Crash Aggregates Reference

Introduction

The crash_aggregates dataset compiles crash statistics over various dimensions for each day.

Rows and Columns

There's one column for each of the stratifying dimensions and the crash statistics. Each row is a distinct set of dimensions, along with their associated crash stats. Example stratifying dimensions include channel and country, example statistics include usage hours and plugin crashes. See the complete documentation for all available dimensions and statistics.

Accessing the Data

This dataset is accessible via re:dash.

The data is stored as a parquet table in S3 at the following address. See this cookbook to get started working with the data in Spark.

s3://telemetry-parquet/crash_aggregates/v1/

Further Reading

The technical documentation for this dataset can be found in the telemetry-batch-view documentation

Data Reference

Example Queries

Here's an example query that computes crash rates for each channel (sorted by number of usage hours):

SELECT dimensions['channel'] AS channel,
       sum(stats['usage_hours']) AS usage_hours,
       1000 * sum(stats['main_crashes']) / sum(stats['usage_hours']) AS main_crash_rate,
       1000 * sum(stats['content_crashes']) / sum(stats['usage_hours']) AS content_crash_rate,
       1000 * sum(stats['plugin_crashes']) / sum(stats['usage_hours']) AS plugin_crash_rate,
       1000 * sum(stats['gmplugin_crashes']) / sum(stats['usage_hours']) AS gmplugin_crash_rate,
       1000 * sum(stats['gpu_crashes']) / sum(stats['usage_hours']) AS gpu_crash_rate
FROM crash_aggregates
GROUP BY dimensions['channel']
ORDER BY -sum(stats['usage_hours'])

Main process crashes by build date and E10S cohort.

WITH channel_rates AS (
  SELECT dimensions['build_id'] AS build_id,
         SUM(stats['main_crashes']) AS main_crashes, -- total number of crashes
         SUM(stats['usage_hours']) / 1000 AS usage_kilohours, -- thousand hours of usage
         dimensions['e10s_cohort'] AS e10s_cohort -- e10s cohort
   FROM crash_aggregates
   WHERE dimensions['experiment_id'] is null -- not in an experiment
     AND regexp_like(dimensions['build_id'], '^\d{14}$') -- validate build IDs
     AND dimensions['build_id'] > '20160201000000' -- only in the date range that we care about
   GROUP BY dimensions['build_id'], dimensions['e10s_cohort']
)
SELECT cast(parse_datetime(build_id, 'yyyyMMddHHmmss') as date) as build_id, -- program build date
       usage_kilohours, -- thousands of usage hours
       e10s_cohort, -- e10s cohort
       main_crashes / usage_kilohours AS main_crash_rate -- crash rate being defined as crashes per thousand usage hours
FROM channel_rates
WHERE usage_kilohours > 100 -- only aggregates that have statistically significant usage hours
ORDER BY build_id ASC

Sampling

Invalid Pings

We ignore invalid pings in our processing. Invalid pings are defined as those that:

  • The submission dates or activity dates are invalid or missing.
  • The build ID is malformed.
  • The docType field is missing or unknown.
  • The ping is a main ping without usage hours or a crash ping with usage hours.

Scheduling

The crash_aggregates job is run daily, at midnight UTC. The job is scheduled on Airflow. The DAG is here

Schema

The crash_aggregates table has 4 commonly-used columns:

  • submission_date is the date pings were submitted for a particular aggregate.
    • For example, select sum(stats['usage_hours']) from crash_aggregates where submission_date = '2016-03-15' will give the total number of user hours represented by pings submitted on March 15, 2016.
    • The dataset is partitioned by this field. Queries that limit the possible values of submission_date can run significantly faster.
  • activity_date is the day when the activity being recorded took place.
    • For example, select sum(stats['usage_hours']) from crash_aggregates where activity_date = '2016-03-15' will give the total number of user hours represented by activities that took place on March 15, 2016.
    • This can be several days before the pings are actually submitted, so it will always be before or on its corresponding submission_date.
    • Therefore, queries that are sensitive to when measurements were taken on the client should prefer this field over submission_date.
  • dimensions is a map of all the other dimensions that we currently care about. These fields include:
    • dimensions['build_version'] is the program version, like 46.0a1.
    • dimensions['build_id'] is the YYYYMMDDhhmmss timestamp the program was built, like 20160123180541. This is also known as the "build ID" or "buildid".
    • dimensions['channel'] is the channel, like release or beta.
    • dimensions['application'] is the program name, like Firefox or Fennec.
    • dimensions['os_name'] is the name of the OS the program is running on, like Darwin or Windows_NT.
    • dimensions['os_version'] is the version of the OS the program is running on.
    • dimensions['architecture'] is the architecture that the program was built for (not necessarily the one it is running on).
    • dimensions['country'] is the country code for the user (determined using geoIP), like US or UK.
    • dimensions['experiment_id'] is the identifier of the experiment being participated in, such as e10s-beta46-noapz@experiments.mozilla.org, or null if no experiment.
    • dimensions['experiment_branch'] is the branch of the experiment being participated in, such as control or experiment, or null if no experiment.
    • dimensions['e10s_enabled'] is whether E10S is enabled.
    • dimensions['e10s_cohort'] is the E10S cohort the user is part of, such as control, test, or disqualified.
    • dimensions['gfx_compositor'] is the graphics backend compositor used by the program, such as d3d11, opengl and simple. Null values may be reported as none as well.
    • All of the above fields can potentially be blank, which means "not present". That means that in the actual pings, the corresponding fields were null.
  • stats contains the aggregate values that we care about:
    • stats['usage_hours'] is the number of user-hours represented by the aggregate.
    • stats['main_crashes'] is the number of main process crashes represented by the aggregate (or just program crashes, in the non-E10S case).
    • stats['content_crashes'] is the number of content process crashes represented by the aggregate.
    • stats['plugin_crashes'] is the number of plugin process crashes represented by the aggregate.
    • stats['gmplugin_crashes'] is the number of Gecko media plugin (often abbreviated GMPlugin) process crashes represented by the aggregate.
    • stats['content_shutdown_crashes'] is the number of content process crashes that were caused by failure to shut down in a timely manner.
    • stats['gpu_crashes'] is the number of gpu process crashes represented by the aggregate.

TODO(harter): https://bugzilla.mozilla.org/show_bug.cgi?id=1361862

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