Longitudinal Examples


The longitudinal dataset is a summary of main pings. If you're not sure which dataset to use for your query, this is probably what you want. It differs from the main_summary table in two important ways:

  • The longitudinal dataset groups all data for a client-id in the same row. This makes it easy to report profile level metrics. Without this deduplicating, metrics would be weighted by the number of submissions instead of by clients.
  • The dataset uses a 1% of all recent profiles, which will reduce query computation time and save resources. The sample of clients will be stable over time.

Accordingly, one should prefer using the Longitudinal dataset except in the rare case where a 100% sample is strictly necessary.

As discussed in the Longitudinal Data Set Example Notebook:

The longitudinal dataset is logically organized as a table where rows
represent profiles and columns the various metrics (e.g. startup time). Each
field of the table contains a list of values, one per Telemetry submission
received for that profile. [...]

The current version of the longitudinal dataset has been build with all
main pings received from 1% of profiles across all channels with [...] up to
180 days of data.

Table structure

To get an overview of the longitudinal data table:

DESCRIBE longitudinal

That table has a row for each client, with columns for the different parts of the ping. There are a lot of fields here, so I recommend downloading the results as a CSV if you want to search through these fields. Unfortunately, there's no way to filter the output of DESCRIBE in Presto.

Because this table combines all rows for a given client id, most columns contain either Arrays or Maps (described below). A few properties are directly available to query on:

SELECT count(*) AS count
FROM longitudinal
WHERE os = 'Linux'


Most properties are arrays, which contain one entry for each submission from a given client (newest first). Note that indexing starts at 1:

SELECT reason[1] AS newest_reason
FROM longitudinal
WHERE os = 'Linux'

To expand arrays and maps and work on the data row-wise we can use UNNEST(array).

WITH lengths AS
  (SELECT os, greatest(-1, least(31, sl / (24*60*60))) AS days
   FROM longitudinal
   CROSS JOIN UNNEST(session_length, reason) AS t(sl, r)
   WHERE r = 'shutdown' OR r = 'aborted-session')
SELECT os, days, count(*) AS count
FROM lengths
GROUP BY days, os ORDER BY days ASC

However, it may be better to use a sample from the main_summary table instead.



Some fields like active_addons or user_prefs are handled as maps, on which you can use the [] operator and special functions:

  (SELECT active_addons[1]['{d10d0bf8-f5b5-c8b4-a8b2-2b9879e08c5d}']
            IS NOT null AS has_adblockplus
   FROM longitudinal)
SELECT has_adblockplus, count(*) AS count



While composing queries, it can be helpful to work on small samples to reduce query runtime:

SELECT * FROM longitudinal LIMIT 1000 ...

There's no need to use other sampling methods, such as TABLESAMPLE, on the longitudinal set. Rows are randomly ordered, so a LIMIT sample is expected to be random.

Example Queries

Blocklist URLs (extensions.blocklist.url)

  (SELECT element_at(settings, 1).user_prefs['extensions.blocklist.url'] AS bl
   FROM longitudinal)

Blocklist enabled/disabled (extensions.blocklist.enabled) count:

  (SELECT element_at(settings, 1).blocklist_enabled AS bl
   FROM longitudinal)

Parsing most recent submission_date

SELECT DATE_PARSE(submission_date[1], '%Y-%m-%dT00:00:00.000Z') as parsed_submission_date
FROM longitudinal

Limiting to most recent ping in the last 7 days

SELECT * FROM longitudinal
WHERE DATE_DIFF('day', DATE_PARSE(submission_date[1], '%Y-%m-%dT00:00:00.000Z'), current_date) < 7

Scalar measurement (how many users with more than 100 tabs)

WITH samples AS
   normalized_channel as channel,
   mctc.value AS max_concurrent_tabs
  FROM longitudinal
  CROSS JOIN UNNEST(scalar_parent_browser_engagement_max_concurrent_tab_count) as t (mctc)
   scalar_parent_browser_engagement_max_concurrent_tab_count is not null and
   mctc.value is not null and
   normalized_channel = 'nightly')
SELECT approx_distinct(client_id) FROM samples WHERE max_concurrent_tabs > 100

Keyed scalars

Retrieve all the keys for a given scalar and sum all values for each key giving one row per key:

SELECT t.key as open_type,
       SUM(REDUCE(t.val, 0, (s, x) -> s + COALESCE(x.value, 0), s -> s)) as open_count,
       normalized_channel AS "channel::multi-filter"
FROM longitudinal
CROSS JOIN UNNEST(scalar_parent_devtools_responsive_open_trigger) AS t(key, val)
GROUP BY t.key, normalized_channel

This query also makes use of multi-filter to show an interactive filter in Re:dash.

This query requires a modern version of Presto, and because of this it currently with the Presto data source but it doesn't work with the Athena data source.

Using Views

If you find yourself copy/pasting SQL between different queries, consider using a Presto VIEW to allow for code reuse. Views create logical tables which you can reuse in other queries. For example, this view defines some important filters and derived variables which are then used in this downstream query.

You can define a view by prefixing your query with


Be careful not to overwrite an existing view! Using a unique name is important.

Find more information here.

Working offline

It's often useful to keep a local sample of the longitudinal data when prototyping an analysis. The data is stored in s3://telemetry-parquet/longitudinal/. Once you have AWS credentials you can copy a shard of the parquet dataset to a local directory using aws s3 cp [filename] .

To request AWS credentials, see this page. To initialize your AWS credentials, try aws configure


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