opmon/firefox-user-choice.toml

# Monitoring of Firefox 1-click set-as-default (UserChoice) telemetry.
#
# We monitor event volumes and rates, and client volumes and rates (the `sum`
# and `total_ratio` statistics, respectively).
#
# The difference is that a single client can report multiple events.  Since
# we're monitoring errors we bias towards reporting any errors per client, even
# when that client also reports success.
#
# This definition is very repetitive because the underlying data is a
# categorical histogram that combines multiple labels (not present in the data
# itself), the frustration of bucketed histograms, and the lack of control
# structures in TOML definitions.  See `firefox-user-choice.py` for code to
# generate *some* (not all!) of these definitions.

[project]

name = "Firefox Set As Default UserChoice"
platform = "firefox_desktop"
xaxis = "submission_date"
start_date = "2023-01-01"
# We want continuous monitoring of this data:
# end_date =
skip_default_metrics = true

metrics = [
    # Everything.
    "total_client_volume",
    "total_event_volume",

    # Specific results.
    "Success_client_volume",
    "Success_event_volume",
    "ErrProgID_client_volume",
    "ErrProgID_event_volume",
    "ErrHash_client_volume",
    "ErrHash_event_volume",
    "ErrLaunchExe_client_volume",
    "ErrLaunchExe_event_volume",
    "ErrExeTimeout_client_volume",
    "ErrExeTimeout_event_volume",
    "ErrExeProgID_client_volume",
    "ErrExeProgID_event_volume",
    "ErrExeHash_client_volume",
    "ErrExeHash_event_volume",
    "ErrExeRejected_client_volume",
    "ErrExeRejected_event_volume",
    "ErrExeOther_client_volume",
    "ErrExeOther_event_volume",
    "ErrOther_client_volume",
    "ErrOther_event_volume",
    "ErrBuild_client_volume",
    "ErrBuild_event_volume",

    # For convenience: everything that's not Success.
    "not_Success_client_volume",
    "not_Success_event_volume",
]

[project.population]

data_source = "firefox_userchoice"
monitor_entire_population = true
dimensions = ["normalized_channel", "windows_version", "is_msix"]

[dimensions.windows_version]
data_source = "firefox_userchoice"
select_expression = "windows_version"
friendly_name = "Windows Version"
description = "Windows version, like 'Windows 11', 'Windows 10', etc.  See https://github.com/mozilla/bigquery-etl/blob/generated-sql/sql/mozfun/norm/windows_version_info/udf.sql."

[dimensions.normalized_channel]
data_source = "firefox_userchoice"
select_expression = "normalized_channel"
friendly_name = "Channel"
description = "Release channel, like 'release', 'beta', 'nightly', 'esr', 'devedition'."

[dimensions.is_msix]
data_source = "firefox_userchoice"
select_expression = "is_msix"
friendly_name = "Is MSIX"
description = "Whether this install is an MSIX build, i.e., installed from the Microsoft Store."

[data_sources]

[data_sources.firefox_userchoice]

# We could incorporate names and not just keys here, but since we
# manually name the keys in the metrics below, there's little
# advantage to the JOIN or CASE.

from_expression = """
(
    SELECT
        DATE(submission_timestamp) AS submission_date,
        SUBSTR(application.build_id, 0, 10) AS build_id,
        client_id,
        mozfun.norm.windows_version_info(
          environment.system.os.name,
          environment.system.os.version,
          CAST(environment.system.os.windows_build_number AS INT64)) AS windows_version,
        normalized_channel,
        COALESCE(environment.system.has_win_package_id, FALSE) AS is_msix,
        values.key AS key,
        SUM(values.value) AS value
    FROM
        `mozdata.telemetry.main`
    CROSS JOIN
        UNNEST(mozfun.hist.extract(payload.histograms.browser_set_default_user_choice_result).VALUES) values
    WHERE
        sample_id = 52
        AND normalized_os = 'Windows'
        AND mozfun.hist.extract(payload.histograms.browser_set_default_user_choice_result).bucket_count IS NOT NULL
    GROUP BY
        submission_date,
        build_id,
        client_id,
        windows_version,
        normalized_channel,
        is_msix,
        key
    HAVING
        value > 0
)
"""

submission_date_column = "submission_date"
build_id_column = "build_id"
client_id_column = "client_id"

[metrics]

# For client volumes: We want each (submission_date, client id, build
# id) group to yield 1 for a single metric and 0 for all others, so
# that proportions and rates are easy to understand.  Choosing `MIN`
# biases towards "ever succeeded"; choosing `MAX` biases towards "ever
# failed".  Right now we care about failure.

[metrics.total_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * 1"
type = "scalar"

[metrics.total_client_volume.statistics]
sum = {}

[metrics.Success_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 0 AS INT64)"
type = "scalar"

[metrics.Success_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrProgID_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 1 AS INT64)"
type = "scalar"

[metrics.ErrProgID_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrHash_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 2 AS INT64)"
type = "scalar"

[metrics.ErrHash_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrLaunchExe_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 3 AS INT64)"
type = "scalar"

[metrics.ErrLaunchExe_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrExeTimeout_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 4 AS INT64)"
type = "scalar"

[metrics.ErrExeTimeout_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrExeProgID_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 5 AS INT64)"
type = "scalar"

[metrics.ErrExeProgID_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrExeHash_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 6 AS INT64)"
type = "scalar"

[metrics.ErrExeHash_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrExeRejected_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 7 AS INT64)"
type = "scalar"

[metrics.ErrExeRejected_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrExeOther_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 8 AS INT64)"
type = "scalar"

[metrics.ErrExeOther_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrOther_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 9 AS INT64)"
type = "scalar"

[metrics.ErrOther_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.ErrBuild_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) = 10 AS INT64)"
type = "scalar"

[metrics.ErrBuild_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

[metrics.not_Success_client_volume]
data_source = "firefox_userchoice"
select_expression = "100 * CAST(MAX(key) > 0 AS INT64)"
type = "scalar"

[metrics.not_Success_client_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_client_volume" }

# For event volumes: we sum everything, so proportions and rates
# should be easy to understand.

[metrics.total_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(value)"
type = "scalar"

[metrics.total_event_volume.statistics]
sum = {}

[metrics.Success_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 0, value, 0))"
type = "scalar"

[metrics.Success_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrProgID_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 1, value, 0))"
type = "scalar"

[metrics.ErrProgID_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrHash_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 2, value, 0))"
type = "scalar"

[metrics.ErrHash_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrLaunchExe_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 3, value, 0))"
type = "scalar"

[metrics.ErrLaunchExe_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrExeTimeout_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 4, value, 0))"
type = "scalar"

[metrics.ErrExeTimeout_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrExeProgID_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 5, value, 0))"
type = "scalar"

[metrics.ErrExeProgID_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrExeHash_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 6, value, 0))"
type = "scalar"

[metrics.ErrExeHash_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrExeRejected_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 7, value, 0))"
type = "scalar"

[metrics.ErrExeRejected_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrExeOther_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 8, value, 0))"
type = "scalar"

[metrics.ErrExeOther_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrOther_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 9, value, 0))"
type = "scalar"

[metrics.ErrOther_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.ErrBuild_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key = 10, value, 0))"
type = "scalar"

[metrics.ErrBuild_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }

[metrics.not_Success_event_volume]
data_source = "firefox_userchoice"
select_expression = "100 * SUM(IF(key > 0, value, 0))"
type = "scalar"

[metrics.not_Success_event_volume.statistics]
sum = {}
total_ratio = { denominator_metric = "total_event_volume" }