplication.GleanMetrics.Search Search.engines.add("wikipedia") Search.engines.add("duck duck go")  import org.mozilla.yourApplication.GleanMetrics.Search Search.INSTANCE.engines().add("wikipedia") Search.INSTANCE.engines().add("duck duck go")  Search.engines.add("wikipedia") Search.engines.add("duck duck go")  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.search.engines.add("wikipedia") metrics.search.engines.add("duck duck go")  use glean_metrics::search; search::engines.add("wikipedia".to_string()); search::engines.add("duck duck go".to_string());  Glean.search.engines.add("wikipedia"); Glean.search.engines.add("duck duck go");  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::search::engines.Add("wikipedia"_ns); mozilla::glean::search::engines.Add("duck duck go"_ns);  JavaScript Glean.search.engines.add("wikipedia"); Glean.search.engines.add("duck duck go");  Recorded errors • invalid_overflow: if the string is too long. (Prior to Glean 31.5.0, this recorded an invalid_value). • invalid_value: if the list is too long. Limits • Fixed maximum string length: 50. Longer strings are truncated. This is measured in the number of bytes when the string is encoded in UTF-8. • Fixed maximum list length: 20 items. Additional strings are dropped. set Set the metric to a specific list of strings. import org.mozilla.yourApplication.GleanMetrics.Search Search.engines.set(listOf("wikipedia", "duck duck go"))  import org.mozilla.yourApplication.GleanMetrics.Search Search.INSTANCE.engines().set(listOf("wikipedia", "duck duck go"))  Search.engines.set(["wikipedia", "duck duck go"])  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.search.engines.set(["wikipedia", "duck duck go"])  use glean_metrics::search; search::engines.set(vec!["wikipedia".to_string(), "duck duck go".to_string()])  Glean.search.engines.set(["wikipedia", "duck duck go"]);  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::search::engines.Set({"wikipedia"_ns, "duck duck go"_ns});  JavaScript Glean.search.engines.set(["wikipedia", "duck duck go"]);  Recorded errors • invalid_overflow: if the string is too long. (Prior to Glean 31.5.0, this recorded an invalid_value). • invalid_value: if the list is too long. Limits • Fixed maximum string length: 50. Longer strings are truncated. This is measured in the number of bytes when the string is encoded in UTF-8. • Fixed maximum list length: 20 items. Additional strings are dropped. Testing API testGetValue Gets the recorded value for a given string list metric. import org.mozilla.yourApplication.GleanMetrics.Search assertEquals(listOf("Google", "DuckDuckGo"), Search.engines.testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Search assertEquals( Arrays.asList("Google", "DuckDuckGo"), Search.INSTANCE.engines().testGetValue() )  @testable import Glean XCTAssertEqual(["Google", "DuckDuckGo"], try Search.engines.testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert ["Google", "DuckDuckGo"] == metrics.search.engines.test_get_value()  use glean_metrics::search; assert_eq!( vec!["Google".to_string(), "DuckDuckGo".to_string()], search::engines.test_get_value(None).unwrap() );  // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. const engines = Glean.search.engines.testGetValue(); Assert.ok(engines.includes("wikipedia")); Assert.ok(engines.includes("duck duck go"));  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_EQUAL(mozilla::glean::search::engines.TestGetValue().isOk()); nsTArray<nsCString> list = mozilla::glean::search::engines.TestGetValue().unwrap(); ASSERT_TRUE(list.Contains("wikipedia"_ns)); ASSERT_TRUE(list.Constains("duck duck go"_ns));  JavaScript // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. const engines = Glean.search.engines.testGetValue(); Assert.ok(engines.includes("wikipedia")); Assert.ok(engines.includes("duck duck go"));  testHasValue Whether or not any value was recorded for a given string list metric. import org.mozilla.yourApplication.GleanMetrics.Search assertTrue(Search.engines.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Search assertTrue(Search.INSTANCE.engines().testHasValue())  @testable import Glean XCTAssert(Search.engines.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert metrics.search.engines.test_has_value()  testGetNumRecordedErrors Gets number of errors recorded for a given string list metric. import org.mozilla.yourApplication.GleanMetrics.Search assertEquals(1, Search.engines.testGetNumRecordedErrors(ErrorType.InvalidValue))  import org.mozilla.yourApplication.GleanMetrics.Search assertEquals( 1, Search.INSTANCE.engines().testGetNumRecordedErrors(ErrorType.InvalidValue) )  @testable import Glean // Were any of the values too long, and thus an error was recorded? XCTAssertEqual(1, Search.engines.testGetNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert 1 == metrics.search.engines.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )  use glean::ErrorType; use glean_metrics::search; assert_eq!( 0, search::engines.test_get_num_recorded_errors(ErrorType::InvalidValue) );  Metric parameters Example string list metric definition: search: engines: type: string_list description: > Records the name of the enabled search engines. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters N/A Data questions • Which search engines are enabled? Reference Timespan Timespans are used to make a measurement of how much time is spent in a particular task. To measure the distribution of multiple timespans, see Timing Distributions. To record absolute times, see Datetimes. It is not recommended to use timespans in multiple threads, since calling start or stop out of order will be recorded as an invalid_state error. Recording API start Starts tracking time. Uses an internal monotonic timer. import org.mozilla.yourApplication.GleanMetrics.Auth fun onShowLogin() { Auth.loginTime.start() // ... }  import org.mozilla.yourApplication.GleanMetrics.Auth; void onShowLogin() { Auth.INSTANCE.loginTime.start(); // ... }  func onShowLogin() { Auth.loginTime.start() // ... }  from glean import load_metrics metrics = load_metrics("metrics.yaml") def on_show_login(): metrics.auth.login_time.start() # ...   #![allow(unused)] fn main() { use fog::metrics; fn show_login() { metrics::auth::login_time.start(); // ... } }  import * as auth from "./path/to/generated/files/auth.js"; function onShowLogin() { auth.loginTime.start(); // ... }  C++ #include "mozilla/glean/GleanMetrics.h" void OnShowLogin() { mozilla::glean::auth::login_time.Start(); // ... }  JavaScript function onShowLogin() { Glean.auth.loginTime.start(); // ... }  Recorded errors • invalid_state: If the metric is already tracking time (start has already been called and not canceled). Limits • The maximum resolution of the elapsed duration is limited by the clock used on each platform. • This also determines the behavior of a timespan over sleep: stop Stops tracking time. The metric value is set to the elapsed time. import org.mozilla.yourApplication.GleanMetrics.Auth fun onLogin() { Auth.loginTime.stop() // ... }  import org.mozilla.yourApplication.GleanMetrics.Auth; void onLogin() { Auth.INSTANCE.loginTime.stop(); // ... }  func onLogin() { Auth.loginTime.stop() // ... }  from glean import load_metrics metrics = load_metrics("metrics.yaml") def on_login(): metrics.auth.login_time.stop() # ...   #![allow(unused)] fn main() { use fog::metrics; fn login() { metrics::auth::login_time.stop(); // ... } }  import * as auth from "./path/to/generated/files/auth.js"; function onLogin() { auth.login_time.stop(); // ... }  C++ #include "mozilla/glean/GleanMetrics.h" void OnLogin() { mozilla::glean::auth::login_time.Stop(); // ... }  JavaScript function onLogin() { Glean.auth.loginTime.stop(); // ... }  Recorded errors cancel Cancels a previous start. No error is recorded if there was no previous start. import org.mozilla.yourApplication.GleanMetrics.Auth fun onLoginCancel() { Auth.loginTime.cancel() // ... }  import org.mozilla.yourApplication.GleanMetrics.Auth; void onLoginCancel() { Auth.INSTANCE.loginTime.cancel(); // ... }  func onLoginCancel() { Auth.loginTime.cancel() // ... }  from glean import load_metrics metrics = load_metrics("metrics.yaml") def on_login_cancel(): metrics.auth.login_time.cancel() # ...   #![allow(unused)] fn main() { use fog::metrics; fn login_cancel() { metrics::auth::login_time.cancel(); // ... } }  import * as auth from "./path/to/generated/files/auth.js"; function onLoginCancel() { auth.login_time.cancel(); // ... }  C++ #include "mozilla/glean/GleanMetrics.h" void OnLoginCancel() { mozilla::glean::auth::login_time.Cancel(); // ... }  JavaScript function onLoginCancel() { Glean.auth.loginTime.cancel(); // ... }  measure Some languages support convenient auto timing of blocks of code. measure is treated as a start and stop pair for the purposes of error recording. Exceptions (if present in the language) are treated as a cancel. import org.mozilla.yourApplication.GleanMetrics.Auth Auth.loginTime.measure { // Process login flow }  import org.mozilla.yourApplication.GleanMetrics.Auth Auth.INSTANCE.loginTime.measure() -> { // Process login flow return null; });  Auth.loginTime.measure { // Process login flow }  from glean import load_metrics metrics = load_metrics("metrics.yaml") with metrics.auth.login_time.measure(): # ... Do the login ...  setRawNanos Explicitly sets the timespan's value. Regardless of the time unit chosen for the metric, this API expects the raw value to be in nanoseconds. Only use this if you have to This API should only be used if the code being instrumented cannot make use of start, stop, and cancel or measure. Time is hard, and this API can't help you with it. import org.mozilla.yourApplication.GleanMetrics.Auth fun afterLogin(loginElapsedNs: Long) { Auth.loginTime.setRawNanos(loginElapsedNs) // ... }  import org.mozilla.yourApplication.GleanMetrics.Auth; void afterLogin(long loginElapsedNs) { Auth.INSTANCE.loginTime.setRawNanos(loginElapsedNs); // ... }  func afterLogin(_ loginElapsedNs: UInt64) { Auth.loginTime.setRawNanos(loginElapsedNs) // ... }  from glean import load_metrics metrics = load_metrics("metrics.yaml") def after_login(login_elapsed_ns): metrics.auth.login_time.set_raw_nanos(login_elapsed_ns) # ...   #![allow(unused)] fn main() { use fog::metrics; use std::time::duration; fn after_login(login_elapsed: Duration) { metrics::auth::login_time.set_raw(login_elapsed); // ... } }  import * as auth from "./path/to/generated/files/auth.js"; function onAfterLogin(loginElapsedNs) { auth.loginTime.setRawNanos(loginElapsedNs); // ... }  These are different Firefox Desktop's setRaw uses the units specified in the metric definition. e.g. if the Timespan's time_unit is millisecond, then the duration parameter is a count of milliseconds. C++ #include "mozilla/glean/GleanMetrics.h" void AfterLogin(uint32_t aDuration) { mozilla::glean::auth::login_time.SetRaw(aDuration); // ... }  JavaScript function afterLogin(aDuration) { Glean.auth.loginTime.setRaw(aDuration); // ... }  Recorded errors Testing API testGetValue Get the currently-stored value. import org.mozilla.yourApplication.GleanMetrics.Auth assertTrue(Auth.loginTime.testGetValue() > 0)  import org.mozilla.yourApplication.GleanMetrics.Auth; assertTrue(Auth.INSTANCE.loginTime.testGetValue() > 0);  @testable import Glean XCTAssert(try Auth.loginTime.testGetValue() > 0)  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert metrics.auth.login_time.test_get_value() > 0   #![allow(unused)] fn main() { use fog::metrics; assert!(metrics::login_time.test_get_value().unwrap() > 0); }  import * as auth from "./path/to/generated/files/auth.js"; assert(await auth.loginTime.testGetValue() > 0);  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_TRUE(mozilla::glean::auth::login_time.TestGetValue().isOk()); ASSERT_GE(mozilla::glean::auth::login_time.TestGetValue().unwrap().value(), 0);  JavaScript // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. Assert.ok(Glean.auth.loginTime.testGetValue() > 0);  testHasValue Test if the metric currently stores any value. import org.mozilla.yourApplication.GleanMetrics.Auth assertTrue(Auth.loginTime.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Auth; assertTrue(Auth.INSTANCE.loginTime.testHasValue() > 0);  @testable import Glean XCTAssert(try Auth.loginTime.testHasValue() > 0)  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert metrics.auth.login_time.test_has_value() > 0   #![allow(unused)] fn main() { use fog::metrics; assert!(metrics::login_time.test_has_value().unwrap() > 0); }  testGetNumRecordedErrors Gets the number of errors recorded during operations on this metric. import org.mozilla.yourApplication.GleanMetrics.Auth assertEquals(1, Auth.loginTime.testGetNumRecordedErrors(ErrorType.InvalidValue))  import org.mozilla.yourApplication.GleanMetrics.Auth; assertEquals( 1, Auth.INSTANCE.loginTime.testGetNumRecordedErrors( ErrorType.InvalidValue ) );  @testable import Glean XCTAssertEqual(1, Auth.loginTime.testGetNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert 1 == metrics.auth.local_time.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )   #![allow(unused)] fn main() { use fog::metrics; assert_eq!(1, login_time.test_get_num_recorded_errors(ErrorType::InvalidValue)); }  import * as auth from "./path/to/generated/files/auth.js"; import { ErrorType } from "@mozilla/glean/<platform>";; assert.strictEqual( 1, await auth.loginTime.testGetNumRecordedErrors(ErrorType.InvalidValue) );  Metric parameters Example timespan metric definition: auth: login_time: type: timespan description: > Measures the time spent logging in. time_unit: millisecond bugs: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-01-01 data_sensitivity: - interaction  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters time_unit Timespans have a required time_unit parameter to specify the smallest unit of resolution that the timespan will record. The allowed values for time_unit are: • nanosecond • microsecond • millisecond • second • minute • hour • day Consider the resolution that is required by your metric, and use the largest possible value that will provide useful information so as to not leak too much fine-grained information from the client. Values are truncated It is important to note that the value sent in the ping is truncated down to the nearest unit. Therefore, a measurement of 500 nanoseconds will be truncated to 0 microseconds. Data questions • How long did it take for the user to log in? Reference Timing Distribution Timing distributions are used to accumulate and store time measurement, for analyzing distributions of the timing data. To measure the distribution of single timespans, see Timespans. To record absolute times, see Datetimes. Timing distributions are recorded in a histogram where the buckets have an exponential distribution, specifically with 8 buckets for every power of 2. That is, the function from a value $$x$$ to a bucket index is: $\lfloor 8 \log_2(x) \rfloor$ This makes them suitable for measuring timings on a number of time scales without any configuration. Note Check out how this bucketing algorithm would behave on the Simulator Timings always span the full length between start and stopAndAccumulate. If the Glean upload is disabled when calling start, the timer is still started. If the Glean upload is disabled at the time stopAndAccumulate is called, nothing is recorded. Multiple concurrent timespans in different threads may be measured at the same time. Timings are always stored and sent in the payload as nanoseconds. However, the time_unit parameter controls the minimum and maximum values that will recorded: • nanosecond: 1ns <= x <= 10 minutes • microsecond: 1μs <= x <= ~6.94 days • millisecond: 1ms <= x <= ~19 years Overflowing this range is considered an error and is reported through the error reporting mechanism. Underflowing this range is not an error and the value is silently truncated to the minimum value. Additionally, when a metric comes from GeckoView (the geckoview_datapoint parameter is present), the time_unit parameter specifies the unit that the samples are in when passed to Glean. Glean will convert all of the incoming samples to nanoseconds internally. Configuration If you wanted to create a timing distribution to measure page load times, first you need to add an entry for it to the metrics.yaml file: pages: page_load: type: timing_distribution description: > Counts how long each page takes to load ...  API Now you can use the timing distribution from the application's code. Starting a timer returns a timer ID that needs to be used to stop or cancel the timer at a later point. Multiple intervals can be measured concurrently. For example, to measure page load time on a number of tabs that are loading at the same time, each tab object needs to store the running timer ID. import mozilla.components.service.glean.GleanTimerId import org.mozilla.yourApplication.GleanMetrics.Pages val timerId : GleanTimerId fun onPageStart(e: Event) { timerId = Pages.pageLoad.start() } fun onPageLoaded(e: Event) { Pages.pageLoad.stopAndAccumulate(timerId) }  For convenience one can measure the time of a function or block of code: Pages.pageLoad.measure { // Load a page }  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the pageLoad example above, at this point the metric should have a sum == 11 and a count == 2: import org.mozilla.yourApplication.GleanMetrics.Pages // Was anything recorded? assertTrue(Pages.pageLoad.testHasValue()) // Get snapshot. val snapshot = Pages.pageLoad.testGetValue() // Usually you don't know the exact timing values, but how many should have been recorded. assertEquals(1L, snapshot.count) // Assert that no errors were recorded. assertEquals(0, Pages.pageLoad.testGetNumRecordedErrors(ErrorType.InvalidValue))  import mozilla.components.service.glean.GleanTimerId; import org.mozilla.yourApplication.GleanMetrics.Pages; GleanTimerId timerId; void onPageStart(Event e) { timerId = Pages.INSTANCE.pageLoad.start(); } void onPageLoaded(Event e) { Pages.INSTANCE.pageLoad.stopAndAccumulate(timerId); }  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the pageLoad example above, at this point the metric should have a sum == 11 and a count == 2: import org.mozilla.yourApplication.GleanMetrics.Pages; // Was anything recorded? assertTrue(pages.INSTANCE.pageLoad.testHasValue()); // Get snapshot. DistributionData snapshot = pages.INSTANCE.pageLoad.testGetValue(); // Usually you don't know the exact timing values, but how many should have been recorded. assertEquals(1L, snapshot.getCount); // Assert that no errors were recorded. assertEquals( 0, pages.INSTANCE.pageLoad.testGetNumRecordedErrors( ErrorType.InvalidValue ) );  import Glean var timerId : GleanTimerId func onPageStart() { timerId = Pages.pageLoad.start() } func onPageLoaded() { Pages.pageLoad.stopAndAccumulate(timerId) }  For convenience one can measure the time of a function or block of code: Pages.pageLoad.measure { // Load a page }  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the pageLoad example above, at this point the metric should have a sum == 11 and a count == 2: @testable import Glean // Was anything recorded? XCTAssert(pages.pageLoad.testHasValue()) // Get snapshot. let snapshot = try! pages.pageLoad.testGetValue() // Usually you don't know the exact timing values, but how many should have been recorded. XCTAssertEqual(1, snapshot.count) // Assert that no errors were recorded. XCTAssertEqual(0, pages.pageLoad.testGetNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") class PageHandler: def __init__(self): self.timer_id = None def on_page_start(self, event): # ... self.timer_id = metrics.pages.page_load.start() def on_page_loaded(self, event): # ... metrics.pages.page_load.stop_and_accumulate(self.timer_id)  The Python bindings also have a context manager for measuring time: with metrics.pages.page_load.measure(): # Load a page ...  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the page_load example above, at this point the metric should have a sum == 11 and a count == 2: # Was anything recorded? assert metrics.pages.page_load.test_has_value() # Get snapshot. snapshot = metrics.pages.page_load.test_get_value() # Usually you don't know the exact timing values, but how many should have been recorded. assert 1 == snapshot.count # Assert that no errors were recorded. assert 0 == metrics.pages.page_load.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )  using static Mozilla.YourApplication.GleanMetrics.Pages; var timerId; void onPageStart(Event e) { timerId = Pages.pageLoad.Start(); } void onPageLoaded(Event e) { Pages.pageLoad.StopAndAccumulate(timerId); }  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the pageLoad example above, at this point the metric should have a sum == 11 and a count == 2: using static Mozilla.YourApplication.GleanMetrics.Pages; // Was anything recorded? Assert.True(Pages.pageLoad.TestHasValue()); // Get snapshot. var snapshot = Pages.pageLoad.TestGetValue(); // Usually you don't know the exact timing values, but how many should have been recorded. Assert.Equal(1, snapshot.Values.Count); // Assert that no errors were recorded. Assert.Equal(0, Pages.pageLoad.TestGetNumRecordedErrors(ErrorType.InvalidValue));   #![allow(unused)] fn main() { use glean_metrics; fn on_page_start() { self.timer_id = pages::page_load.start(); } fn on_page_loaded() { pages::page_load.stop_and_accumulate(self.timer_id); } }  There are test APIs available too.  #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; // Was anything recorded? assert!(pages::page_load.test_get_value(None).is_some()); // Assert no errors were recorded. let errors = [ ErrorType::InvalidValue, ErrorType::InvalidState, ErrorType::InvalidOverflow ]; for error in errors { assert_eq!(0, pages::page_load.test_get_num_recorded_errors(error)); } }  Note: C++ APIs are only available in Firefox Desktop. #include "mozilla/glean/GleanMetrics.h" auto timerId = mozilla::glean::pages::page_load.Start(); PR_Sleep(PR_MillisecondsToInterval(10)); mozilla::glean::pages::page_load.StopAndAccumulate(std::move(timerId));  There are test APIs available too: #include "mozilla/glean/GleanMetrics.h" // Does it have an expected values? const data = mozilla::glean::pages::page_load.TestGetValue().value().unwrap(); ASSERT_TRUE(data.sum > 0);  Note: JS APIs are only available in Firefox Desktop. const timerId = Glean.pages.pageLoad.start(); await sleep(10); Glean.pages.pageLoad.stopAndAccumulate(timerId);  There are test APIs available too: Assert.ok(Glean.pages.pageLoad.testGetValue().sum > 0);  Limits • Timings are recorded in nanoseconds. • The maximum timing value that will be recorded depends on the time_unit parameter: • nanosecond: 1ns <= x <= 10 minutes • microsecond: 1μs <= x <= ~6.94 days • millisecond: 1ms <= x <= ~19 years Longer times will be truncated to the maximum value and an error will be recorded. Examples • How long does it take a page to load? Recorded errors • invalid_value: If recording a negative timespan. • invalid_state: If a non-existing/stopped timer is stopped again. • invalid_overflow: If recording a time longer than the maximum for the given unit. Reference Simulator Please, insert your custom data below as a JSON array. Data options Properties Note The data provided, is assumed to be in the configured time unit. The data recorded, on the other hand, is always in nanoseconds. This means that, if the configured time unit is not nanoseconds, the data will be transformed before being recorded. Notice this, by using the select field above to change the time unit and see the mean of the data recorded changing. Memory Distribution Memory distributions are used to accumulate and store memory sizes. Memory distributions are recorded in a histogram where the buckets have an exponential distribution, specifically with 16 buckets for every power of 2. That is, the function from a value $$x$$ to a bucket index is: $\lfloor 16 \log_2(x) \rfloor$ This makes them suitable for measuring memory sizes on a number of different scales without any configuration. Note Check out how this bucketing algorithm would behave on the Simulator Configuration Memory distributions have a required memory_unit parameter, which specifies the unit the incoming memory size values are recorded in. The units are the power-of-2 units, so "kilobyte" is more correctly a "kibibyte". - kilobyte == 2^10 == 1,024 bytes - megabyte == 2^20 == 1,048,576 bytes - gigabyte == 2^30 == 1,073,741,824 bytes  If you wanted to create a memory distribution to measure the amount of heap memory allocated, first you need to add an entry for it to the metrics.yaml file: memory: heap_allocated: type: memory_distribution description: > The heap memory allocated memory_unit: kilobyte ...  API Now you can use the memory distribution from the application's code. Note The data provided to the accumulate method is in the configured memory unit specified in the metrics.yaml file. The data recorded, on the other hand, is always in bytes. For example, to measure the distribution of heap allocations: import org.mozilla.yourApplication.GleanMetrics.Memory fun allocateMemory(nbytes: Int) { // ... Memory.heapAllocated.accumulate(nbytes / 1024) }  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the heapAllocated example above, at this point the metric should have a sum == 11 and a count == 2: import org.mozilla.yourApplication.GleanMetrics.Memory // Was anything recorded? assertTrue(Memory.heapAllocated.testHasValue()) // Get snapshot val snapshot = Memory.heapAllocated.testGetValue() // Does the sum have the expected value? assertEquals(11, snapshot.sum) // Usually you don't know the exact memory values, but how many should have been recorded. assertEquals(2L, snapshot.count) // Did this record a negative value? assertEquals(1, Memory.heapAllocated.testGetNumRecordedErrors(ErrorType.InvalidValue))  func allocateMemory(nbytes: UInt64) { // ... Memory.heapAllocated.accumulate(nbytes / 1024) }  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the heapAllocated example above, at this point the metric should have a sum == 11 and a count == 2: @testable import Glean // Was anything recorded? XCTAssert(Memory.heapAllocated.testHasValue()) // Get snapshot let snapshot = try! Memory.heapAllocated.testGetValue() // Does the sum have the expected value? XCTAssertEqual(11, snapshot.sum) // Usually you don't know the exact memory values, but how many should have been recorded. XCTAssertEqual(2, snapshot.count) // Did this record a negative value? XCTAssertEqual(1, Memory.heapAllocated.testGetNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") def allocate_memory(nbytes): # ... metrics.memory.heap_allocated.accumulate(nbytes / 1024)  There are test APIs available too. For convenience, properties sum and count are exposed to facilitate validating that data was recorded correctly. Continuing the heapAllocated example above, at this point the metric should have a sum == 11 and a count == 2: # Was anything recorded? assert metrics.memory.head_allocated.test_has_value() # Get snapshot snapshot = metrics.memory.heap_allocated.test_get_value() # Does the sum have the expected value? assert 11 == snapshot.sum # Usually you don't know the exact memory values, but how many should have been recorded. assert 2 == snapshot.count # Did this record a negative value? assert 1 == metrics.memory.heap_allocated.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )  using static Mozilla.YourApplication.GleanMetrics.Memory; fun allocateMemory(ulong nbytes) { // ... Memory.heapAllocated.Accumulate(nbytes / 1024); }  There are test APIs available too. For convenience, properties Sum and Count are exposed to facilitate validating that data was recorded correctly. Continuing the heapAllocated example above, at this point the metric should have a Sum == 11 and a Count == 2: using static Mozilla.YourApplication.GleanMetrics.Memory; // Was anything recorded? Assert.True(Memory.heapAllocated.TestHasValue()); // Get snapshot var snapshot = Memory.heapAllocated.TestGetValue(); // Does the sum have the expected value? Assert.Equal(11, snapshot.Sum); // Usually you don't know the exact memory values, but how many should have been recorded. Assert.Equal(2L, snapshot.Count); // Did this record a negative value? Assert.Equal(1, Memory.heapAllocated.TestGetNumRecordedErrors(ErrorType.InvalidValue));   #![allow(unused)] fn main() { use glean_metrics; fn allocate_memory(bytes: u64) { memory::heap_allocated.accumulate(bytes / 1024); } }  There are test APIs available too:  #![allow(unused)] fn main() { use glean::{DistributionData, ErrorType}; use glean_metrics; // Was anything recorded? assert!(memory::heap_allocated.test_get_value(None).is_some()); // Is the sum as expected? let data = memory::heap_allocated.test_get_value(None).unwrap(); assert_eq!(11, data.sum) // The actual buckets and counts live in data.values. // Were there any errors? assert_eq!(1, memory::heap_allocated.test_get_num_recorded_errors(InvalidValue)); }  Note: C++ APIs are only available in Firefox Desktop. #include "mozilla/glean/GleanMetrics.h" mozilla::glean::memory::heap_allocated.Accumulate(bytes / 1024);  There are test APIs available too: #include "mozilla/glean/GleanMetrics.h" // Does it have the expected value? ASSERT_EQ(11 * 1024, mozilla::glean::memory::heap_allocated.TestGetValue().unwrap().value().sum);  Note: JS APIs are only available in Firefox Desktop. Glean.memory.heapAllocated.accumulate(bytes / 1024);  There are test APIs available too: const data = Glean.memory.heapAllocated.testGetValue(); Assert.equal(11 * 1024, data.sum); // Does it have the right number of samples? Assert.equal(1, Object.entries(data.values).reduce(([bucket, count], sum) => count + sum, 0));  Limits • The maximum memory size that can be recorded is 1 Terabyte (240 bytes). Larger sizes will be truncated to 1 Terabyte. Examples • What is the distribution of the size of heap allocations? Recorded errors • invalid_value: If recording a negative memory size. • invalid_value: If recording a size larger than 1TB. Reference Simulator Please, insert your custom data below as a JSON array. Data options Properties Note The data provided, is assumed to be in the configured memory unit. The data recorded, on the other hand, is always in bytes. This means that, if the configured memory unit is not byte, the data will be transformed before being recorded. Notice this, by using the select field above to change the memory unit and see the mean of the data recorded changing. UUID UUIDs metrics are used to record values that uniquely identify some entity, such as a client id. Recording API generateAndSet Sets a UUID metric to a randomly generated UUID value (UUID v4) . import org.mozilla.yourApplication.GleanMetrics.User // Generate a new UUID and record it User.clientId.generateAndSet()  import org.mozilla.yourApplication.GleanMetrics.User; // Generate a new UUID and record it User.INSTANCE.clientId.generateAndSet();  // Generate a new UUID and record it User.clientId.generateAndSet()  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Generate a new UUID and record it metrics.user.client_id.generate_and_set()   #![allow(unused)] fn main() { use glean_metrics; use uuid::Uuid; // Generate a new UUID and record it user::client_id.generate_and_set(); }  import * as user from "./path/to/generated/files/user.js"; user.clientId.generateAndSet();  C++ #include "mozilla/glean/GleanMetrics.h" // Generate a new UUID and record it. mozilla::glean::user::client_id.GenerateAndSet();  JavaScript // Generate a new UUID and record it. Glean.user.clientId.generateAndSet();  set Sets a UUID metric to a specific value. Accepts any UUID version. import org.mozilla.yourApplication.GleanMetrics.User // Set a UUID explicitly User.clientId.set(UUID.randomUUID()) // Set a UUID explicitly  import org.mozilla.yourApplication.GleanMetrics.User; // Set a UUID explicitly User.INSTANCE.clientId.set(UUID.randomUUID());  User.clientId.set(UUID()) // Set a UUID explicitly  import uuid from glean import load_metrics metrics = load_metrics("metrics.yaml") # Set a UUID explicitly metrics.user.client_id.set(uuid.uuid4())   #![allow(unused)] fn main() { use glean_metrics; use uuid::Uuid; // Set a UUID explicitly user::client_id.set(Uuid::new_v4()); }  import * as user from "./path/to/generated/files/user.js"; const uuid = "decafdec-afde-cafd-ecaf-decafdecafde"; user.clientId.set(uuid);  C++ #include "mozilla/glean/GleanMetrics.h" // Set a specific value. nsCString kUuid("decafdec-afde-cafd-ecaf-decafdecafde"); mozilla::glean::user::client_id.Set(kUuid);  JavaScript // Set a specific value. const uuid = "decafdec-afde-cafd-ecaf-decafdecafde"; Glean.user.clientId.set(uuid);  Recorded errors • invalid_value: if the value is set to a string that is not a UUID (only applies for dynamically-typed languages, such as Python). Testing API testGetValue Gets the recorded value for a given UUID metric. import org.mozilla.yourApplication.GleanMetrics.User // Was it the expected value? assertEquals(uuid, User.clientId.testGetValue())  import org.mozilla.yourApplication.GleanMetrics.User; // Was it the expected value? assertEquals(uuid, User.INSTANCE.clientId.testGetValue());  @testable import Glean // Was it the expected value? XCTAssertEqual(uuid, try User.clientId.testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Was it the expected value? assert uuid == metrics.user.client_id.test_get_value()   #![allow(unused)] fn main() { use glean_metrics; use uuid::Uuid; let u = Uuid::new_v4(); // Does it have the expected value? assert_eq!(u, user::client_id.test_get_value(None).unwrap()); }  import * as user from "./path/to/generated/files/user.js"; const uuid = "decafdec-afde-cafd-ecaf-decafdecafde"; assert(uuid, await user.clientId.testGetValue());  C++ #include "mozilla/glean/GleanMetrics.h" // Is it clear of errors? ASSERT_TRUE(mozilla::glean::user::client_id.TestGetValue().isOk()); // Does it have an expected values? ASSERT_STREQ(kUuid.get(), mozilla::glean::user::client_id.TestGetValue().unwrap().value().get());  JavaScript const uuid = "decafdec-afde-cafd-ecaf-decafdecafde"; // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. Assert.equal(Glean.user.clientId.testGetValue(), uuid);  testHasValue Whether or not any value was recorded for a given UUID metric. import org.mozilla.yourApplication.GleanMetrics.User // Was anything recorded? assertTrue(User.clientId.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.User; // Was anything recorded? assertTrue(User.INSTANCE.clientId.testHasValue());  @testable import Glean // Was anything recorded? XCTAssert(User.clientId.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Was anything recorded? assert metrics.user.client_id.test_has_value()  testGetNumRecordedErrors Gets number of errors recorded for a given UUID metric. import org.mozilla.yourApplication.GleanMetrics.User assertEquals( 1, User.clientId.testGetNumRecordedErrors(ErrorType.InvalidValue) )  import org.mozilla.yourApplication.GleanMetrics.User; assertEquals( 1, User.INSTANCE.clientId.testGetNumRecordedErrors(ErrorType.InvalidValue) );  XCTAssertEqual(1, User.clientId.testGetNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") from glean.testing import ErrorType assert 1 == metrics.user.client_id.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; assert_eq!( 1, user::client_id.test_get_num_recorded_errors( ErrorType::InvalidValue ) ); }  import * as user from "./path/to/generated/files/user.js"; import { ErrorType } from "@mozilla/glean/<platform>"; // Was the string truncated, and an error reported? assert.strictEqual( 1, await user.clientId.testGetNumRecordedErrors(ErrorType.InvalidValue) );  Metric Parameters You first need to add an entry for it to the metrics.yaml file: user: client_id: type: uuid description: > A unique identifier for the client's profile bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters N/A Data questions • A unique identifier for the client. Reference URL URL metrics allow recording URL-like1 strings. This metric type does not support recording data URLs - please get in contact with the Glean SDK team if you're missing a type. Important Be careful using arbitrary URLs and make sure they can't accidentally contain identifying data (like directory paths, user input or credentials). 1 The Glean SDKs specifically do not validate if a URL is fully spec compliant, all the validations performed are the ones listed in the "Recorded errors" section of this page. Recording API set Set a URL metric to a specific string value. import org.mozilla.yourApplication.GleanMetrics.Search Search.template.set("https://mysearchengine.com/")  import org.mozilla.yourApplication.GleanMetrics.Search; Search.INSTANCE.template.set("https://mysearchengine.com/");  Search.template.set("https://mysearchengine.com") // Swift's URL type is supported let url = URL(string: "https://mysearchengine.com")! Search.template.set(url: url)  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.search.template.set("https://mysearchengine.com/")   #![allow(unused)] fn main() { use glean_metrics; search::template.set("https://mysearchengine.com/"); }  import * as search from "./path/to/generated/files/search.js"; search.template.set("https://mysearchengine.com/");  setUrl Set a URL metric to a specific URL value. import * as search from "./path/to/generated/files/search.js"; search.template.setUrl(new URL("https://mysearchengine.com/"));  Recorded errors Limits • Fixed maximum URL length: 2048. Longer URLs are dropped. Testing API testGetValue Gets the recorded value for a given URL metric as a (unencoded) string. import org.mozilla.yourApplication.GleanMetrics.Search assertEquals("https://mysearchengine.com/", Search.template.testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Search assertEquals("https://mysearchengine.com/", Search.INSTANCE.template.testGetValue());  @testable import Glean XCTAssertEqual("https://mysearchengine.com/", try Search.template.testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert "https://mysearchengine.com/" == metrics.search.template.test_get_value()   #![allow(unused)] fn main() { use glean_metrics; assert_eq!("https://mysearchengine.com/", search::template.test_get_value(None).unwrap()); }  import * as search from "./path/to/generated/files/search.js"; assert.strictEqual("https://mysearchengine.com/", await search.template.testGetValue());  testHasValue Whether or not any value was recorded for a given UUID metric. import org.mozilla.yourApplication.GleanMetrics.Search assertTrue(Search.template.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Search assertTrue(Search.INSTANCE.template.testHasValue());  @testable import Glean XCTAssert(try Search.template.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert metrics.search.template.test_has_value()  testGetNumRecordedErrors Gets number of errors recorded for a given counter metric. import org.mozilla.yourApplication.GleanMetrics.Search assertEquals(0, Search.template.testGetNumRecordedErrors(ErrorType.InvalidValue)) assertEquals(0, Search.template.testGetNumRecordedErrors(ErrorType.InvalidOverflow))  import org.mozilla.yourApplication.GleanMetrics.Search; assertEquals( 0, Search.INSTANCE.template.testGetNumRecordedErrors( ErrorType.InvalidValue ) ); assertEquals( 0, Search.INSTANCE.template.testGetNumRecordedErrors( ErrorType.InvalidOverflow ) );  @testable import Glean XCTAssertEqual(0, Search.template.testGetNumRecordedErrors(.invalidValue)) XCTAssertEqual(0, Search.template.testGetNumRecordedErrors(.invalidOverflow))  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert 0 == metrics.search.template.test_get_num_recorded_errors( ErrorType.INVALID_VALUE ) assert 0 == metrics.search.template.test_get_num_recorded_errors( ErrorType.INVALID_OVERFLOW )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; assert_eq!( 0, search::template.test_get_num_recorded_errors( ErrorType::InvalidValue ) ); assert_eq!( 0, search::template.test_get_num_recorded_errors( ErrorType::InvalidOverflow ) ); }  import * as search from "./path/to/generated/files/search.js"; import { ErrorType } from "@mozilla/glean/<platform>"; assert.strictEqual( 0, await search.template.testGetNumRecordedErrors(ErrorType.InvalidValue) ); assert.strictEqual( 0, await search.template.testGetNumRecordedErrors(ErrorType.InvalidOverflow) );  Metric parameters Example URL metric definition: search: template: type: url description: > The base URL used to build the search query for the search engine. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01 data_sensitivity: - web_activity  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML format reference page. Note on data_sensitivity of URL metrics URL metrics can only either be on categories 3 or 4, namely "Web activity data" or "Highly sensitive data". Extra metric parameters N/A Data questions • What is the base URL used to build the search query for the search engine? Reference Datetime Datetimes are used to record an absolute date and time, for example the date and time that the application was first run. The device's offset from UTC is recorded and sent with the Datetime value in the ping. To record a single elapsed time, see Timespan. To measure the distribution of multiple timespans, see Timing Distributions. Recording API set Sets a datetime metric to a specific date value. Defaults to now. import org.mozilla.yourApplication.GleanMetrics.Install Install.firstRun.set() // Records "now" Install.firstRun.set(Calendar(2019, 3, 25)) // Records a custom datetime  import org.mozilla.yourApplication.GleanMetrics.Install; Install.INSTANCE.firstRun.set(); // Records "now" Install.INSTANCE.firstRun.set(Calendar(2019, 3, 25)); // Records a custom datetime  Install.firstRun.set() // Records "now" let dateComponents = DateComponents( calendar: Calendar.current, year: 2004, month: 12, day: 9, hour: 8, minute: 3, second: 29 ) Install.firstRun.set(dateComponents.date!) // Records a custom datetime  import datetime from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.install.first_run.set() # Records "now" metrics.install.first_run.set(datetime.datetime(2019, 3, 25)) # Records a custom datetime  use glean_metrics; use chrono::{FixedOffset, TimeZone}; install::first_run.set(None); // Records "now" let custom_date = FixedOffset::east(0).ymd(2019, 3, 25).and_hms(0, 0, 0); install::first_run.set(Some(custom_date)); // Records a custom datetime  import * as install from "./path/to/generated/files/install.js"; install.firstRun.set(); // Records "now" install.firstRun.set(new Date("March 25, 2019 00:00:00")); // Records a custom datetime  C++ #include "mozilla/glean/GleanMetrics.h" PRExplodedTime date = {0, 35, 10, 12, 6, 10, 2020, 0, 0, {5 * 60 * 60, 0}}; mozilla::glean::install::first_run.Set(&date);  JavaScript const value = new Date("2020-06-11T12:00:00"); Glean.install.firstRun.set(value.getTime() * 1000);  Recorded errors Testing API testGetValue Get the recorded value for a given datetime metric as a language-specific Date object. import org.mozilla.yourApplication.GleanMetrics.Install assertEquals(Install.firstRun.testGetValue(), Date(2019, 3, 25))  import org.mozilla.yourApplication.GleanMetrics.Install; assertEquals(Install.INSTANCE.firstRun.testGetValue(), Date(2019, 3, 25));  @testable import Glean let expectedDate = DateComponents( calendar: Calendar.current, year: 2004, month: 12, day: 9, hour: 8, minute: 3, second: 29 ) XCTAssertEqual(expectedDate.date!, try Install.firstRun.testGetValue())  import datetime from glean import load_metrics metrics = load_metrics("metrics.yaml") value = datetime.datetime(1993, 2, 23, 5, 43, tzinfo=datetime.timezone.utc) assert value == metrics.install.first_run.test_get_value()  use glean_metrics; use chrono::{FixedOffset, TimeZone}; let expected_date = FixedOffset::east(0).ymd(2019, 3, 25).and_hms(0, 0, 0); assert_eq!(expected_date, metrics.install.first_run.test_get_value(None));  import * as install from "./path/to/generated/files/install.js"; const expectedDate = new Date("March 25, 2019 00:00:00"); assert.deepStrictEqual(expectedDate, await install.firstRun.testGetValue());  C++ #include "mozilla/glean/GleanMetrics.h" PRExplodedTime date{0, 35, 10, 12, 6, 10, 2020, 0, 0, {5 * 60 * 60, 0}}; ASSERT_TRUE(mozilla::glean::install::first_run.TestGetValue().isOk()); ASSERT_EQ( 0, std::memcmp( &date, mozilla::glean::install::first_run.TestGetValue().unwrap().ptr(), sizeof(date)));  JavaScript const value = new Date("2020-06-11T12:00:00"); // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. Assert.equal(Glean.install.firstRun.testGetValue().getTime(), value.getTime());  testGetValueAsString Get the recorded value for a given datetime metric as an ISO Date String. The returned string will be truncated to the metric's time unit and will include the timezone offset from UTC, e.g. 2019-03-25-05:00 (in this example, time_unit is day). import org.mozilla.yourApplication.GleanMetrics.Install assertEquals("2019-03-25-05:00", Install.firstRun.testGetValueAsString())  import org.mozilla.yourApplication.GleanMetrics.Install; assertEquals("2019-03-25-05:00", Install.INSTANCE.firstRun.testGetValueAsString());  @testable import Glean assertEquals("2019-03-25-05:00", try Install.firstRun.testGetValueAsString())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert "2019-03-25-05:00" == metrics.install.first_run.test_get_value_as_str()  import * as install from "./path/to/generated/files/install.js"; assert.strictEqual("2019-03-25-05:00", await install.firstRun.testGetValueAsString());  testHasValue Whether or not any value was recorded for a given datetime metric. import org.mozilla.yourApplication.GleanMetrics.Install assertTrue(Install.firstRun.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Install; assertTrue(Install.INSTANCE.firstRun.testHasValue());  @testable import Glean XCTAssert(Install.firstRun.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert metrics.install.first_run.test_has_value()  testGetNumRecordedErrors Get number of errors recorded for a given datetime metric. import mozilla.telemetry.glean.testing.ErrorType import org.mozilla.yourApplication.GleanMetrics.Install assertEquals(0, Install.firstRun.testGetNumRecordedErrors(ErrorType.InvalidValue))  import mozilla.telemetry.glean.testing.ErrorType import org.mozilla.yourApplication.GleanMetrics.Install; assertEquals(0, Install.INSTANCE.firstRun.testGetNumRecordedErrors(ErrorType.InvalidValue));  @testable import Glean XCTAssertEqual(0, Install.firstRun.getNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert 0 == metrics.install.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; assert_eq!(0, install::first_run.test_get_num_recorded_errors( ErrorType::InvalidValue )); }  import * as install from "./path/to/generated/files/install.js"; import { ErrorType } from "@mozilla/glean/<platform>"; assert.strictEqual( 1, await install.firstRun.testGetNumRecordedErrors(ErrorType.InvalidValue) );  Metric parameters Example datetime metric definition: install: first_run: type: datetime time_unit: day description: > Records the date when the application was first run bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters time_unit Datetimes have a required time_unit parameter to specify the smallest unit of resolution that the metric will record. The allowed values for time_unit are: • nanosecond • microsecond • millisecond • second • minute • hour • day Carefully consider the required resolution for recording your metric, and choose the coarsest resolution possible. Data questions • When did the user first run the application? Reference Events Events allow recording of e.g. individual occurrences of user actions, say every time a view was open and from where. Each event contains the following data: • A timestamp, in milliseconds. The first event in any ping always has a value of 0, and subsequent event timestamps are relative to it. • If sending events in custom pings, see note on event timestamp calculation throughout restarts. • The name of the event. • A set of key-value pairs, where the keys are predefined in the extra_keys metric parameter, and the values are strings. Are you sure you need an event metric? Events are best-suited to measuring user behavior, where the frequency of events is relatively low and the order of the events matters. Therefore, events should be the default choice for most user-behavior telemetry. For other types of telemetry (e.g. performance or stability), events may be too expensive metric type to record, transmit, store and, most importantly, analyze. In those cases, consider lighter metrics, such as counters. When in doubt, refer to the metric type choosing guide. Recording API record(object) Added in: v38.0.0 Record a new event, with optional typed extra values. This requires type annotations in the definition. See Extra metrics parameters. Note that an enum has been generated for handling the extra_keys: it has the same name as the event metric, with Extra added. import org.mozilla.yourApplication.GleanMetrics.Views Views.loginOpened.record(Views.loginOpenedExtra(sourceOfLogin = "toolbar"))  Note that an enum has been generated for handling the extra_keys: it has the same name as the event metric, with Extra added. Views.loginOpened.record(LoginOpenedExtra(sourceOfLogin: "toolbar"))  Note that an enum has been generated for handling the extra_keys: it has the same name as the event metric, with _extra added. from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.views.login_opened.record(LoginOpenedExtra(sourceOfLogin="toolbar"))  Note that an enum has been generated for handling the extra_keys: it has the same name as the event metric, with Keys added. use metrics::views::{self, LoginOpenedExtra}; let extra = LoginOpenedExtra { source_of_login: Some("toolbar".to_string()) }; views::login_opened.record(extra);  import * as views from "./path/to/generated/files/views.js"; views.loginOpened.record({ sourceOfLogin: "toolbar" });  C++ #include "mozilla/glean/GleanMetrics.h" using mozilla::glean::views::LoginOpenedExtra; LoginOpenedExtra extra = { .source_of_login = Some("value"_ns) }; mozilla::glean::views::login_opened.Record(std::move(extra))  JavaScript const extra = { sourceOfLogin: "toolbar" }; Glean.views.loginOpened.record(extra);  record(map) (deprecated) Deprecated in: v38.0.0 Record a new event, with optional extra values. Deprecation notice This API is used in v38.0.0 if an event has no type annotations in the definition. See Extra metrics parameters. In future versions extra values will default to a string type and this API will be removed. In Rust and Firefox Desktop this API is not supported. Note that an enum has been generated for handling the extra_keys: it has the same name as the event metric, with Keys added. import org.mozilla.yourApplication.GleanMetrics.Views Views.loginOpened.record(mapOf(Views.loginOpenedKeys.sourceOfLogin to "toolbar"))  import org.mozilla.yourApplication.GleanMetrics.Views; Views.INSTANCE.loginOpened.record();  Note that an enum has been generated for handling the extra_keys: it has the same name as the event metric, with Keys added. Views.loginOpened.record(extra: [.sourceOfLogin: "toolbar"])  Note that an enum has been generated for handling the extra_keys: it has the same name as the event metric, with _keys added. from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.views.login_opened.record( { metrics.views.login_opened_keys.SOURCE_OF_LOGIN: "toolbar" } )  import * as views from "./path/to/generated/files/views.js"; views.loginOpened.record({ sourceOfLogin: "toolbar" });  Recorded errors • invalid_overflow: if any of the values in the extras object are greater than 50 bytes in length. (Prior to Glean 31.5.0, this recorded an invalid_value). Testing API testGetValue Get the list of recorded events. import org.mozilla.yourApplication.GleanMetrics.Views val snapshot = Views.loginOpened.testGetValue() assertEquals(2, snapshot.size) val first = snapshot.single() assertEquals("login_opened", first.name)  import org.mozilla.yourApplication.GleanMetrics.Views assertEquals(Views.INSTANCE.loginOpened.testGetValue().size)  @testable import Glean val snapshot = try! Views.loginOpened.testGetValue() XCTAssertEqual(2, snapshot.size) val first = snapshot[0] XCTAssertEqual("login_opened", first.name)  from glean import load_metrics metrics = load_metrics("metrics.yaml") snapshot = metrics.views.login_opened.test_get_value() assert 2 == len(snapshot) first = snapshot[0] assert "login_opened" == first.name  use metrics::views; var snapshot = views::login_opened.test_get_value(None).unwrap(); assert_eq!(2, snapshot.len()); let first = &snapshot[0]; assert_eq!("login_opened", first.name);  import * as views from "./path/to/generated/files/views.js"; const snapshot = await views.loginOpened.testGetValue(); assert.strictEqual(2, snapshot.length); const first = snapshot[0] assert.strictEqual("login_opened", first.name)  C++ #include "mozilla/glean/GleanMetrics.h" auto optEvents = mozilla::glean::views::login_opened.TestGetValue(); auto events = optEvents.extract(); ASSERT_EQ(2UL, events.Length()); ASSERT_STREQ("login_opened", events[0].mName.get());  JavaScript var events = Glean.views.loginOpened.testGetValue(); Assert.equal(2, events.length); Assert.equal("login_opened", events[0].name);  testHasValue Whether or not any event was recorded for a given event metric. import org.mozilla.yourApplication.GleanMetrics.Views assertTrue(Views.loginOpened.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Views assertTrue(Views.INSTANCE.loginOpened.testHasValue())  @testable import Glean XCTAssert(Views.loginOpened.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") assert metrics.views.login_opened.test_has_value()  testGetNumRecordedErrors Get the number of errors recorded for a given event metric. import mozilla.telemetry.glean.testing.ErrorType import org.mozilla.yourApplication.GleanMetrics.Views assertEquals(0, Views.loginOpened.testGetNumRecordedErrors(ErrorType.InvalidOverflow))  import mozilla.telemetry.glean.testing.ErrorType import org.mozilla.yourApplication.GleanMetrics.Views assertEquals(0, Views.INSTANCE.loginOpened.testGetNumRecordedErrors(ErrorType.InvalidOverflow))  @testable import Glean XCTAssertEqual(0, Views.loginOpened.testGetNumRecordedErrors(.invalidOverflow))  from glean import load_metrics from glean.testing import ErrorType metrics = load_metrics("metrics.yaml") assert 0 == metrics.views.login_opened.test_get_num_recorded_errors( ErrorType.INVALID_OVERFLOW )  use glean::ErrorType; use metrics::views; assert_eq!( 0, views::login_opened.test_get_num_recorded_errors(ErrorType::InvalidOverflow, None) );  import * as views from "./path/to/generated/files/views.js"; import { ErrorType } from "@mozilla/glean/<platform>"; assert.strictEqual( 1, await views.loginOpened.testGetNumRecordedErrors(ErrorType.InvalidValue) );  Metric parameters Example event metric definition: views: login_opened: type: event description: | Recorded when the login view is opened. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01 extra_keys: source_of_login: description: The source from which the login view was opened, e.g. "toolbar". type: string  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Events require lifetime: ping. Recorded events are always sent in their respective pings and then cleared. They cannot be persisted longer. The glean_parser will reject any other lifetime. Extra metric parameters extra_keys The acceptable keys on the "extra" object sent with events. A maximum of 10 extra keys is allowed. Each extra key contains additional metadata: • description: Required. A description of the key. • type: The type of value this extra key can hold. One of string, boolean, quantity. Defaults to string. Note: If not specified only the legacy API on record is available. Data questions • When and from where was the login view opened? Limits • When 500 events are queued on the client an events ping is immediately sent. • The extra_keys allows for a maximum of 10 keys. • The keys in the extra_keys list must be in dotted snake case, with a maximum length of 40 bytes, when encoded as UTF-8. • The values in the extras object have a maximum of 50 bytes, when encoded as UTF-8. Reference Custom Distribution Custom distributions are used to record the distribution of arbitrary values. It should be used only when direct control over how the histogram buckets are computed is required. Otherwise, look at the standard distribution metric types: Note: Custom distributions are currently not universally supported. See below for available APIs. Recording API accumulateSamples Accumulate the provided samples in the metric. import org.mozilla.yourApplication.GleanMetrics.Graphics Graphics.checkerboardPeak.accumulateSamples([23])  use glean_metrics; graphics::checkerboard_peak.accumulate_samples_signed(vec![23]);  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::graphics::checkerboard_peak.AccumulateSamples({ 23 });  JavaScript Glean.graphics.checkerboardPeak.accumulateSamples([23])  Limits • The maximum value of bucket_count is 100. • Only non-negative values may be recorded (>= 0). Recorded errors • invalid_value: If recording a negative value. Testing API testGetValue Gets the recorded value for a given counter metric. import org.mozilla.yourApplication.GleanMetrics.Graphics // Get snapshot val snapshot = Graphics.checkerboardPeak.testGetValue() // Does the sum have the expected value? assertEquals(11, snapshot.sum) // Usually you don't know the exact timing values, but how many should have been recorded. assertEquals(2L, snapshot.count())  use glean::ErrorType; use glean_metrics::graphics; // Does it have the expected value? assert_eq!(23, graphics::checkerboard_peak.test_get_value(None).unwrap().sum);  C++ #include "mozilla/glean/GleanMetrics.h" auto data = mozilla::glean::graphics::checkerboard_peak.TestGetValue().value(); ASSERT_EQ(23UL, data.sum);  JavaScript let data = Glean.graphics.checkerboardPeak.testGetValue(); Assert.equal(23, data.sum);  testHasValue Whether or not any value was recorded for a given counter metric. import org.mozilla.yourApplication.GleanMetrics.Graphics // Was anything recorded? assertTrue(Graphics.checkerboardPeak.testHasValue())  testGetNumRecordedErrors Gets number of errors recorded for a given counter metric. import org.mozilla.yourApplication.GleanMetrics.Graphics /// Did the metric receive a negative value? assertEquals(1, Graphics.checkerboardPeak.testGetNumRecordedErrors(ErrorType.InvalidValue))  use glean::ErrorType; use glean_metrics::graphics; // Were any of the values negative and thus caused an error to be recorded? assert_eq!( 0, graphics::checkerboard_peak.test_get_num_recorded_errors(ErrorType::InvalidValue));  Metric Parameters Example custom distribution metric definition: graphics: checkerboard_peak: type: custom_distribution description: > Peak number of CSS pixels checkerboarded during a checkerboard event. range_min: 1 range_max: 66355200 bucket_count: 50 histogram_type: exponential unit: pixels gecko_datapoint: CHECKERBOARD_PEAK bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  Extra metric parameters Custom distributions have the following required parameters: • range_min: (Integer) The minimum value of the first bucket • range_max: (Integer) The minimum value of the last bucket • bucket_count: (Integer) The number of buckets • histogram_type: • linear: The buckets are evenly spaced • exponential: The buckets follow a natural logarithmic distribution Note Check out how these bucketing algorithms would behave on the Custom distribution simulator. Custom distributions have the following optional parameters: • unit: (String) The unit of the values in the metric. For documentation purposes only -- does not affect data collection. • gecko_datapoint: (String) This is a Gecko-specific property. It is the name of the Gecko metric to accumulate the data from, when using the Glean SDK in a product using GeckoView. Reference Simulator Please, insert your custom data below as a JSON array. Data options Properties Quantity Used to record a single non-negative integer value or 0. For example, the width of the display in pixels. Do not use Quantity for counting If you need to count something (e.g. the number of times a button is pressed) prefer using the Counter metric type, which has a specific API for counting things. Recording API set Sets a quantity metric to a specific value. import org.mozilla.yourApplication.GleanMetrics.Display Display.width.set(width)  import org.mozilla.yourApplication.GleanMetrics.Display; Display.INSTANCE.width.set(width);  Display.width.set(width)  from glean import load_metrics metrics = load_metrics("metrics.yaml") metrics.display.width.set(width)  import * as display from "./path/to/generated/files/display.js"; display.width.set(window.innerHeight);   #![allow(unused)] fn main() { use glean_metrics; display::width.set(width); }  C++ #include "mozilla/glean/GleanMetrics.h" mozilla::glean::display::width.Set(innerHeight);  JavaScript Glean.display.width.set(innerHeight);  Limits • Quantities must be non-negative integers or 0. Recorded errors Testing API testGetValue Gets the recorded value for a given quantity metric. import org.mozilla.yourApplication.GleanMetrics.Display // Does the quantity have the expected value? assertEquals(6, Display.width.testGetValue())  import org.mozilla.yourApplication.GleanMetrics.Display; // Was anything recorded? assertTrue(Display.INSTANCE.width.testHasValue()); // Does the quantity have the expected value? assertEquals(6, Display.INSTANCE.width.testGetValue());  @testable import Glean // Does the quantity have the expected value? XCTAssertEqual(6, try Display.width.testGetValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Does the quantity have the expected value? assert 6 == metrics.display.width.test_get_value()  import * as display from "./path/to/generated/files/display.js"; assert.strictEqual(433, await display.width.testGetValue());   #![allow(unused)] fn main() { use glean_metrics; // Was anything recorded? assert!(display::width.test_get_value(None).is_some()); }  C++ #include "mozilla/glean/GleanMetrics.h" ASSERT_TRUE(mozilla::glean::display::width.TestGetValue().isOk()); ASSERT_EQ(433, mozilla::glean::display::width.TestGetValue().unwrap().value());  JavaScript // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. Assert.equal(433, Glean.display.width.testGetValue());  testHasValue Whether or not any value was recorded for a given quantity metric. import org.mozilla.yourApplication.GleanMetrics.Display // Was anything recorded? assertTrue(Display.width.testHasValue())  import org.mozilla.yourApplication.GleanMetrics.Display; // Was anything recorded? assertTrue(Display.INSTANCE.width.testHasValue()); );  @testable import Glean // Was anything recorded? XCTAssert(Display.width.testHasValue())  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Was anything recorded? assert metrics.display.width.test_has_value()  testGetNumRecordedErrors Gets number of errors recorded for a given quantity metric. import org.mozilla.yourApplication.GleanMetrics.Display // Did it record an error due to a negative value? assertEquals(1, Display.width.testGetNumRecordedErrors(ErrorType.InvalidValue))  import org.mozilla.yourApplication.GleanMetrics.Display; // Did the quantity record a negative value? assertEquals( 1, Display.INSTANCE.width.testGetNumRecordedErrors(ErrorType.InvalidValue) );  @testable import Glean // Did the quantity record a negative value? XCTAssertEqual(1, Display.width.testGetNumRecordedErrors(.invalidValue))  from glean import load_metrics metrics = load_metrics("metrics.yaml") # Did the quantity record an negative value? from glean.testing import ErrorType assert 1 == metrics.display.width.test_get_num_recorded_errors( ErrorType.INVALID_VALUE )  import * as display from "./path/to/generated/files/display.js"; import { ErrorType } from "@mozilla/glean/<platform>"; assert.strictEqual( 0, await display.width.testGetNumRecordedErrors(ErrorType.InvalidValue) );   #![allow(unused)] fn main() { use glean::ErrorType; use glean_metrics; assert_eq!( 1, display::width.test_get_num_recorded_errors( ErrorType::InvalidValue ) ); }  Metric parameters Example quantity metric definition: controls: refresh_pressed: type: quantity description: > The width of the display, in pixels. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01 unit: pixels  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Extra metric parameters unit Quantities have the required unit parameter, which is a free-form string for documentation purposes. Data questions • What is the width of the display, in pixels? Reference Rate Used to count how often something happens relative to how often something else happens. Like how many documents use a particular CSS Property, or how many HTTP connections had an error. You can think of it like a fraction, with a numerator and a denominator. All rates start without a value. A rate with a numerator of 0 is valid and will be sent to ensure we capture the "no errors happened" or "no use counted" cases. Let the Glean metric do the counting When using a rate metric, it is important to let the Glean metric do the counting. Using your own variable for counting and setting the metric yourself could be problematic: ping scheduling will make it difficult to ensure the metric is at the correct value at the correct time. Instead, count to the numerator and denominator as you go. Recording API addToNumerator / addToDenominator Count numerator and denominator individually: use glean_metrics::*; if connection_had_error { network::http_connection_error.add_to_numerator(1); } network::http_connection_error.add_to_denominator(1);  External Denominators If the rate uses an external denominator, adding to the denominator must be done through the denominator's counter API: use glean_metrics; if connection_had_error { network::http_connection_error.add_to_numerator(1); } network::http_connections.add(1);  C++ #include "mozilla/glean/GleanMetrics.h" if (aHadError) { mozilla::glean::network::http_connection_error.add_to_numerator(1); } mozilla::glean::network::http_connection_error.add_to_denominator(1);  JavaScript if (aHadError) { Glean.network.httpConnectionError.addToNumerator(1); } Glean.network.httpConnectionError.addToDenominator(1);  Recorded errors • invalid_value: If either numerator or denominator is incremented by a negative value. Limits • Numerator and Denominator only increment. • Numerator and Denominator saturate at the largest value that can be represented as a 32-bit signed integer (2147483647). Testing API testGetValue use glean_metrics::*; assert_eq!((1, 1), network::http_connection_error.test_get_value(None).unwrap());  C++ #include "mozilla/glean/GleanMetrics.h" auto pair = mozilla::glean::network::http_connection_error.TestGetValue().unwrap(); ASSERT_EQ(1, pair.first); ASSERT_EQ(1, pair.second);  JavaScript // testGetValue will throw NS_ERROR_LOSS_OF_SIGNIFICANT_DATA on error. Assert.deepEqual( { numerator: 1, denominator: 1 }, Glean.network.httpConnectionError.testGetValue() );  testHasValue testGetNumRecordedErrors use glean_metrics::*; use glean::ErrorType; assert_eq!( 0, network::http_connection_error.test_get_num_recorded_errors( ErrorType::InvalidValue ) );  Metric parameters Example rate metric definition: network: http_connection_error: type: rate description: > How many HTTP connections error out out of the total connections made. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. External Denominators If several rates share the same denominator then the denominator should be defined as a counter and shared between rates using the denominator_metric property: network: http_connections: type: counter description: > Total number of http connections made. ... http_connection_error: type: rate description: > How many HTTP connections error out out of the total connections made. denominator_metric: network.http_connections ... http_connection_slow: type: rate description: > How many HTTP connections were slow, out of the total connections made. denominator_metric: network.http_connections ...  Data Questions • How often did an HTTP connection error? • How many documents used a given CSS Property? Reference Text Records a single long Unicode text, used when the limits on String are too low. Important This type should only be used in special cases when other metrics don't fit. See limitations below. Reach out to the Glean team before using this. Recording API set Sets a text metric to a specific value. import * as article from "./path/to/generated/files/article.js"; article.content.set(extractedText);  Limits • Text metrics can only be sent in custom pings. • Text metrics are always of data collection category 3 (web_activity) or category 4 (highly_sensitive). • Only ping and application lifetimes are allowed. • Fixed maximum text length: 200 kilobytes. Longer text is truncated. This is measured in the number of bytes when the string is encoded in UTF-8. Recorded errors Testing API testGetValue Gets the recorded value for a given text metric. import * as article from "./path/to/generated/files/article.js"; assert.strictEqual("some content", await article.content.testGetValue());  testGetNumRecordedErrors Gets number of errors recorded for a given text metric. import * as article from "./path/to/generated/files/article.js"; import { ErrorType } from "@mozilla/glean/<platform>"; assert.strictEqual( 0, await article.content.testGetNumRecordedErrors(ErrorType.InvalidValue) );  Metric parameters Example text metric definition: article: content: type: text lifetime: ping send_in_pings: - research data_sensitivity: - web_activity description: > The plaintext content of the displayed article. bugs: - https://bugzilla.mozilla.org/000000 data_reviews: - https://bugzilla.mozilla.org/show_bug.cgi?id=000000#c3 notification_emails: - me@mozilla.com expires: 2020-10-01  For a full reference on metrics parameters common to all metric types, refer to the metrics YAML registry format reference page. Data questions • What article content was displayed to the user? Pings Glean-owned pings are submitted automatically Products do not need to submit Glean built-in pings, as their scheduling is managed internally. The APIs on this page are only relevant for products defining custom pings. Submission API submit Collect and queue a custom ping for eventual uploading. By default, if the ping doesn't currently have any events or metrics set, submit will do nothing. However, if the send_if_empty flag is set to true in the ping definition, it will always be submitted. It is not necessary for the caller to check if Glean is disabled before calling submit. If Glean is disabled submit is a no-op. For example, to submit the custom ping defined in Adding new custom pings: import org.mozilla.yourApplication.GleanMetrics.Pings Pings.search.submit( Pings.searchReasonCodes.performed )  import org.mozilla.yourApplication.GleanMetrics.Pings Pings.INSTANCE.search.submit( Pings.INSTANCE.searchReasonCodes.performed );  import Glean GleanMetrics.Pings.shared.search.submit( reason: .performed )  from glean import load_pings pings = load_pings("pings.yaml") pings.search.submit(pings.search_reason_codes.PERFORMED)  use glean::Pings; pings::search.submit(pings::SearchReasonCodes::Performed);  import * as pings from "./path/to/generated/files/pings.js"; pings.search.submit(pings.searchReasonCodes.Performed);  C++ mozilla::glean_pings::Search.Submit("performed"_ns);  JavaScript GleanPings.search.submit("performed");  Testing API testBeforeNextSubmit Runs a validation function before the ping is collected. import org.mozilla.yourApplication.GleanMetrics.Search import org.mozilla.yourApplication.GleanMetrics.Pings // Record some data. Search.defaultEngine.add(5); // Instruct the ping API to validate the ping data. var validatorRun = false Pings.search.testBeforeNextSubmit { reason -> assertEquals(Pings.searchReasonCodes.performed, reason) assertEquals(5, Search.defaultEngine.testGetValue()) validatorRun = true } // Submit the ping. Pings.search.submit( Pings.searchReasonCodes.performed ) // Verify that the validator run. assertTrue(validatorRun)  import org.mozilla.yourApplication.GleanMetrics.Search import org.mozilla.yourApplication.GleanMetrics.Pings // Record some data. Search.INSTANCE.defaultEngine.add(5); // Instruct the ping API to validate the ping data. boolean validatorRun = false; Pings.INSTANCE.search.testBeforeNextSubmit((reason) -> { assertEquals(Pings.searchReasonCodes.performed, reason); assertEquals(5, Search.INSTANCE.defaultEngine.testGetValue()); validatorRun = true; }); // Submit the ping. Pings.INSTANCE.search.submit( Pings.INSTANCE.searchReasonCodes.performed ); // Verify that the validator run. assertTrue(validatorRun);  @testable import Glean // Record some data. Search.defaultEngine.add(5) // Instruct the ping API to validate the ping data. var validatorRun = false GleanMetrics.pings.shared.search.testBeforeNextSubmit { reason in XCTAssertEqual(.performed, reason, "Unexpected reason for search ping submitted") XCTAssertEqual(5, try Search.defaultEngine.testGetValue(), "Unexpected value for default engine in search ping") validatorRun = true } // Submit the ping. GleanMetrics.Pings.shared.search.submit( reason: .performed ) // Verify that the validator run. XCTAssert(validatorRun, "Expected validator to be called by now.")  from glean import load_metrics, load_pings pings = load_pings("pings.yaml") metrics = load_metrics("metrics.yaml") # Record some data. metrics.search.default_engine.add(5) # Need a mutable object and plain booleans are not. callback_was_called = [False] def check_custom_ping(reason): assert reason == pings.search_reason_codes.PERFORMED assert 5 == metrics.search.default_engine.test_get_value() callback_was_called[0] = True # Instruct the ping API to validate the ping data. pings.search.test_before_next_submit(check_custom_ping) # Submit the ping. pings.search.submit(pings.search_reason_codes.PERFORMED) # Verify that the validator run. assert callback_was_called[0]  use glean::Pings; use glean_metrics; // Record some data. search::default_engine.add(5); // Instruct the ping API to validate the ping data. pings::search.test_before_next_submit(move |reason| { assert_eq!(pings::SearchReasonCodes::Performed, reason); assert_eq!(5, search::default_engine.test_get_value(None).unwrap()); }); // When the submit API is not directly called by the // test code, it may be worth checking that the validator // function run by using a canary boolean in the closure // used in test_before_next_submit and asserting on its // value after submission. // Submit the ping. pings::search.submit(pings::SearchReasonCodes::Performed);  import * as search from "./path/to/generated/files/search.js"; import * as pings from "./path/to/generated/files/pings.js"; // Record some data. search.defaultEngine.add(5); // Instruct the ping API to validate the ping data. let validatorRun = false; const p = pings.search.testBeforeNextSubmit(async reason => { assert.strictEqual(reason, "performed"); assert.strictEqual(await search.defaultEngine.testGetValue(), 5); validatorRun = true; }); // Submit the ping. pings.search.submit("performed"); // Wait for the validation to finish. assert.doesNotThrow(async () => await p); // Verify that the validator run. assert.ok(validatorRun);  JavaScript: Glean.search.defaultEngine.add(5); let submitted = false; GleanPings.search.testBeforeNextSubmit(reason => { submitted = true; Assert.equal(5, Glean.search.defaultEngine.testGetValue()); }); GleanPings.search.submit(); Assert.ok(submitted);  C++: mozilla::glean::search::default_engine.Add(5); bool submitted = false; mozilla::glean_pings::Search.TestBeforeNextSubmit([&submitted](const nsACString& aReason) { submitted = true; ASSERT_EQ(false, mozilla::glean::search::default_engine.TestGetValue().unwrap().ref()); }); mozilla::glean_pings::Search.Submit(); ASSERT_TRUE(submitted);  Overview The Glean SDK provides multiple language bindings for integration into different platforms and with different programming languages Rust The Rust bindings can be used with any Rust application. It can serve a wide variety of usage patterns, such as short-lived CLI applications as well as longer-running desktop or server applications. It can optionally send builtin pings at startup. It does not assume an interaction model and the integrating application is responsible to connect the respective hooks. It is available as the glean crate on crates.io. Kotlin The Kotlin bindings are primarily used for integration with Android applications. It assumes a common interaction model for mobile applications. It sends builtin pings at startup of the integrating application. It is available standalone as org.mozilla.telemetry:glean or via Android Components as org.mozilla.components:service-glean from the Mozilla Maven instance. Note: The Kotlin bindings can also be used from Java. See Android for more on integrating Glean on Android. Swift The Swift bindings are primarily used for integration with iOS applications. It assumes a common interaction model for mobile applications. It sends builtin pings at startup of the integrating application. It is available as a standalone Xcode framework from the Glean releases page or bundled with the AppServices framework. Python The Python bindings allow integration with any Python application. It can serve a wide variety of usage patterns, such as short-lived CLI applications as well as longer-running desktop or server applications. It is available as glean-sdk on PyPI. Android-specific information The Glean SDK can be used in Android applications. The integration into the build system for Android applications can be tuned to your needs. See the following chapters for details. Android build script configuration options This chapter describes build configuration options that control the behavior of the Glean SDK's Gradle plugin. These options are not usually required for normal use. Options can be turned on by setting a variable on the Gradle ext object before applying the Glean Gradle plugin. allowMetricsFromAAR Normally, the Glean SDK looks for metrics.yaml and pings.yaml files in the root directory of the Glean-using project. However, in some cases, these files may need to ship inside the dependencies of the project. For example, this is used in the engine-gecko component to grab the metrics.yaml from the geckoview AAR. ext.allowMetricsFromAAR = true  When this flag is set, every direct dependency of your library will be searched for a metrics.yaml file, and those metrics will be treated as the metrics as if they were defined by your library. That is, API wrappers accessible from your library will be generated for those metrics. The metrics.yaml can be added to the dependency itself by calling this on each relevant build variant: variant.packageLibraryProvider.get().from("${topsrcdir}/path/metrics.yaml")


gleanGenerateMarkdownDocs

The Glean SDK can automatically generate Markdown documentation for metrics and pings defined in the registry files, in addition to the metrics API code.

ext.gleanGenerateMarkdownDocs = true


Flipping the feature to true will generate a metrics.md file in $projectDir/docs at build-time. In general this is not necessary for projects using Mozilla's data ingestion infrastructure: in those cases human-readable documentation will automatically be viewable via the Glean Dictionary. gleanDocsDirectory The gleanDocsDirectory can be used to customize the path of the documentation output directory. If gleanGenerateMarkdownDocs is disabled, it does nothing. Please note that only the metrics.md will be overwritten: any other file available in the target directory will be preserved. ext.gleanDocsDirectory = "$rootDir/docs/user/telemetry"


gleanYamlFiles

By default, the Glean Gradle plugin will look for metrics.yaml and pings.yaml files in the same directory that the plugin is included from in your application or library. To override this, ext.gleanYamlFiles may be set to a list of explicit paths.

ext.gleanYamlFiles = ["$rootDir/glean-core/metrics.yaml", "$rootDir/glean-core/pings.yaml"]


Offline builds of Android applications that use Glean

The Glean SDK has basic support for building Android applications that use Glean in offline mode.

The Glean SDK uses a Python script, glean_parser to generate code for metrics from the metrics.yaml and pings.yaml files when Glean-using applications are built. When online, the pieces necessary to run this script are installed automatically.

For offline builds, the Python environment, and packages of glean_parser and its dependencies must be provided prior to building the Glean-using application.

To build a Glean-using application in offline mode, do the following:

• Install Python 3.6 or later and ensure it's on the PATH.

• On Linux, installing Python from your Linux distribution's package manager is usually sufficient.

• On macOS, installing Python from homebrew is known to work, but other package managers may also work.

• On Windows, we recommend installing one of the official Python installers from python.org.

• Determine the version of glean_parser required.

• It can be really difficult to manually determine the version of glean_parser that is required for a given application, since it needs to be tracked through android-components, to glean-core and finally to glean_parser. The required version of glean_parser can be determined by running the following at the top-level of the Glean-using application:

$./gradlew | grep "Requires glean_parser" Requires glean_parser==1.28.1  • Download packages for glean_parser and its dependencies: • In the root directory of the Glean-using project, create a directory called glean-wheels and cd into it. • Download packages for glean_parser and its dependencies, replacing X.Y.Z with the correct version of glean_parser: $ python3 -m pip download glean_parser==X.Y.Z

• Build the Glean-using project using ./gradlew, but passing in the --offline flag.

There are a couple of environment variables that control offline building:

• To override the location of the Python interpreter to use, set the GLEAN_PYTHON environment variable. If unset, the first Python interpreter on the PATH will be used.

• To override the location of the downloaded Python wheels, set the GLEAN_PYTHON_WHEELS_DIR environment variable. If unset ${projectDir}/glean-wheels will be used. Instrumenting Android crashes with the Glean SDK One of the things that might be useful to collect data on in an Android application is crashes. This guide will walk through a basic strategy for instrumenting an Android application with crash telemetry using a custom ping. Note: This is a very simple example of instrumenting crashes using the Glean SDK. There will be challenges to using this approach in a production application that should be considered. For instance, when an app crashes it can be in an unknown state and may not be able to do things like upload data to a server. The recommended way of instrumenting crashes with Android Components is called lib-crash, which takes into consideration things like multiple processes and persistence. Before You Start There are a few things that need to be installed in order to proceed, mainly Android Studio. If you include the Android SDK, Android Studio can take a little while to download and get installed. This walk-through assumes some knowledge of Android application development. Knowing where to go to create a new project and how to add dependencies to a Gradle file will be helpful in following this guide. Setup Build Configuration Please follow the instruction in the "Adding Glean to your project" chapter in order to set up Glean in an Android project. Add A Custom Metric Since crashes will be instrumented with some custom metrics, the next step will be to add a metrics.yaml file to define the metrics used to record the crash information and a pings.yaml file to define a custom ping which will give some control over the scheduling of the uploading. See "Adding new metrics" for more information about adding metrics. What metric type should be used to represent the crash data? While this could be implemented several ways, an event is an excellent choice, simply because events capture information in a nice concise way and they have a built-in way of passing additional information using the extras field. If it is necessary to pass along the cause of the exception or a few lines of description, events let us do that easily (with some limitations). Now that a metric type has been chosen to represent the metric, the next step is creating the metrics.yaml. Inside of the root application folder of the Android Studio project create a new file named metrics.yaml. After adding the schema definition and event metric definition, the metrics.yaml should look like this: # Required to indicate this is a metrics.yaml file$schema: moz://mozilla.org/schemas/glean/metrics/2-0-0

crash:
exception:
type: event
description: |
Event to record crashes caused by unhandled exceptions
notification_emails:
- crashes@example.com
bugs:
- https://bugzilla.mozilla.org/show_bug.cgi?id=1582479
data_reviews:
- https://bugzilla.mozilla.org/show_bug.cgi?id=1582479
expires:
2021-01-01
send_in_pings:
- crash
extra_keys:
cause:
description: The cause of the crash
message:
description: The exception message


As a brief explanation, this creates a metric called exception within a metric category called crash. There is a text description and the required notification_emails, bugs, data_reviews, and expires fields. The send_in_pings field is important to note here that it has a value of - crash. This means that the crash event metric will be sent via a custom ping named crash (which hasn't been created yet). Finally, note the extra_keys field which has two keys defined, cause and message. This allows for sending additional information along with the event to be associated with these keys.

Note: For Mozilla applications, a mandatory data review is required in order to collect information with the Glean SDK.

Add A Custom Ping

Define the custom ping that will help control the upload scheduling by creating a pings.yaml file in the same directory as the metrics.yaml file. For more information about adding custom pings, see the section on custom pings.
The name of the ping will be crash, so the pings.yaml file should look like this:

# Required to indicate this is a pings.yaml file

v40.2.0 (2021-09-08)

Full changelog

• General
• Updated glean_parser version to 4.0.0
• Android
• Add support for the URL metric type (#1778)
• Rust
• Add support for the URL metric type (#1778)
• Python
• Add support for the URL metric type (#1778)

v40.1.1 (2021-09-02)

Full changelog

• iOS
• Use 'Unknown' value if system data can't be decoded as UTF-8 (#1769)
• BUGFIX: Add quantity metric type to the build (#1774). Previous builds are unable to use quantity metrics

v40.1.0 (2021-08-25)

Full changelog

• Android
• Updated to Kotlin 1.5, Android Gradle Plugin 4.2.2 and Gradle 6.7.1 (#1747)
• The glean-gradle-plugin now forces a compile failure when multiple Glean versions are detected in the build (#1756)
• The glean-gradle-plugin does not enable a glean-native capability on GeckoView anymore. That will be done by GeckoView directly (#1759)

v40.0.0 (2021-07-28)

Full changelog

• Android

• Breaking Change: Split the Glean Kotlin SDK into two packages: glean and glean-native (#1595). Consumers will need to switch to org.mozilla.telemetry:glean-native-forUnitTests. Old code in build.gradle:

testImplementation "org.mozilla.telemetry:glean-forUnitTests:${project.ext.glean_version}"  New code in build.gradle: testImplementation "org.mozilla.telemetry:glean-native-forUnitTests:${project.ext.glean_version}"

• The glean-gradle-plugin now automatically excludes the glean-native dependency if geckoview-omni is also part of the build. Glean native functionality will be provided by the geckoview-omni package.

• Rust

• The glean-ffi is no longer compiled as a cdylib. Other language SDKs consume glean-bundle instead as a cdylib. This doesn't affect consumers.

v39.1.0 (2021-07-26)

Full changelog

• General
• Updated glean_parser version to 3.6.0
• Allow Custom Distribution metric type on all platforms (#1679)

v39.0.4 (2021-07-26)

Full changelog

• General
• Extend invalid_timezone_offset metric until the end of the year (#1697)

v39.0.3 (2021-06-09)

Full changelog

• Android
• Unbreak Event#record API by accepting null on the deprecated API. The previous 39.0.0 release introduced the new API, but accidentally broke certain callers that just forward arguments. This restores passing null (or nothing) when using the old API. It remains deprecated.

Full changelog

v39.0.1 (2021-06-04)

Full changelog

• iOS
• Build and release Glean as an xcframework (#1663) This will now also auto-update the Glean package at https://github.com/mozilla/glean-swift.

v39.0.0 (2021-05-31)

Full changelog

• General
• Add new event extras API to all implementations. See below for details (#1603)
• Updated glean_parser version to 3.4.0 (#1603)
• Rust
• Breaking Change: Allow event extras to be passed as an object. This replaces the old HashMap-based API. Values default to string. See the event documentation for details. (#1603) Old code:

let mut extra = HashMap::new();
extra.insert(SomeExtra::Key1, "1".into());
extra.insert(SomeExtra::Key2, "2".into());
metric.record(extra);


New code:

let extra = SomeExtra {
key1: Some("1".into()),
key2: Some("2".into()),
};
metric.record(extra);

• Android
• Deprecation: The old event recording API is replaced by a new one, accepting a typed object (#1603). See the event documentation for details.
• Skip build info generation for libraries (#1654)
• Python
• Deprecation: The old event recording API is replaced by a new one, accepting a typed object (#1603). See the event documentation for details.
• Swift
• Deprecation: The old event recording API is replaced by a new one, accepting a typed object (#1603). See the event documentation for details.

v38.0.1 (2021-05-17)

Full changelog

• General
• BUGFIX: Invert the lock order in glean-core's metrics ping scheduler (#1637)

v38.0.0 (2021-05-12)

Full changelog

• General
• Update documentation to recommend using Glean Dictionary instead of metrics.md (#1604)
• Rust
• Breaking Change: Don't return a result from submit_ping. The boolean return value indicates whether a ping was submitted (#1613)
• Breaking Change: Glean now schedules "metrics" pings, accepting a new Configuration parameter. (#1599)
• Dispatch setting the source tag to avoid a potential crash (#1614)
• Testing mode will wait for init & upload tasks to finish (#1628)
• Android
• Set required fields for client_info before optional ones (#1633)
• Provide forward-compatibility with Gradle 6.8 (#1616)

v37.0.0 (2021-04-30)

Full changelog

• General
• Breaking Change: "deletion-request" pings now include the reason upload was disabled: at_init (Glean detected a change between runs) or set_upload_enabled (Glean was told of a change as it happened). (#1593).
• Attempt to upload a ping even in the face of IO Errors (#1576).
• Implement an additional check to avoid crash due to faulty timezone offset (#1581)
• This now records a new metric glean.time.invalid_timezone_offset, counting how often we failed to get a valid timezone offset.
• Use proper paths throughout to hopefully handle non-UTF-8 paths more gracefully (#1596)
• Updated glean_parser version to 3.2.0 (#1609)
• iOS
• Code generator: Ensure at least pip 20.3 is available in iOS build (#1590)

v36.0.1 (2021-04-09)

Full changelog

• RLB
• Provide an internal-use-only API to pass in raw samples for timing distributions (#1561).
• Expose Timespan's set_raw to Rust (#1578).
• Android
• BUGFIX: TimespanMetricType.measure and TimingDistributionMetricType.measure won't get inlined anymore (#1560). This avoids a potential bug where a return used inside the closure would end up not measuring the time. Use return@measure <val> for early returns.
• Python
• The Glean Python bindings now use rkv's safe mode backend. This should avoid intermittent segfaults in the LMDB backend.

v36.0.0 (2021-03-16)

Full changelog

• General
• Introduce a new API Ping#test_before_next_submit to run a callback right before a custom ping is submitted (#1507).
• The new API exists for all language bindings (Kotlin, Swift, Rust, Python).
• Updated glean_parser version to 2.5.0
• Change the fmt- and lint- make commands for consistency (#1526)
• The Glean SDK can now produce testing coverage reports for your metrics (#1482).
• Python
• Update minimal required version of cffi dependency to 1.13.0 (#1520).
• Ship wheels for arm64 macOS (#1534).
• RLB
• Added rate metric type (#1516).
• Set internal_metrics::os_version for MacOS, Windows and Linux (#1538)
• Expose a function get_timestamp_ms to get a timestamp from a monotonic clock on all supported operating systems, to be used for event timestamps (#1546).
• Expose a function to record events with an externally provided timestamp.
• iOS
• Breaking Change: Event timestamps are now correctly recorded in milliseconds (#1546).
• Since the first release event timestamps were erroneously recorded with nanosecond precision (#1549). This is now fixed and event timestamps are in milliseconds. This is equivalent to how it works in all other language bindings.

v35.0.0 (2021-02-22)

Full changelog

• Android
• Glean.initialize can now take a buildInfo parameter to pass in build time version information, and avoid calling out to the Android package manager at runtime. A suitable instance is generated by glean_parser in \${PACKAGE_ROOT}.GleanMetrics.GleanBuildInfo.buildInfo (#1495). Not passing in a buildInfo object is still supported, but is deprecated.
• The testGetValue APIs now include a message on the NullPointerException thrown when the value is missing.
• Breaking change: LEGACY_TAG_PINGS is removed from GleanDebugActivity (#1510)
• RLB
• Breaking change: Configuration.data_path is now a std::path::PathBuf(#1493).

v34.1.0 (2021-02-04)

Full changelog

• General
• A new metric glean.validation.pings_submitted tracks the number of pings sent. It is included in both the metrics and baseline pings.
• iOS
• The metric glean.validation.foreground_count is now sent in the metrics ping (#1472).
• BUGFIX: baseline pings with reason dirty_startup are no longer sent if Glean did not full initialize in the previous run (#1476).
• Python
• Expose the client activity API (#1481).
• BUGFIX: Publish a macOS wheel again. The previous release failed to build a Python wheel for macOS platforms (#1471).
• RLB
• BUGFIX: baseline pings with reason dirty_startup are no longer sent if Glean did shutdown cleanly (#1483).

v34.0.0 (2021-01-29)

Full changelog

• General
• Other bindings detect when RLB is used and try to flush the RLB dispatcher to unblock the Rust API (#1442).
• This is detected automatically, no changes needed for consuming code.
• Add support for the client activity API (#1455). This API is either automatically used or exposed by the language bindings.
• Rename the reason background to inactive for both the baseline and events ping. Rename the reason foreground to active for the baseline ping.
• RLB
• When the pre-init task queue overruns, this is now recorded in the metric glean.error.preinit_tasks_overflow (#1438).
• Expose the client activity API (#1455).
• Send the baseline ping with reason dirty_startup, if needed, at startup.
• Expose all required types directly (#1452).
• Rust consumers will not need to depend on glean-core anymore.
• Android
• BUGFIX: Don't crash the ping uploader when throttled due to reading too large wait time values (#1454).
• Use the client activity API (#1455).
• Update AndroidX dependencies (#1441).
• iOS
• Use the client activity API (#1465). Note: this now introduces a baseline ping with reason active on startup.

v33.10.3 (2021-01-18)

Full changelog

• Rust
• Upgrade rkv to 0.17 (#1434)

v33.10.2 (2021-01-15)

Full changelog

• General:
• A new metric glean.error.io has been added, counting the times an IO error happens when writing a pending ping to disk (#1428)
• Android
• A new metric glean.validation.foreground_count was added to the metrics ping (#1418).
• Rust
• BUGFIX: Fix lock order inversion in RLB Timing Distribution (#1431).
• Use RLB types instead of glean-core ones for RLB core metrics. (#1432).

v33.10.1 (2021-01-06)

Full changelog

No functional changes. v33.10.0 failed to generated iOS artifacts due to broken tests (#1421).

v33.10.0 (2021-01-06)

Full changelog

• General
• A new metric glean.validation.first_run_hour, analogous to the existing first_run_date but with hour resolution, has been added. Only clients running the app for the first time after this change will report this metric (#1403).
• Rust
• BUGFIX: Don't require mutable references in RLB traits (#1417).
• Python
• Building the Python package from source now works on musl-based Linux distributions, such as Alpine Linux (#1416).

v33.9.1 (2020-12-17)

Full changelog

• Rust
• BUGFIX: Don't panic on shutdown and avoid running tasks if uninitialized (#1398).
• BUGFIX: Don't fail on empty database files (#1398).
• BUGFIX: Support ping registration before Glean initializes (#1393).

v33.9.0 (2020-12-15)

Full changelog

• Rust
• Introduce the String List metric type in the RLB. (#1380).
• Introduce the Datetime metric type in the RLB (#1384).
• Introduce the CustomDistribution and TimingDistribution metric type in the RLB (#1394).

v33.8.0 (2020-12-10)

Full changelog

• Rust
• Introduce the Memory Distribution metric type in the RLB. (#1376).
• Shut down Glean in tests before resetting to make sure they don't mistakenly init Glean twice in parallel (#1375).
• BUGFIX: Fixing 2 lock-order-inversion bugs found by TSan (#1378).
• TSan runs on mozilla-central tests, which found two (potential) bugs where 2 different locks were acquired in opposite order in different code paths, which could lead to deadlocks in multi-threaded code. As RLB uses multiple threads (e.g. for init and the dispatcher) by default, this can easily become an actual issue.
• Python
• All log messages from the Glean SDK are now on the glean logger, obtainable through logging.getLogger("glean"). (Prior to this, each module had its own logger, for example glean.net.ping_upload_worker).

v33.7.0 (2020-12-07)

Full changelog

• Rust
• Upgrade rkv to 0.16.0 (no functional changes) (#1355).
• Introduce the Event metric type in the RLB (#1361).
• Python
• Python Linux wheels no longer work on Linux distributions released before 2014 (they now use the manylinux2014 ABI) (#1353).
• Unbreak Python on non-Linux ELF platforms (BSD, Solaris/illumos) (#1363).

v33.6.0 (2020-12-02)

Full changelog

• Rust
• BUGFIX: Negative timespans for the timespan metric now correctly record an InvalidValue error (#1347).
• Introduce the Timespan metric type in the RLB (#1347).
• Python
• BUGFIX: Network slowness or errors will no longer block the main dispatcher thread, leaving work undone on shutdown (#1350).
• BUGFIX: Lower sleep time on upload waits to avoid being stuck when the main process ends (#1349).

v33.5.0 (2020-12-01)

Full changelog

• Rust
• Introduce the UUID metric type in the RLB.
• Introduce the Labeled metric type in the RLB (#1327).
• Introduce the Quantity metric type in the RLB.
• Introduce the shutdown API.
• Add Glean debugging APIs.
• Python
• BUGFIX: Setting a UUID metric to a value that is not in the expected UUID format will now record an error with the Glean error reporting system.

v33.4.0 (2020-11-17)

Full changelog

• General
• When Rkv's safe mode is enabled (features = ["rkv-safe-mode"] on the glean-core crate) LMDB data is migrated at first start (#1322).
• Rust
• Introduce the Counter metric type in the RLB.
• Introduce the String metric type in the RLB.
• BUGFIX: Track the size of the database directory at startup (#1304).
• Python
• BUGFIX: Fix too-long sleep time in uploader due to unit mismatch (#1325).
• Swift
• BUGFIX: Fix too-long sleep time in uploader due to unit mismatch (#1325).

v33.3.0 (2020-11-12)

Full changelog

• General
• Do not require default-features on rkv and downgrade bincode (#1317)
• Rust
• Implement the experiments API (#1314)

v33.2.0 (2020-11-10)

Full changelog

• Python
• Fix building of Linux wheels (#1303)
• Python Linux wheels no longer work on Linux distributions released before 2010. (They now use the manylinux2010 ABI, rather than the manylinux1 ABI.)
• Rust
• Introduce the RLB net module (#1292)

v33.1.2 (2020-11-04)

Full changelog

• No changes. v33.1.1 was tagged incorrectly.

v33.1.1 (2020-11-04)

Full changelog

• No changes. v33.1.0 was tagged incorrectly.

v33.1.0 (2020-11-04)

Full changelog

• General
• Standardize throttle backoff time throughout all bindings. (#1240)
• Update glean_parser to 1.29.0
• Generated code now includes a comment next to each metric containing the name of the metric in its original snake_case form.
• Expose the description of the metric types in glean_core using traits.
• Rust
• Add the BooleanMetric type.
• Add the dispatcher module (copied over from mozilla-central).
• Allow consumers to specify a custom uploader.
• Android
• Update the JNA dependency from 5.2.0 to 5.6.0
• The glean-gradle-plugin now makes sure that only a single Miniconda installation will happen at the same time to avoid a race condition when multiple components within the same project are using Glean.

v33.0.4 (2020-09-28)

Full changelog

Note: Previous 33.0.z releases were broken. This release now includes all changes from 33.0.0 to 33.0.3.

• General
• Update glean_parser to 1.28.6
• BUGFIX: Ensure Kotlin arguments are deterministically ordered
• Android
• Breaking change: Updated to the Android Gradle Plugin v4.0.1 and Gradle 6.5.1. Projects using older versions of these components will need to update in order to use newer versions of the Glean SDK.
• Update the Kotlin Gradle Plugin to version 1.4.10.
• Fixed the building of .aar releases on Android so they include the Rust shared objects.

v33.0.3 (2020-09-25)

Full changelog

• General
• v33.0.2 was tagged incorrectly. This release is just to correct that mistake.

v33.0.2 (2020-09-25)

Full changelog

• Android
• Fixed the building of .aar releases on Android so they include the Rust shared objects.

v33.0.1 (2020-09-24)

Full changelog

• General
• Update glean_parser to 1.28.6
• BUGFIX: Ensure Kotlin arguments are deterministically ordered
• Android
• Update the Kotlin Gradle Plugin to version 1.4.10.

v33.0.0 (2020-09-22)

Full changelog

• Android
• Breaking change: Updated to the Android Gradle Plugin v4.0.1 and Gradle 6.5.1. Projects using older versions of these components will need to update in order to use newer versions of the Glean SDK.

v32.4.1 (2020-10-01)

Full changelog

• General
• Update glean_parser to 1.28.6
• BUGFIX: Ensure Kotlin arguments are deterministically ordered
• BUGFIX: Transform ping directory size from bytes to kilobytes before accumulating to glean.upload.pending_pings_directory_size (#1236).

v32.4.0 (2020-09-18)

Full changelog

• General
• Allow using quantity metric type outside of Gecko (#1198)
• Update glean_parser to 1.28.5
• The SUPERFLUOUS_NO_LINT warning has been removed from the glinter. It likely did more harm than good, and makes it hard to make metrics.yaml files that pass across different versions of glean_parser.
• Expired metrics will now produce a linter warning, EXPIRED_METRIC.
• Expiry dates that are more than 730 days (~2 years) in the future will produce a linter warning, EXPIRATION_DATE_TOO_FAR.
• Allow using the Quantity metric type outside of Gecko.
• New parser configs custom_is_expired and custom_validate_expires added. These are both functions that take the expires value of the metric and return a bool. (See Metric.is_expired and Metric.validate_expires). These will allow FOG to provide custom validation for its version-based expires values.
• Add a limit of 250 pending ping files. (#1217).
• Android
• Don't retry the ping uploader when waiting, sleep instead. This avoids a never-ending increase of the backoff time (#1217).

v32.3.2 (2020-09-11)

Full changelog

• General
• Track the size of the database file at startup (#1141).
• Submitting a ping with upload disabled no longer shows an error message (#1201).
• BUGFIX: scan the pending pings directories after dealing with upload status on initialization. This is important, because in case upload is disabled we delete any outstanding non-deletion ping file, and if we scan the pending pings folder before doing that we may end up sending pings that should have been discarded. (#1205)
• iOS
• Disabled code coverage in release builds (#1195).
• Python
• Glean now ships a source package to pip install on platforms where wheels aren't provided.

v32.3.1 (2020-09-09)

Full changelog

• Python
• Fixed the release process to generate all wheels (#1193).

v32.3.0 (2020-08-27)

Full changelog

• Android
• Handle ping registration off the main thread. This removes a potential blocking call (#1132).
• iOS
• Handle ping registration off the main thread. This removes a potential blocking call (#1132).
• Glean for iOS is now being built with Xcode 12.0.0 (Beta 5) (#1170).

v32.2.0 (2020-08-25)

Full changelog

• General
• Move logic to limit the number of retries on ping uploading "recoverable failures" to glean-core. (#1120)
• The functionality to limit the number of retries in these cases was introduced to the Glean SDK in v31.1.0. The work done now was to move that logic to the glean-core in order to avoid code duplication throughout the language bindings.
• Update glean_parser to v1.28.3
• BUGFIX: Generate valid C# code when using Labeled metric types.
• BUGFIX: Support HashSet and Dictionary in the C# generated code.
• Add a 10MB quota to the pending pings storage. (#1100)
• C#
• Add support for the String List metric type (#1108).
• Enable generating the C# APIs using the glean_parser (#1092).
• Add support for the EventMetricType in C# (#1129).
• Add support for the TimingDistributionMetricType in C# (#1131).
• Implement the experiments API in C# (#1145).
• This is the last release with C# language bindings changes. Reach out to the Glean SDK team if you want to use the C# bindings in a new product and require additional features.
• Python
• BUGFIX: Limit the number of retries for 5xx server errors on ping uploads (#1120).
• This kinds of failures yield a "recoverable error", which means the ping gets re-enqueued. That can cause infinite loops on the ping upload worker. For python we were incorrectly only limiting the number of retries for I/O errors, another type of "recoverable error".
• kebab-case ping names are now converted to snake_case so they are available on the object returned by load_pings (#1122).
• For performance reasons, the glinter is no longer run as part of glean.load_metrics(). We recommend running glinter as part of your project's continuous integration instead (#1124).
• A measure context manager for conveniently measuring runtimes has been added to TimespanMetricType and TimingDistributionMetricType (#1126).
• Networking errors have changed from ERROR level to DEBUG level so they aren't displayed by default (#1166).
• iOS

Full changelog

v32.1.0 (2020-08-17)

Full changelog

• General
• The upload rate limiter has been changed from 10 pings per minute to 15 pings per minute.

v32.0.0 (2020-08-03)

Full changelog

• General
• Limit ping request body size to 1MB. (#1098)
• iOS
• Implement ping tagging (i.e. the X-Source-Tags header) through custom URL (#1100).
• C#
• Add support for Labeled Strings and Labeled Booleans.
• Add support for the Counter metric type and Labeled Counter.
• Add support for the MemoryDistributionMetricType.
• Python
• Breaking change: data_dir must always be passed to Glean.initialize. Prior to this, a missing value would store Glean data in a temporary directory.
• Logging messages from the Rust core are now sent through Python's standard library logging module. Therefore all logging in a Python application can be controlled through the logging module interface.
• Android
• BUGFIX: Require activities executed via GleanDebugView to be exported.

v31.6.0 (2020-07-24)

Full changelog

• General
• Implement JWE metric type (#1073, #1062).
• DEPRECATION: getUploadEnabled is deprecated (respectively get_upload_enabled in Python) (#1046)
• Due to Glean's asynchronous initialization the return value can be incorrect. Applications should not rely on Glean's internal state. Upload enabled status should be tracked by the application and communicated to Glean if it changes. Note: The method was removed from the C# and Python implementation.
• Update glean_parser to v1.28.1
• The glean_parser linting was leading consumers astray by incorrectly suggesting that deletion-request be instead deletion_request when used for send_in_pings. This was causing metrics intended for the deletion-request ping to not be included when it was collected and submitted. Consumers that are sending metrics in the deletion-request ping will need to update the send_in_pings value in their metrics.yaml to correct this.
• Fixes a bug in doc rendering.

v31.5.0 (2020-07-22)

Full changelog

• General
• Implement ping tagging (i.e. the X-Source-Tags header) (#1074). Note that this is not yet implemented for iOS.
• String values that are too long now record invalid_overflow rather than invalid_value through the Glean error reporting mechanism. This affects the string, event and string list metrics.
• metrics.yaml files now support a data_sensitivity field to all metrics for specifying the type of data collected in the field.
• Python
• The Python unit tests no longer send telemetry to the production telemetry endpoint.
• BUGFIX: If an application_version isn't provided to Glean.initialize, the client_info.app_display_version metric is set to "Unknown", rather than resulting in invalid pings.
• Android
• Allow defining which Activity to run next when using the GleanDebugActivity.
• iOS
• BUGFIX: The memory unit is now correctly set on the MemoryDistribution metric type in Swift in generated metrics code.
• C#
• Metrics can now be generated from the metrics.yaml files.

v31.4.1 (2020-07-20)

Full changelog

• General
• BUGFIX: fix int32 to ErrorType mapping. The InvalidOverflow had a value mismatch between glean-core and the bindings. This would only be a problem in unit tests. (#1063)
• Android
• Enable propagating options to the main product Activity when using the GleanDebugActivity.
• BUGFIX: Fix the metrics ping collection for startup pings such as reason=upgrade to occur in the same thread/task as Glean initialize. Otherwise, it gets collected after the application lifetime metrics are cleared such as experiments that should be in the ping. (#1069)

v31.4.0 (2020-07-16)

Full changelog

• General
• Enable debugging features through environment variables. (#1058)

v31.3.0 (2020-07-10)

Full changelog

• General
• Remove locale from baseline ping. (1609968, #1016)
• Persist X-Debug-ID header on store ping. (1605097, #1042)
• BUGFIX: raise an error if Glean is initialized with an empty string as the application_id (#1043).
• Python
• BUGFIX: correctly set the app_build metric to the newly provided application_build_id initialization option (#1031).
• The Python bindings now report networking errors in the glean.upload.ping_upload_failure metric (like all the other bindings) (#1039).
• Python default upgraded to Python 3.8 (#995)
• iOS
• BUGFIX: Make LabeledMetric subscript public, so consuming applications can actually access it (#1027)

v31.2.3 (2020-06-29)

Full changelog

• General
• Move debug view tag management to the Rust core. (1640575, #998)
• BUGFIX: Fix mismatch in events keys and values by using glean_parser version 1.23.0.

v31.2.2 (2020-06-26)

Full changelog

• Android
• BUGFIX: Compile dependencies with NDEBUG to avoid linking unavailable symbols. This fixes a crash due to a missing stderr symbol on older Android (#1020)

v31.2.1 (2020-06-25)

Full changelog

• Python
• BUGFIX: Core metrics are now present in every ping, even if submit is called before initialize has a chance to complete. (#1012)

v31.2.0 (2020-06-24)

Full changelog

• General
• Add rate limiting capabilities to the upload manager. (1543612, #974)
• Android
• BUGFIX: baseline pings with reason "dirty startup" are no longer sent if Glean did not full initialize in the previous run (#996).
• Python
• Support for Python 3.5 was dropped (#987).
• Python wheels are now shipped with Glean release builds, resulting in much smaller libraries (#1002)
• The Python bindings now use locale.getdefaultlocale() rather than locale.getlocale() to determine the locale (#1004).

v31.1.2 (2020-06-23)

Full changelog

• General
• BUGFIX: Correctly format the date and time in the Date header (#993).
• Python
• BUGFIX: Additional time is taken at shutdown to make sure pings are sent and telemetry is recorded. (1646173, #983)
• BUGFIX: Glean will run on the main thread when running in a multiprocessing subprocess (#986).

v31.1.1 (2020-06-12)

Full changelog

• Android
• Dropping the version requirement for lifecycle extensions down again. Upping the required version caused problems in A-C.

v31.1.0 (2020-06-11)

Full changelog

• General:
• The regex crate is no longer required, making the Glean binary smaller (#949)
• Record upload failures into a new metric (#967)
• Log FFI errors as actual errors (#935)
• Limit the number of upload retries in all implementations (#953, #968)
• Python
• Additional safety guarantees for applications that use Python threading (#962)

v31.0.2 (2020-05-29)

Full changelog

• Rust
• Fix list of included files in published crates

v31.0.1 (2020-05-29)

Full changelog

• Rust
• Relax version requirement for flate2 for compatibility reasons

v31.0.0 (2020-05-28)

Full changelog

• General:

• The version of glean_parser has been upgraded to v1.22.0
• A maximum of 10 extra_keys is now enforced for event metric types.
• Breaking change: (Swift only) Combine all metrics and pings into a single generated file Metrics.swift.
• For Swift users this requires to change the list of output files for the sdk_generator.sh script. It now only needs to include the single file Generated/Metrics.swift.
• Python:

• BUGFIX: lifetime: application metrics are no longer recorded as lifetime: user.
• BUGFIX: glean-core is no longer crashing when calling uuid.set with invalid UUIDs.
• Refactor the ping uploader to use the new upload mechanism and add gzip compression.
• Most of the work in Glean.initialize happens on a worker thread and no longer blocks the main thread.
• Rust:

• Expose Datetime types to Rust consumers.

v30.1.0 (2020-05-22)

Full changelog

• Android & iOS
• Ping payloads are now compressed using gzip.
• iOS
• Glean.initialize is now a no-op if called from an embedded extension. This means that Glean will only run in the base application process in order to prevent extensions from behaving like separate applications with different client ids from the base application. Applications are responsible for ensuring that extension metrics are only collected within the base application.
• Python
• lifetime: application metrics are now cleared after the Glean-owned pings are sent, after the product starts.
• Glean Python bindings now build in a native Windows environment.
• BUGFIX: MemoryDistributionMetric now parses correctly in metrics.yaml files.
• BUGFIX: Glean will no longer crash if run as part of another library's coverage testing.

v30.0.0 (2020-05-13)

Full changelog

• General:
• We completely replaced how the upload mechanism works. glean-core (the Rust part) now controls all upload and coordinates the platform side with its own internals. All language bindings implement ping uploading around a common API and protocol. There is no change for users of Glean, the language bindings for Android and iOS have been adopted to the new mechanism already.
• Expose RecordedEvent and DistributionData types to Rust consumers (#876)
• Log crate version at initialize (#873)
• Android:
• Refactor the ping uploader to use the new upload mechanism.
• iOS:
• Refactor the ping uploader to use the new upload mechanism.

v29.1.2 (2021-01-26)

Full changelog

This is an iOS release only, built with Xcode 11.7

Otherwise no functional changes.

• iOS
• Build with Xcode 11.7 (#1457)

v29.1.1 (2020-05-22)

Full changelog

• Android
• BUGFIX: Fix a race condition that leads to a ConcurrentModificationException. Bug 1635865

v29.1.0 (2020-05-11)

Full changelog

• General:
• The version of glean_parser has been upgraded to v1.20.4
• BUGFIX: yamllint errors are now reported using the correct file name.
• The minimum and maximum values of a timing distribution can now be controlled by the time_unit parameter. See bug 1630997 for more details.

v29.0.0 (2020-05-05)

Full changelog

• General:
• The version of glean_parser has been upgraded to v1.20.2 (#827):
• Breaking change: glinter errors found during code generation will now return an error code.
• glean_parser now produces a linter warning when user lifetime metrics are set to expire. See bug 1604854 for additional context.
• Android:
• The PingType.submit() can now be called without a null by Java consumers (#853).
• Python:
• BUGFIX: Fixed a race condition in the atexit handler, that would have resulted in the message "No database found" (#854).
• The Glean FFI header is now parsed at build time rather than runtime. Relevant for packaging in PyInstaller, the wheel no longer includes glean.h and adds _glean_ffi.py (#852).
• The minimum versions of many secondary dependencies have been lowered to make the Glean SDK compatible with more environments.
• Dependencies that depend on the version of Python being used are now specified using the Declaring platform specific dependencies syntax in setuptools. This means that more recent versions of dependencies are likely to be installed on Python 3.6 and later, and unnecessary backport libraries won't be installed on more recent Python versions.
• iOS:
• Glean for iOS is now being built with Xcode 11.4.1 (#856)

v28.0.0 (2020-04-23)

Full changelog

• General:
• The baseline ping is now sent when the application goes to foreground, in addition to background and dirty-startup.
• Python:
• BUGFIX: The ping uploader will no longer display a trace back when the upload fails due to a failed DNS lookup, network outage, or related issues that prevent communication with the telemetry endpoint.
• The dependency on inflection has been removed.
• The Python bindings now use subprocess rather than multiprocessing to perform ping uploading in a separate process. This should be more compatible on all of the platforms Glean supports.

v27.1.0 (2020-04-09)

Full changelog

• General:
• BUGFIX: baseline pings sent at startup with the dirty_startup reason will now include application lifetime metrics (#810)
• iOS:
• Breaking change: Change Glean iOS to use Application Support directory #815. No migration code is included. This will reset collected data if integrated without migration. Please contact the Glean SDK team if this affects you.
• Python
• BUGFIX: Fixed a race condition between uploading pings and deleting the temporary directory on shutdown of the process.

v27.0.0 (2020-04-08)

Full changelog

• General
• Glean will now detect when the upload enabled flag changes outside of the application, for example due to a change in a config file. This means that if upload is disabled while the application wasn't running (e.g. between the runs of a Python command using the Glean SDK), the database is correctly cleared and a deletion request ping is sent. See #791.
• The events ping now includes a reason code: startup, background or max_capacity.
• iOS:
• BUGFIX: A bug where the metrics ping is sent immediately at startup on the last day of the month has been fixed.
• Glean for iOS is now being built with Xcode 11.4.0
• The measure convenience function on timing distributions and time spans will now cancel the timing if the measured function throws, then rethrow the exception (#808)
• Broken doc generation has been fixed (#805).
• Kotlin
• The measure convenience function on timing distributions and time spans will now cancel the timing if the measured function throws, then rethrow the exception (#808)
• Python:
• Glean will now wait at application exit for up to one second to let its worker thread complete.
• Ping uploading now happens in a separate child process by default. This can be disabled with the allow_multiprocessing configuration option.

v26.0.0 (2020-03-27)

Full changelog

• General:
• The version of glean_parser has been updated to 1.19.0:
• Breaking change: The regular expression used to validate labels is stricter and more correct.
• Add more information about pings to markdown documentation:
• State whether the ping includes client id;
• Add list of data review links;
• Add list of related bugs links.
• glean_parser now makes it easier to write external translation functions for different language targets.
• BUGFIX: glean_parser now works on 32-bit Windows.
• Android:
• gradlew clean will no longer remove the Miniconda installation in ~/.gradle/glean. Therefore clean can be used without reinstalling Miniconda afterward every time.
• Python:
• Breaking Change: The glean.util and glean.hardware modules, which were unintentionally public, have been made private.
• Most Glean work and I/O is now done on its own worker thread. This brings the parallelism Python in line with the other platforms.
• The timing distribution, memory distribution, string list, labeled boolean and labeled string metric types are now supported in Python (#762, #763, #765, #766)

v25.1.0 (2020-02-26)

Full changelog

• Python:
• The Boolean, Datetime and Timespan metric types are now supported in Python (#731, #732, #737)
• Make public, document and test the debugging features (#733)

v25.0.0 (2020-02-17)

Full changelog

• General:
• ping_type is not included in the ping_info any more (#653), the pipeline takes the value from the submission URL.
• The version of glean_parser has been upgraded to 1.18.2:
• Breaking Change (Java API) Have the metrics names in Java match the names in Kotlin. See Bug 1588060.
• The reasons a ping are sent are now included in the generated markdown documentation.
• Android:
• The Glean.initialize method runs mostly off the main thread (#672).
• Labels in labeled metrics now have a correct, and slightly stricter, regular expression. See label format for more information.
• iOS:
• The baseline ping will now include reason codes that indicate why it was submitted. If an unclean shutdown is detected (e.g. due to force-close), this ping will be sent at startup with reason: dirty_startup.
• Per Bug 1614785, the clearing of application lifetime metrics now occurs after the metrics ping is sent in order to preserve values meant to be included in the startup metrics ping.
• initialize() now performs most of its work in a background thread.
• Python:
• When the pre-init task queue overruns, this is now recorded in the metric glean.error.preinit_tasks_overflow.
• glinter warnings are printed to stderr when loading metrics.yaml and pings.yaml files.

v24.2.0 (2020-02-11)

Full changelog

• General:
• Add locale to client_info section.
• Deprecation Warning Since locale is now in the client_info section, the one in the baseline ping (glean.baseline.locale) is redundant and will be removed by the end of the quarter.
• Drop the Glean handle and move state into glean-core (#664)
• If an experiment includes no extra fields, it will no longer include {"extra": null} in the JSON payload.
• Support for ping reason codes was added.
• The metrics ping will now include reason codes that indicate why it was submitted.
• The version of glean_parser has been upgraded to 1.17.3
• Android:
• Collections performed before initialization (preinit tasks) are now dispatched off the main thread during initialization.
• The baseline ping will now include reason codes that indicate why it was submitted. If an unclean shutdown is detected (e.g. due to force-close), this ping will be sent at startup with reason: dirty_startup.
• iOS:
• Collections performed before initialization (preinit tasks) are now dispatched off the main thread and not awaited during initialization.
• Added recording of glean.error.preinit_tasks_overflow to report when the preinit task queue overruns, leading to data loss. See bug 1609734

v24.1.0 (2020-01-16)

Full changelog

• General:
• Stopping a non started measurement in a timing distribution will now be reported as an invalid_state error.
• Android:
• A new metric glean.error.preinit_tasks_overflow was added to report when the preinit task queue overruns, leading to data loss. See bug 1609482

v24.0.0 (2020-01-14)

Full changelog

• General:
• Breaking Change An enableUpload parameter has been added to the initialize() function. This removes the requirement to call setUploadEnabled() prior to calling the initialize() function.
• Android:
• The metrics ping scheduler will now only send metrics pings while the application is running. The application will no longer "wake up" at 4am using the Work Manager.
• The code for migrating data from Glean SDK before version 19 was removed.
• When using the GleanTestLocalServer rule in instrumented tests, pings are immediately flushed by the WorkManager and will reach the test endpoint as soon as possible.
• Python:
• The Python bindings now support Python 3.5 - 3.7.
• The Python bindings are now distributed as a wheel on Linux, macOS and Windows.

v23.0.1 (2020-01-08)

Full changelog

• Android:
• BUGFIX: The Glean Gradle plugin will now work if an app or library doesn't have a metrics.yaml or pings.yaml file.
• iOS:
• The released iOS binaries are now built with Xcode 11.3.

v23.0.0 (2020-01-07)

Full changelog

• Python bindings:
• Support for events and UUID metrics was added.
• Android:
• The Glean Gradle Plugin correctly triggers docs and API updates when registry files change, without requiring them to be deleted.
• parseISOTimeString has been made 4x faster. This had an impact on Glean migration and initialization.
• Metrics with lifetime: application are now cleared when the application is started, after startup Glean SDK pings are generated.
• All platforms:
• The public method PingType.send() (in all platforms) have been deprecated and renamed to PingType.submit().
• Rename deletion_request ping to deletion-request ping after glean_parser update

v22.1.0 (2019-12-17)

Full changelog

• Add InvalidOverflow error to TimingDistributions (#583)

v22.0.0 (2019-12-05)

Full changelog

• Add option to defer ping lifetime metric persistence (#530)
• Add a crate for the nice control API (#542)
• Pending deletion_request pings are resent on start (#545)

v21.3.0 (2019-12-03)

Full changelog

• Timers are reset when disabled. That avoids recording timespans across disabled/enabled toggling (#495).
• Add a new flag to pings: send_if_empty (#528)
• Upgrade glean_parser to v1.12.0
• Implement the deletion request ping in Glean (#526)

v21.2.0 (2019-11-21)

Full changelog

• All platforms

• The experiments API is no longer ignored before the Glean SDK initialized. Calls are recorded and played back once the Glean SDK is initialized.

• String list items were being truncated to 20, rather than 50, bytes when using .set() (rather than .add()). This has been corrected, but it may result in changes in the sent data if using string list items longer than 20 bytes.

v21.1.1 (2019-11-20)

Full changelog

• Android:

• Use the LifecycleEventObserver interface, rather than the DefaultLifecycleObserver interface, since the latter isn't compatible with old SDK targets.

v21.1.0 (2019-11-20)

Full changelog

• Android:

• Two new metrics were added to investigate sending of metrics and baseline pings. See bug 1597980 for more information.

• Glean's two lifecycle observers were refactored to avoid the use of reflection.

• All platforms:

• Timespans will now not record an error if stopping after setting upload enabled to false.

v21.0.0 (2019-11-18)

Full changelog

• Android:

• The GleanTimerId can now be accessed in Java and is no longer a typealias.

• Fixed a bug where the metrics ping was getting scheduled twice on startup.

• All platforms

• Bumped glean_parser to version 1.11.0.

v20.2.0 (2019-11-11)

Full changelog

• In earlier 20.x.x releases, the version of glean-ffi was incorrectly built against the wrong version of glean-core.

v20.1.0 (2019-11-11)

Full changelog

• The version of Glean is included in the Glean Gradle plugin.

• When constructing a ping, events are now sorted by their timestamp. In practice, it rarely happens that event timestamps are unsorted to begin with, but this guards against a potential race condition and incorrect usage of the lower-level API.

v20.0.0 (2019-11-11)

Full changelog

• Glean users should now use a Gradle plugin rather than a Gradle script. (#421) See integrating with the build system docs for more information.

• In Kotlin, metrics that can record errors now have a new testing method, testGetNumRecordedErrors. (#401)

v19.1.0 (2019-10-29)

Full changelog

• Fixed a crash calling start on a timing distribution metric before Glean is initialized. Timings are always measured, but only recorded when upload is enabled (#400)
• BUGFIX: When the Debug Activity is used to log pings, each ping is now logged only once (#407)
• New invalid state error, used in timespan recording (#230)
• Add an Android crash instrumentation walk-through (#399)
• Fix crashing bug by avoiding assert-printing in LMDB (#422)
• Upgrade dependencies, including rkv (#416)

v19.0.0 (2019-10-22)

Full changelog

First stable release of Glean in Rust (aka glean-core). This is a major milestone in using a cross-platform implementation of Glean on the Android platform.

• Fix round-tripping of timezone offsets in dates (#392)
• Handle dynamic labels in coroutine tasks (#394)

v0.0.1-TESTING6 (2019-10-18)

Full changelog

• Ignore dynamically stored labels if Glean is not initialized (#374)
• Make sure ProGuard doesn't remove Glean classes from the app (#380)
• Keep track of pings in all modes (#378)
• Add jnaTest dependencies to the forUnitTest JAR (#382)

v0.0.1-TESTING5 (2019-10-10)

Full changelog

• Upgrade to NDK r20 (#365)

v0.0.1-TESTING4 (2019-10-09)

Full changelog

• Take DST into account when converting a calendar into its items (#359)
• Include a macOS library in the forUnitTests builds (#358)
• Keep track of all registered pings in test mode (#363)

v0.0.1-TESTING3 (2019-10-08)

Full changelog

• Allow configuration of Glean through the GleanTestRule
• Bump glean_parser version to 1.9.2

v0.0.1-TESTING2 (2019-10-07)

Full changelog

• Include a Windows library in the forUnitTests builds

v0.0.1-TESTING1 (2019-10-02)

Full changelog

General

First testing release.

Unreleased changes

Full changelog

• #856: Implement the PlatformInfo module for the web platform.
• Out of os, os_version, architecture and locale, on the web platform, we can only retrive os and locale information. The other information will default to the known value Unknown for all pings coming from this platform.
• #856: Expose the @mozilla/glean/web entry point for using Glean.js in websites.

v0.23.0 (2021-10-12)

Full changelog

• #755: Only allow calling of test* functions in "test mode".
• Glean is put in "test mode" once the Glean.testResetGlean API called.
• #811: Apply various fixes to the Qt entry point file.
• Expose ErrorType. This is only useful for testing purposes;
• Fix version of QtQuick.LocalStorage plugin;
• Fix the way to access the lib from inside the shutdown method. Previous to this fix, it is not possible to use the shutdown method;
• Expose the Glean.testRestGlean API.
• #822: Fix API reference docs build step.
• #825: Accept architecture and osVersion as initialization parameters in Qt. In Qt these values are not easily available from the environment.

v0.22.0 (2021-10-06)

Full changelog

• #796: Support setting the app_channel metric.
• #799: Make sure Glean does not do anything else in case initialization errors.
• This may happen in case there is an error creating the databases. Mostly an issue on Qt/QML where we use a SQLite database which can throw errors on initialization.
• #799: Provide stack traces when logging errors.

v0.21.1 (2021-09-30)

Full changelog

• #780: Fix the publishing step for releases. The Qt-specific build should now publish correctly.

v0.21.0 (2021-09-30)

Full changelog

• #754: Change target ECMAScript target from 2015 to 2016 when building for Qt.

• #779: Add a number of workarounds for the Qt Javascript engine.

• #775: Disallow calling test only methods outside of test mode.

• NOTE: Test mode is set once the API Glean.testResetGlean is called.

v0.20.0 (2021-09-17)

Full changelog

• #696: Expose Node.js entry point @mozilla/glean/node.
• #695: Implement PlatformInfo module for the Node.js platform.
• #695: Implement Uploader module for the Node.js platform.

v0.19.0 (2021-09-03)

Full changelog

• #526: Implement mechanism to sort events reliably throughout restarts.
• A new event (glean.restarted) will be included in the events payload of pings, in case there was a restart in the middle of the ping measurement window.
• #534: Expose Uploader base class through @mozilla/glean/<platform>/uploader entry point.
• #580: Limit size of pings database to 250 pings or 10MB.
• #580: BUGFIX: Pending pings at startup up are uploaded from oldest to newest.
• #607: Record an error when incoherent timestamps are calculated for events after a restart.
• #630: Accept booleans and numbers as event extras.
• #647: Implement the Text metric type.
• #658: Implement rate limiting for ping upload.
• Only up to 15 ping submissions every 60 seconds are now allowed.
• #658: BUGFIX: Unblock ping uploading jobs after the maximum of upload failures are hit for a given uploading window.
• #661: Include unminified version of library on Qt/QML builds.
• #681: BUGFIX: Fix error in scanning events database upon initialization on Qt/QML.
• This bug prevents the changes introduced in #526 from working properly in Qt/QML.
• #692: BUGFIX: Ensure events database is initialized at a time Glean is already able to record metrics.
• This bug also prevents the changes introduced in #526 from working properly in all platforms.

v0.18.1 (2021-07-22)

Full changelog

• #552: BUGFIX: Do not clear deletion-request ping from upload queue when disabling upload.

v0.18.0 (2021-07-20)

Full changelog

• #542: Implement shutdown API.

v0.17.0 (2021-07-16)

Full changelog

• #529: Implement the URL metric type.

• #526: Implement new events sorting logic, which allows for reliable sorting of events throughout restarts.

v0.16.0 (2021-07-06)

Full changelog

• #346: Provide default HTTP client for Qt/QML platform.
• #399: Check if there are ping data before attempting to delete it.
• This change lowers the amount of log messages related to attempting to delete inexistent data.
• #411: Tag all messages logged by Glean with the component they are coming from.
• #415, #430: Gzip ping paylod before upload
• This changes the signature of Uploader.post to accept string | Uint8Array for the body parameter, instead of only string.
• #431: BUGFIX: Record the timestamp for events before dispatching to the internal task queue.
• #462: Implement persistent storage for Qt/QML platform.
• #466: Expose ErrorType enum, for using with the testGetNumRecordedErrors API.
• #497: Implement limit of 1MB for ping request payload. Limit is calculated after gzip compression.

v0.15.0 (2021-06-03)

Full changelog

• #389: BUGFIX: Make sure to submit a deletion-request ping before clearing data when toggling upload.
• #375: Release Glean.js for Qt as a QML module.

v0.14.1 (2021-05-21)

Full changelog

• #342: BUGFIX: Fix timespan payload representatin to match exactly the payload expected according to the Glean schema.
• #343: BUGFIX: Report the correct failure exit code when the Glean command line tool fails.

v0.14.0 (2021-05-19)

Full changelog

• #313: Send Glean.js version and platform information on X-Telemetry-Agent header instead of User-Agent header.

v0.13.0 (2021-05-18)

Full changelog

• #313: Implement error recording mechanism and error checking testing API.
• #319: BUGFIX: Do not allow recording floats with the quantity and counter metric types.

v0.12.0 (2021-05-11)

Full changelog

• #279: BUGFIX: Ensure only empty pings triggers logging of "empty ping" messages.
• #288: Support collecting PlatformInfo from Qt applications. Only OS name and locale are supported.
• #281: Add the QuantityMetricType.
• #303: Implement setRawNanos API for the TimespanMetricType.

v0.11.0 (2021-05-03)

Full changelog

• #260: Set minimum node (>= 12.0.0) and npm (>= 7.0.0) versions.
• #202: Add a testing API for the ping type.
• #253:
• Implement the timespan metric type.
• BUGFIX: Report event timestamps in milliseconds.
• #261: Show a spinner while setting up python virtual environment
• #273: BUGFIX: Expose the missing LabeledMetricType and TimespanMetricType in Qt.

v0.10.2 (2021-04-26)

Full changelog

• #256: BUGFIX: Add the missing js extension to the dispatcher.

v0.10.1 (2021-04-26)

Full changelog

• #254: BUGFIX: Allow the usage of the Glean specific metrics API before Glean is initialized.

v0.10.0 (2021-04-20)

Full changelog

• #228: Provide a Qt build with every new release.
• #227: BUGFIX: Fix a bug that prevented using labeled_string and labeled_boolean.
• #226: BUGFIX: Fix Qt build configuration to target ES5.

v0.9.2 (2021-04-19)

Full changelog

• #220: Update glean_parser to version 3.1.1.

v0.9.1 (2021-04-19)

Full changelog

• #219: BUGFIX: Fix path to ping entry point in package.json.

v0.9.0 (2021-04-19)

Full changelog

• #201: BUGFIX: Do not let the platform be changed after Glean is initialized.
• #215: Update the glean-parser to version 3.1.0.
• #214: Improve error reporting of the Glean command.

v0.8.1 (2021-04-14)

Full changelog

• #206: BUGFIX: Fix ping URL path.
• Application ID was being reporting as undefined.

v0.8.0 (2021-04-13)

Full changelog

• #173: Drop Node.js support from webext entry points
• #155: Allow to define custom uploaders in the configuration.
• #184: Correctly report appBuild and appDisplayVersion if provided by the user.
• #198, #192, #184, #180, #174, #165: BUGFIX: Remove all circular dependencies.

v0.7.0 (2021-03-26)

Full changelog

• #143: Provide a way to initialize and reset Glean.js in tests.

v0.6.1 (2021-03-22)

Full changelog

• #130: BUGFIX: Fix destination path of CommonJS' build package.json.

v0.6.0 (2021-03-22)

Full changelog

• #123: BUGFIX: Fix support for ES6 environments.
• Include .js extensions in all local import statements.
• ES6' module resolution algorithm does not currently support automatic resolution of file extensions and does not have the hability to import directories that have an index file. The extension and the name of the file being import need to always be specified. See: https://nodejs.org/api/esm.html#esm_customizing_esm_specifier_resolution_algorithm
• Add a type: module declaration to the main package.json.
• Without this statement, ES6 support is disabled. See: https://nodejs.org/docs/latest-v13.x/api/esm.html#esm_enabling.:
• To keep support for CommonJS, in our CommonJS build we include a package.json that overrides the type: module of the main package.json with a type: commonjs.

v0.5.0 (2021-03-18)

Full changelog

• #96: Provide a ping encryption plugin.
• This plugin listens to the afterPingCollection event. It receives the collected payload of a ping and returns an encrypted version of it using a JWK provided upon instantiation.
• #95: Add a plugins property to the configuration options and create an event abstraction for triggering internal Glean events.
• The only internal event triggered at this point is the afterPingCollection event, which is triggered after ping collection and logging, and before ping storing.
• Plugins are built to listen to a specific Glean event. Each plugin must define an action, which is executed everytime the event they are listening to is triggered.
• #101: BUGFIX: Only validate Debug View Tag and Source Tags when they are present.
• #102: BUGFIX: Include a Glean User-Agent header in all pings.
• #97: Add support for labeled metric types (string, boolean and counter).
• #105: Introduce and publish the glean command for using the glean-parser in a virtual environment.

v0.4.0 (2021-03-10)

Full changelog

• #92: Remove web-ext-types from peerDependencies list.
• #98: Add external APIs for setting the Debug View Tag and Source Tags.
• #99: BUGFIX: Add a default ping value in the testing APIs.

v0.3.0 (2021-02-24)

Full changelog

• #90: Provide exports for CommonJS and browser environemnts.
• #90: BUGIFX: Accept lifetimes as strings when instantiating metric types.
• #90: BUGFIX: Fix type declaration paths.
• #90: BUGFIX: Make web-ext-types a peer dependency.

v0.2.0 (2021-02-23)

• #85: Include type declarations in @mozilla/glean webext package bundle.

Full changelog

v0.1.1 (2021-02-17)

Full changelog

• #77: Include README.md file in @mozilla/glean package bundle.

v0.1.0 (2021-02-17)

Full changelog

• #73: Add this changelog file.
• #42: Implement the deletion-request ping.
• #41: Implement the logPings debug tool.
• When logPings is enabled, pings are logged upon collection.
• #40: Use the dispatcher in all Glean external API functions. Namely:
• Metric recording functions;
• Ping submission;
• initialize and setUploadEnabled.
• #36: Implement the event metric type.
• #31: Implement a task Dispatcher to help in executing Promises in a deterministic order.
• #26: Implement the setUploadEnable API.
• #25: Implement an adapter that leverages browser APIs to upload pings.
• #24: Implement a ping upload manager.
• #23: Implement the initialize API and glean internal metrics.
• #22: Implement the PingType structure and a ping maker.
• #20: Implement the datetime metric type.
• #17: Implement the UUID metric type.
• #14: Implement the counter metric type.
• #13: Implement the string metric type.
• #11: Implement the boolean metric type.
• #9: Implement a metrics database module.
• #8: Implement a web extension version of the underlying storage module.
• #6: Implement an abstract underlying storage module.

This Week in Glean (TWiG)

“This Week in Glean” is a series of blog posts that the Glean Team at Mozilla is using to try to communicate better about our work. They could be release notes, documentation, hopes, dreams, or whatever: so long as it is inspired by Glean.

Contribution Guidelines

There are two important questions to answer before adding new content to this book:

• Where to include this content?
• In which format to present it?

This guide aims to provide context for answering both questions.

Where to add new content?

This book is divided in five different sections. Each section contains pages that are of a specific type. New content will fit in one of these section. Following is an explanation on what kind of content fits in each section.

Overview

Is the content you want to add an essay or high level explanation on some aspect of Glean?

The overview section is the place to provide important higher level context for users of Glean. This section may include essays about Glean’s views, principles, data practices, etc. It also contains primers on information such as what is the Glean SDK and Glean.js.

User Guides

Is the content you want to add a general purpose guide on a Glean aspect or feature?

This section is the place for Glean SDK user guides. Pages under this section contain prose format guides on how to include Glean in a project, how to add metrics, how to create pings, and so on.

Pages on this section may link to the API Reference pages, but should not themselves be API References.

Guides can be quite long, thus we should favor having one page per language binding instead of using tabs.

API Reference

Is the content you want to add a developer reference on a specific Glean API?

This section of the book contains reference for all of Glean’s user APIs. Its pages follow a strict structure.

Each API description contains:

• A title with the name of the API.
• It’s important to use titles, because they automatically generate links to that API.
• A brief description of what the API does.
• Tabs with examples for using that API in each language binding.
• Even if a certain language binding do not contain a given API, the tab will be included in the tabs bar in the disabled state.

The API Reference pages should not contain any guides in prose format, they should all be linked from the User’s Guide when convenient.

Language Binding Information

Is the content you want to add a language binding specific guide on a Glean feature?

Language bindings may require some dedicated pages, these section contains these pages. Each language binding has a top level section under this section, specific pages live under these titles.

Appendix

Is the content you want to add support content for the rest of the content on book?

The appendix contains support information related to the Glean SDK or the content of this book.

In which format to present content?

General guidelines

Each page of the book should be written as if it were the first page a user is visiting ever. There should be links to other pages of the book wherever there is missing context in the current page. This is important, because documentations are first and foremost reference books, manuals. They should not be expected to be read in order.

Prefer using headers whenever a new topic is introduced

mdbook (the tool used to build this book) turns all headers into links. Which is useful to refer to specific documentation sections.

Favor creating new pages, instead of adding unrelated content to an already existing page

This makes it easier to find content through the Summary.

Custom elements

Tabs

Tabs are useful for providing small code examples of Glean's APIs for each language binding. A tabs section starts with the tab_header include and ends with the tab_footer include. Each tab is declared in between these include statements.

Each tab content is placed inside an html div tag with the data-lang and class="tab" attributes. The data-lang attribute contains the title of the tab. Titles must match for different tabs on the same language binding. Whenever a user clicks in a tab with a specific title, all tabs with that same title will be opened by default, until the user clicks in a tab with a different title.

Every tab section should contain tabs for all Glean language bindings, even if a language binding does not provide the API in question. In this case, the tab div should still be there without any inner HTML. When that is the case that tab will be rendered in a disabled state.

These are the tabs every tab section is expected to contain, in order:

• Kotlin
• Java
• Swift
• Python
• Rust
• JavaScript
• Firefox Desktop

Finally, here is an example code for a tabs sections:

{{#include ../../shared/tab_header.md}}
<div data-lang="Kotlin" class="tab">
Kotlin information...
</div>
<div data-lang="Java" class="tab">
Java information...
</div>
<div data-lang="Swift" class="tab">
Swift information...
</div>
<div data-lang="Python" class="tab">
Python information...
</div>
<div data-lang="Rust" class="tab">
Rust information...
</div>
<!--
In this example, JavaScript and Firefox Desktop
would show up as disabled in the final page.
-->
<div data-lang="JavaScript" class="tab"></div>
<div data-lang="Firefox Desktop" class="tab"></div>
{{#include ../../shared/tab_footer.md}}


And this is how those tabs will look like:

Kotlin information...
Java information...
Swift information...
Python information...
Rust information...

Tab tooltips

Tabs in a disabled i.e. tabs that do not have any content, will show a tooltip when hovered.

By default, this tooltip will show the message <lang> doe not provide this API. The following data-* attributes can be used to modify this message.

data-bug

This attribute expects a Bugzilla bug number. When this attribute is added a link to the provided bug will be added to the tooltip text.

data-info

This attribute expects free form text or valid HTML. Be careful when adding long texts here. If a text needs to be too long, consider adding it as an actual section / paragraph to the page instead of as a tooltip.

This is how you can use the above attributes.

{{#include ../../shared/tab_header.md}}
...

<!-- No attribute, default text will show up. -->
<div data-lang="Rust" class="tab"></div>
<!-- data-bug attribute, default text will show up + link to bug. -->
<div data-lang="JavaScript" class="tab" data-bug="000000"></div>
<!-- data-info attribute, free form text will show up. -->
<div data-lang="Firefox Desktop" class="tab" data-info="Hello, Glean world!"></div>
{{#include ../../shared/tab_footer.md}}


And this is how each tool tip is rendered.

Custom block quotes

Sometimes it is necessary to bring attention to a special piece of information, or simply to provide extra context related to the a given text.

In order to do that, there are three custom block quote formats available.

Info quote

An information block quote format, to provide useful extra context for a given text.

Warning quote

A warning block quote format, to provide useful warning related to a given text.

Stop quote

A stronger warning block quote format, to provide useful warning related to a given text in a more emphatic format. Use these sparingly.

To include such quotes, again you can use mdbook include statements.

For the above quotes, this is the corresponding code.

{{#include ../../shared/blockquote-info.html}}

##### Info quote

> An information blockquote format, to provide useful extra context for a given text.

{{#include ../../shared/blockquote-warning.html}}

##### Warning quote

> A warning blockquote format, to provide useful warning related to a given text.

{{#include ../../shared/blockquote-stop.html}}

##### Stop quote

> A stronger warning blockquote format, to provide useful warning related to a given text in a
> more emphatic format. Use these sparingly.


It is possible to use any level header with these special block quotes and also no header at all.