Choosing a Desktop Product Dataset

This document will help you find the best data source for a given analysis.

This guide focuses on descriptive datasets and does not cover experimentation. For example, this guide will help if you need to answer questions like:

  • How many users do we have in Germany, how many crashes we see per day?
  • How many users have a given addon installed?

If you're interested in figuring out whether there's a causal link between two events take a look at our tools for experimentation.

Table of Contents

Raw Pings

We receive data from our users via pings. There are several types of pings, each containing different measurements and sent for different purposes. To review a complete list of ping types and their schemata, see this section of the Mozilla Source Tree Docs.

Many pings are also described by a JSONSchema specification which can be found in this repository.

There are a few pings that are central to delivering our core data collection primitives (Histograms, Events, Scalars) and for keeping an eye on Firefox behaviour (Environment, New Profiles, Updates, Crashes).

For instance, a user's first session in Firefox might have four pings like this:

Flowchart of pings in the user's first session

"main" ping

The "main" ping is the workhorse of the Firefox Telemetry system. It delivers the Telemetry Environment as well as Histograms and Scalars for all process types that collect data in Firefox. It has several variants each with specific delivery characteristics:

ReasonSent whenNotes
shutdownFirefox session ends cleanlyAccounts for about 80% of all "main" pings. Sent by Pingsender immediately after Firefox shuts down, subject to conditions: Firefox 55+, if the OS isn't also shutting down, and if this isn't the client's first session. If Pingsender fails or isn't used, the ping is sent by Firefox at the beginning of the next Firefox session.
dailyIt has been more than 24 hours since the last "main" ping, and it is around local midnightIn long-lived Firefox sessions we might go days without receiving a "shutdown" ping. Thus the "daily" ping is sent to ensure we occasionally hear from long-lived sessions.
environment-changeTelemetry Environment changesIs sent immediately when triggered by Firefox (Installing or removing an addon or changing a monitored user preference are common ways for the Telemetry Environment to change)
aborted-sessionFirefox session doesn't end cleanlySent by Firefox at the beginning of the next Firefox session.

It was introduced in Firefox 38.

"first-shutdown" ping

The "first-shutdown" ping is identical to the "main" ping with reason "shutdown" created at the end of the user's first session, but sent with a different ping type. This was introduced when we started using Pingsender to send shutdown pings as there would be a lot of first-session "shutdown" pings that we'd start receiving all of a sudden.

It is sent using Pingsender.

It was introduced in Firefox 57.

"event" ping

The "event" ping provides low-latency eventing support to Firefox Telemetry. It delivers the Telemetry Environment, Telemetry Events from all Firefox processes, and some diagnostic information about Event Telemetry. It is sent every hour if there have been events recorded, and up to once every 10 minutes (governed by a preference) if the maximum event limit for the ping (default to 1000 per process, governed by a preference) is reached before the hour is up.

It was introduced in Firefox 62.

"update" ping

Firefox Update is the most important means we have of reaching our users with the latest fixes and features. The "update" ping notifies us when an update is downloaded and ready to be applied (reason: "ready") and when the update has been successfully applied (reason: "success"). It contains the Telemetry Environment and information about the update.

It was introduced in Firefox 56.

"new-profile" ping

When a user starts up Firefox for the first time, a profile is created. Telemetry marks the occasion with the "new-profile" ping which sends the Telemetry Environment. It is sent either 30 minutes after Firefox starts running for the first time in this profile (reason: "startup") or at the end of the profile's first session (reason: "shutdown"), whichever comes first. "new-profile" pings are sent immediately when triggered. Those with reason "startup" are sent by Firefox. Those with reason "shutdown" are sent by Pingsender.

It was introduced in Firefox 55.

"crash" ping

The "crash" ping provides diagnostic information whenever a Firefox process exits abnormally. Unlike the "main" ping with reason "aborted-session", this ping does not contain Histograms or Scalars. It contains a Telemetry Environment, Crash Annotations, and Stack Traces.

It was introduced in Firefox 40.

"optout" ping

In the event a user opts out of Telemetry, we send one final "optout" ping to let us know. We try exactly once to send it, discarding the ping if sending fails. It contains only the common ping data and an empty payload.

It was introduced in Firefox 63.


Pingsender is a small application shipped with Firefox which attempts to send pings even if Firefox is not running. If Firefox has crashed or has already shut down we would otherwise have to wait for the next Firefox session to begin to send pings.

Pingsender was introduced in Firefox 54 to send "crash" pings. It was expanded to send "main" pings of reason "shutdown" in Firefox 55 (excepting the first session). It sends the "first-shutdown" ping since its introduction in Firefox 57.


The large majority of analyses can be completed using only the main ping. This ping includes histograms, scalars, and other performance and diagnostic data.

Few analyses actually rely directly on any raw ping data. Instead, we provide derived datasets which are processed versions of these data, made to be:

  • Easier and faster to query
  • Organized to make the data easier to analyze
  • Cleaned of erroneous or misleading data

Before analyzing raw ping data, check to make sure there isn't already a derived dataset made for your purpose. If you do need to work with raw ping data, be aware that loading the data can take a while. Try to limit the size of your data by controlling the date range, etc.

Accessing the Data

Ping data lives in BigQuery and is accessible in re:dash; see our BigQuery intro. There is currently limited history for main pings available in BigQuery; an import of historical data is planned, but without a determined timeline, so longer history requires an ATMO cluster using the Dataset API.

Further Reading

You can find the complete ping documentation. To augment our data collection, see Collecting New Data and the Data Collection Policy.

Main Ping Derived Datasets

The main ping contains most of the measurements used to track performance and health of Firefox in the wild. This ping includes histograms, scalars, and events.

This section describes the derived datasets we provide to make analyzing this data easier.


The longitudinal dataset is a 1% sample of main ping data organized so that each row corresponds to a client_id. If you're not sure which dataset to use for your analysis, this is probably what you want.


Each row in the longitudinal dataset represents one client_id, which is approximately a user. Each column represents a field from the main ping. Most fields contain arrays of values, with one value for each ping associated with a client_id. Using arrays give you access to the raw data from each ping, but can be difficult to work with from SQL. Here's a query showing some sample data to help illustrate. Take a look at the longitudinal examples if you get stuck.

Background and Caveats

Think of the longitudinal table as wide and short. The dataset contains more columns than main_summary and down-samples to 1% of all clients to reduce query computation time and save resources.

In summary, the longitudinal table differs from main_summary in two important ways:

  • The longitudinal dataset groups all data so that one row represents a client_id
  • The longitudinal dataset samples to 1% of all client_ids

Please note that this dataset only contains release (or opt-out) histograms and scalars.

Accessing the Data

The longitudinal is available in re:dash, though it can be difficult to work with the array values in SQL. Take a look at this example query.

The data is stored as a parquet table in S3 at the following address.



The main_summary table is the most direct representation of a main ping but can be difficult to work with due to its size. Prefer the clients_daily dataset unless it doesn't aggregate the measurements you're interested in.


The main_summary table contains one row for each ping. Each column represents one field from the main ping payload, though only a subset of all main ping fields are included. This dataset does not include most histograms.

Background and Caveats

This table is massive, and due to its size, it can be difficult to work with. You should avoid querying main_summary from re:dash. Your queries will be slow to complete and can impact performance for other users, since re:dash on a shared cluster.

Instead, we recommend using the longitudinal or clients_daily dataset where possible. If these datasets do not suffice, consider using Spark on an ATMO cluster. In the odd case where these queries are necessary, make use of the sample_id field and limit to a short submission date range.

Accessing the Data

The data is stored as a parquet table in S3 at the following address.


Though not recommended main_summary is accessible through re:dash. Here's an example query. Your queries will be slow to complete and can impact performance for other users, since re:dash is on a shared cluster.

Further Reading

The technical documentation for main_summary is located in the telemetry-batch-view documentation.

The code responsible for generating this dataset is here


The first_shutdown_summary table is a summary of the first-shutdown ping.


The first shutdown ping contains first session usage data. The dataset has rows similar to the telemetry_new_profile_parquet, but in the shape of main_summary.

Background and Caveats

Ping latency was reduced through the shutdown ping-sender mechanism in Firefox 55. To maintain consistent historical behavior, the first main ping is not sent until the second start up. In Firefox 57, a separate first-shutdown ping was created to evaluate first-shutdown behavior while maintaining backwards compatibility.

In many cases, the first-shutdown ping is a duplicate of the main ping. The first-shutdown summary can be used in conjunction with the main summary by taking the union and deduplicating on the document_id.

Accessing the Data

The data can be accessed as first_shutdown_summary. It is currently stored in the following path.


The data is backfilled to 2017-09-22, the date of its first nightly appearance. This data should be available to all releases on and after Firefox 57.


The client_count_daily dataset is useful for estimating user counts over a few pre-defined dimensions.

The client_count_daily dataset is similar to the deprecated client_count dataset except that is aggregated by submission date and not activity date.


This dataset includes columns for a dozen factors and an HLL variable. The hll column contains a HyperLogLog variable, which is an approximation to the exact count. The factor columns include submission date and the dimensions listed here. Each row represents one combinations of the factor columns.

Background and Caveats

It's important to understand that the hll column is not a standard count. The hll variable avoids double-counting users when aggregating over multiple days. The HyperLogLog variable is a far more efficient way to count distinct elements of a set, but comes with some complexity. To find the cardinality of an HLL use cardinality(cast(hll AS HLL)). To find the union of two HLL's over different dates, use merge(cast(hll AS HLL)). The Firefox ER Reporting Query is a good example to review. Finally, Roberto has a relevant write-up here.

Accessing the Data

The data is available in Re:dash. Take a look at this example query.

I don't recommend accessing this data from ATMO.

Further Reading


The clients_last_seen dataset is useful for efficiently determining exact user counts such as DAU and MAU.

It does not use approximates, unlike the HyperLogLog algorithm used in the client_count_daily dataset, and it includes the most recent values in a 28 day window for all columns in the clients_daily dataset.

This dataset should be used instead of client_count_daily.


For each submission_date this dataset contains one row per client_id that appeared in clients_daily in a 28 day window including submission_date and preceding days.

The days_since_seen column indicates the difference between submission_date and the most recent submission_date in clients_daily where the client_id appeared. A client observed on the given submission_date will have days_since_seen = 0.

Other days_since_ columns use the most recent date in clients_daily where a certain condition was met. If the condition was not met for a client_id in a 28 day window NULL is used. For example days_since_visited_5_uri uses the condition scalar_parent_browser_engagement_total_uri_count_sum >= 5. These columns can be used for user counts where a condition must be met on any day in a window instead of using the most recent values for each client_id.

The rest of the columns use the most recent value in clients_daily where the client_id appeared.

Background and Caveats

User counts generated using days_since_seen only reflect the most recent values from clients_daily for each client_id in a 28 day window. This means Active MAU as defined cannot be efficiently calculated using days_since_seen because if a given client_id appeared every day in February and only on February 1st had scalar_parent_browser_engagement_total_uri_count_sum >= 5 then it would only be counted on the 1st, and not the 2nd-28th. Active MAU can be efficiently and correctly calculated using days_since_visited_5_uri.

MAU can be calculated over a GROUP BY submission_date[, ...] clause using COUNT(*), because there is exactly one row in the dataset for each client_id in the 28 day MAU window for each submission_date.

User counts generated using days_since_seen can use SUM to reduce groups, because a given client_id will only be in one group per submission_date. So if MAU were calculated by country and channel, then the sum of the MAU for each country would be the same as if MAU were calculated only by channel.

Accessing the Data

The data is available in Re:dash and BigQuery. Take a look at this full running example query in Re:dash.


The clients_daily table is intended as the first stop for asking questions about how people use Firefox. It should be easy to answer simple questions. Each row in the table is a (client_id, submission_date) and contains a number of aggregates about that day's activity.


Many questions about Firefox take the form "What did clients with characteristics X, Y, and Z do during the period S to E?" The clients_daily table is aimed at answer those questions.

Accessing the Data

The data is stored as a parquet table in S3 at the following address.


The clients_daily table is accessible through re:dash using the Athena data source. It is also available via the Presto data source, though Athena should be preferred for performance and stability reasons.

Here's an example query.

Crash Ping Derived Datasets

The crash ping is captured after the main Firefox process crashes or after a content process crashes, whether or not the crash report is submitted to It includes non-identifying metadata about the crash.

This section describes the derived datasets we provide to make analyzing this data easier.


The error_aggregates_v2 table represents counts of errors counted from main and crash pings, aggregated every 5 minutes. It is the dataset backing the main mission control view, but may also be queried independently.


The error_aggregates_v2 table contains counts of various error measures (for example: crashes, "the slow script dialog showing"), aggregated across each unique set of dimensions (for example: channel, operating system) every 5 minutes. You can get an aggregated count for any particular set of dimensions by summing using SQL.

Experiment unpacking

It's important to note that when this dataset is written, pings from clients participating in an experiment are aggregated on the experiment_id and experiment_branch dimensions corresponding to what experiment and branch they are participating in. However, they are also aggregated with the rest of the population where the values of these dimensions are null. Therefore care must be taken when writing aggregating queries over the whole population - in these cases one needs to filter for experiment_id is null and experiment_branch is null in order to not double-count pings from experiments.

Accessing the data

You can access the data via re:dash. Choose Athena and then select the telemetry.error_aggregates_v2 table.

Further Reading

The code responsible for generating this dataset is here.


The crash_summary table is the most direct representation of a crash ping.


The crash_summary table contains one row for each crash ping. Each column represents one field from the crash ping payload, though only a subset of all crash ping fields are included.

Accessing the Data

The data is stored as a parquet table in S3 at the following address.


crash_summary is accessible through re:dash. Here's an example query.

Further Reading

The technical documentation for crash_summary is located in the telemetry-batch-view documentation.

The code responsible for generating this dataset is here

New-Profile Derived Datasets

The new-profile ping is sent from Firefox Desktop on the first session of a newly created profile and contains the initial information about the user environment.

This data is available in the telemetry_new_profile_parquet dataset.

The telemetry_new_profile_parquet table is the most direct representation of a new-profile ping.


The table contains one row for each ping. Each column represents one field from the new-profile ping payload, though only a subset of all fields are included.

Accessing the Data

The data is stored as a parquet table in S3 at the following address.


The telemetry_new_profile_parquet is accessible through re:dash. Here's an example query.

Further Reading

This dataset is generated automatically using direct to parquet. The configuration responsible for generating this dataset was introduced in bug 1360256.

Update Derived Dataset

The update ping is sent from Firefox Desktop when a browser update is ready to be applied and after it was correctly applied. It contains the build information and the update blob information, in addition to some information about the user environment. The telemetry_update_parquet table is the most direct representation of an update ping.


The table contains one row for each ping. Each column represents one field from the update ping payload, though only a subset of all fields are included.

Accessing the Data

The data is stored as a parquet table in S3 at the following address.


The telemetry_update_parquet is accessible through re:dash. Here's an example query.

Further Reading

This dataset is generated automatically using direct to parquet. The configuration responsible for generating this dataset was introduced in bug 1384861.

Other Datasets

Public crash statistics for Firefox are available through the Data Platform in a socorro_crash dataset. The crash data in Socorro is sanitized and made available to ATMO and STMO. A nightly import job converts batches of JSON documents into a columnar format using the associated JSON Schema.


Accessing the Data

The dataset is available in parquet at s3://telemetry-parquet/socorro_crash/v2. It is also indexed with Athena and Presto with the table name socorro_crash.

Obsolete Datasets


The heavy_users table provides information about whether a given client_id is considered a "heavy user" on each day (using submission date).


The heavy_users table contains one row per client-day, where day is submission_date. A client has a row for a specific submission_date if they were active at all in the 28 day window ending on that submission_date.

A user is a "heavy user" as of day N if, for the 28 day period ending on day N, the sum of their active_ticks is in the 90th percentile (or above) of all clients during that period. For more analysis on this, and a discussion of new profiles, see this link.

Background and Caveats

  1. Data starts at 20170801. There is technically data in the table before this, but the heavy_user column is NULL for those dates because it needed to bootstrap the first 28 day window.
  2. Because it is top the 10% of clients for each 28 day period, more than 10% of clients active on a given submission_date will be considered heavy users. If you join with another data source (main_summary, for example), you may see a larger proportion of heavy users than expected.
  3. Each day has a separate, but related, set of heavy users. Initial investigations show that approximately 97.5% of heavy users as of a certain day are still considered heavy users as of the next day.
  4. There is no "fixing" or weighting of new profiles - days before the profile was created are counted as zero active_ticks. Analyses may need to use the included profile_creation_date field to take this into account.

Accessing the Data

The data is available both via sql.t.m.o and Spark.

In Spark:"s3://telemetry-parquet/heavy_users/v1")


SELECT * FROM heavy_users LIMIT 3

Further Reading

The code responsible for generating this dataset is here


The retention table provides client counts relevant to client retention at a 1-day granularity. The project is tracked in Bug 1381840


The retention table contains a set of attribute columns used to specify a cohort of users and a set of metric columns to describe cohort activity. Each row contains a permutation of attributes, an approximate set of clients in a cohort, and the aggregate engagement metrics.

This table uses the HyperLogLog (HLL) sketch to create an approximate set of clients in a cohort. HLL allows counting across overlapping cohorts in a single pass while avoiding the problem of double counting. This data-structure has the benefit of being compact and performant in the context of retention analysis, at the expense of precision. For example, calculating a 7-day retention period can be obtained by aggregating over a week of retention data using the union operation. With SQL primitive, this requires a recalculation of COUNT DISTINCT over client_id's in the 7-day window.

Background and Caveats

  1. The data starts at 2017-03-06, the merge date where Nightly started to track Firefox 55 in Mozilla-Central. However, there was not a consistent view into the behavior of first session profiles until the new_profile ping. This means much of the data is inaccurate before 2017-06-26.
  2. This dataset uses 4 day reporting latency to aggregate at least 99% of the data in a given submission date. This figure is derived from the telemetry-health measurements on submission latency, with the discussion in Bug 1407410. This latency metric was reduced Firefox 55 with the introduction of the shutdown ping-sender mechanism.
  3. Caution should be taken before adding new columns. Additional attribute columns will grow the number of rows exponentially.
  4. The number of HLL bits chosen for this dataset is 13. This means the default size of the HLL object is 2^13 bits or 1KiB. This maintains about a 1% error on average. See this table from Algebird's HLL implementation for more details.

Accessing the Data

The data is primarily available through Re:dash on STMO via the Presto source. This service has been configured to use predefined HLL functions.

The column should first be cast to the HLL type. The scalar cardinality(<hll_column>) function will approximate the number of unique items per HLL object. The aggregate merge(<hll_column>) function will perform the set union between all objects in a column.

Example: Cast the count column into the appropriate type.

SELECT cast(hll as HLL) as n_profiles_hll FROM retention

Count the number of clients seen over all attribute combinations.

SELECT cardinality(cast(hll as HLL)) FROM retention

Group-by and aggregate client counts over different release channels.

SELECT channel, cardinality(merge(cast(hll AS HLL))
FROM retention
GROUP BY channel

The HyperLogLog library wrappers are available for use outside of the configured STMO environment, spark-hyperloglog and presto-hyperloglog.

Also see the client_count_daily dataset.


The churn dataset tracks the 7-day churn rate of telemetry profiles. This dataset is generally used for analyzing cohort churn across segments and time.


Churn is the rate of attrition defined by (clients seen in week N)/(clients seen in week 0) for groups of clients with some shared attributes. A group of clients with shared attributes is called a cohort. The cohorts in this dataset are created every week and can be tracked over time using the acquisition_date and the weeks since acquisition or current_week.

The following example demonstrates the current logic for generating this dataset. Each column represents the days since some arbitrary starting date.


All three clients are part of the same cohort. Client A is retained for weeks 0 and 1 since there is activity in both periods. A client only needs to show up once in the period to be counted as retained. Client B is acquired in week 0 and is active frequently but does not appear in following weeks. Client B is considered churned on week 1. However, a client that is churned can become retained again. Client C is considered churned on week 1 but retained on week 2.

The following table summarizes the above daily activity into the following view where every column represents the current week since acquisition date..


The clients are then grouped into cohorts by attributes. An attribute describes a property about the cohort such as the country of origin or the binary distribution channel. Each group also contains descriptive aggregates of engagement. Each metric describes the activity of a cohort such as size and overall usage at a given time instance.

Background and Caveats

The original concept for churn is captured in this Mana page. The original derived data-set was created in bug 1198537. The first major revision (v2) of this data-set added attribution, search, and uri counts. The second major revision (v3) included additional clients through the new-profile ping and adjusted the collection window from 10 to 5 days.

  • Each row in this dataset describes a unique segment of users
    • The number of rows is exponential with the number of dimensions
    • New fields should be added sparing to account for data-set size
  • The dataset lags by 10 days in order account for submission latency
    • This value was determined to be time for 99% of main pings to arrive at the server. With the shutdown-ping sender, this has been reduced to 4 days. However, churn_v3 still tracks releases older than Firefox 55.
  • The start of the period is fixed to Sundays. Once it has been aggregated, the period cannot be shifted due to the way clients are counted.
    • A supplementary 1-day retention dataset using HyperLogLog for client counts is available for counting over arbitrary retention periods and date offsets. Additionally, calculating churn or retention over specific cohorts is tractable in STMO with main_summary or clients_daily datasets.

Accessing the Data

churn is available in Re:dash under Athena and Presto. The data is also available in parquet for consumption by columnar data engines at s3://telemetry-parquet/churn/v3.


Mobile Metrics

There are several tables owned by the mobile team documented here:

  • android_events
  • android_clients
  • android_addons
  • mobile_clients