Metric Hub

Metric Hub is a repository that contains metric, data source and segment definitions that have been reviewed and can be seen as the source of truth. Definitions that are part of Metric Hub can be referenced in configurations of other tooling as well, such as Jetstream and OpMon.

Generated docs for available metrics is available here.

Adding definitions

To add or update a project configuration, open a pull request against metric-hub. CI checks will validate that the structure of the definitions as well as the SQL syntax is correct. A review by data science is required before changes can get merged.

Definitions are part of config files that are written in TOML. These definitions files are platform-specific and located in the definitions/ directory of the metric-hub repository. Definitions files are named after the platform they target, for example definitions related to Firefox Desktop are in the firefox_desktop.toml file.

Generally, configuration files have four main sections: [data_sources], [metrics], [segments], and [dimensions]. All of these sections are optional.

Lines starting with a # are comments and have no effect.

[data_sources] Section

Data sources specify the tables data should be queried from.


# FROM expression - often just a fully-qualified table name. Sometimes a subquery.
from_expression = "mozdata.telemetry.main"

# SQL snippet specifying the submission_date column
submission_date_column = "submission_date"

# FROM expression - subquery
from_expression = """
        FROM ``
            event_category = 'memory_watcher'
submission_date_column = "submission_date"

[metrics] Section

The metrics sections allows to specify metrics. A metric aggregates data and is associated with some data source.

Each metric is identified by a unique slug and a version (versions are optional but strongly encouraged), and can be defined by adding a new section with a name like:




# The data source to use. Use the slug of a data source defined in a platform-specific config,
# or else define a new data source (see above).
data_source = "events_memory"

# A clause of a SELECT expression with an aggregation
select_expression = "SUM(SAFE_CAST(SPLIT(event_string_value, ',')[OFFSET(1)] AS NUMERIC))"

# Type of the metric to be evaluated.
# This is used to determine the method of aggregation to be applied.
# Either "scalar" or "histogram".
# scalar = a single value is returned
# histogram = an array of histograms is returned
type = "scalar"

# A friendly metric name displayed in dashboards.
friendly_name = "Memory Pressure Count"

# A description that will be displayed by dashboards.
description = "Number of memory pressure events"

# This can be any string value. It's currently not being used but in the future, this could be used to visually group different metrics by category.
category = "performance"

# And optional owner or team owning this metric
owner = ""

# Whether the metric is deprecated and should no longer be used
deprecated = false

Since metrics aggregate data, the metric SQL definition must contain some aggregation method (like SUM, COUNT, ...) to be valid.

Existing metrics cannot be removed after they have been added to Metric Hub. Other tools or configurations might still reference the deleted metric resulting in their computations to break. Instead, to indicate that a metric should no longer be used deprecated should be set to true.

[dimensions] Section

Dimensions define a field or dimension on which the client population should be segmented. Dimensions are used in OpMon. For segmenting client populations clients see the [segments] section.

For example:


# The data source to use. Use the slug of a data source defined in a platform-specific config,
# or else define a new data source (see above).
data_source = "main"

# SQL snippet referencing a field whose values should be used to segment the client population.
select_expression = "normalized_os"

[segments] Section

Segments specify a boolean condition that determines whether a client is part of the segment. Segment are used in Jetstream, for segmenting client populations in OpMon please see the [dimensions] section.

# Note the aggregation function; these expressions are grouped over client_id
select_expression = '{{agg_any("is_default_browser")}}'data_source = "my_data_source"

# segments require their own data source to be defined
# the standard `data_source`s cannot be used for segments
from_expression = '(SELECT submission_date, client_id, is_default_browser FROM my_cool_table)'

Segment SQL snippets need to be boolean expressions to be valid.

Accessing and Using Metric Definitions

All the definitions are automatically available in some of our tooling:

  • Jetstream - used for analyzing experiments
  • mozanalysis - a Python library which standardizes how experiment data is analyzed at Mozilla
  • OpMon - a tool for monitoring operational metrics
  • bigquery-etl - used for writing ETL queries
  • Looker - used for creating dashboards and ad-hoc analyses

Using Metrics in ETL queries

Metrics and data sources can be referenced in query and view definitions in bigquery-etl. Query and view definitions are Jinja templates which have access to the metrics.calculate() and metrics.data_source() functions.

Metrics can be referenced as follows:

    {{ metrics.calculate(
    metrics=['days_of_use', 'active_hours'],
    group_by={'sample_id': 'sample_id', 'channel': ''},
    where='submission_date = "2023-01-01"'
    ) }}

This query will get translated to:

    WITH clients_daily AS (
        client_id AS client_id,
        submission_date AS submission_date,
        COALESCE(SUM(active_hours_sum), 0) AS active_hours,
        COUNT(submission_date) AS days_of_use,
        GROUP BY

The following parameters are available for metrics.calculate() to customize the query:

  • metrics: unique reference(s) to metric definition, all metric definitions are aggregations (e.g. SUM, AVG, ...)
  • platform: platform to compute metrics for (e.g. firefox_desktop, firefox_ios, fenix, ...)
  • group_by: fields used in the GROUP BY statement; this is a dictionary where the key represents the alias, the value is the field path; GROUP BY always includes the configured client_id and submission_date fields
  • where: SQL filter clause
  • group_by_client_id: Whether the field configured as client_id (defined as part of the data source specification in metric-hub) should be part of the GROUP BY. True by default
  • group_by_submission_date: Whether the field configured as submission_date (defined as part of the data source specification in metric-hub) should be part of the GROUP BY. True by default

Data sources can be referenced as follows:

FROM {{ metrics.data_source(
        where='submission_date = "2023-01-01"'

To render queries into raw SQL queries use the ./bqetl query render path/to/query.sql command. Rendered views and queries are also available on the generated-sql branch in bigquery-etl. The bqetl tooling does support running and publishing artifacts that use Jinja (translating Jinja templates into raw SQL isn't strictly necessary to perform these actions).

Using Metrics in Python Scripts

Metric definitions can also be imported into Python scripts by using the mozilla-metric-config-parser. This library automatically parses the definitions in Metric Hub and returns their Python type representations.

from metric_config_parser.config import ConfigCollection

config_collection = ConfigCollection.from_github_repo("")
metric = config_collection.get_metric_definition(slug="active_hours", app_name="firefox_desktop")

To use the metrics with Mozanalysis, you'll need Metrics not MetricDefinitions. For example:

from mozanalysis.config import ConfigLoader
metric = ConfigLoader.get_metric(metric_slug="active_hours", app_name="firefox_desktop")

Using Metrics in Looker

Metric definitions are available in Looker. A single explore exists for each product/namespace that exposes all metric definitions from metric-hub. These explores are prefixed with "Metric Definitions" followed by the platform name. For example, for Firefox Desktop an explore "Metric Definitions Firefox Desktop" is available.

The explore looks like the following:

The side pane has all available fields, with metrics appearing as dimensions. Metrics appear in separate sections that correspond to the data source they are derived from. A Base Fields section contains dimensions that are useful for filtering or segmenting the population, like channel or operating system. These base fields are based on clients_daily tables.

By default, metrics are computed on a per-client basis. To get a summary over the entire population or a population segment for a specific metric it is necessary to create a custom measure which aggregates the metric dimension.


Should metrics be defined in the metric definition, data source definition or source table?

Definitions for metrics can be encoded at different levels. It is preferable to specify the SQL that defines how a metric should be computed as much upstream as possible. This allows the most flexible usage of metric definitions.

Ideally, metrics should be defined in the [metrics] section. However in some cases metrics might rely on more complex logic. For example, if some more complicated unnesting of fields or JOINs across multiple tables are required it might make sense to move the metric computation into the [data_sources] definition and then simply reference the field in the [metrics] section. The main drawback of this is that if users want to find the definition they will have to go one layer deeper and check how the data source is defined versus just having to look at the metric definition itself.

For computationally expensive metrics it can make sense to set up an ETL job that computes the metrics on a daily basis and writes results to a separate table. This table can serve as basis of a data source which can then be used to define a metric.