Telemetry/LongitudinalExamples

Introduction

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 deduping, 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

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 outputof DESCRIBE in Presto.

Some properties are directly available to query on:

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

Note that the longitudinal table is from a 1% sample of the clients. The sampling is based on the client id and stable over time.

Sampling

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

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.

Arrays

Some properties are arrays, which contain one entry for each submission from that client (newest first):

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

To expand arrays and maps and work on the data in them 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

Links:

• Documentation on array functions
• UNNEST documentation

Maps

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

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

Links:

• Documentation on map functions

Example Queries

• Blocklist URLs (extensions.blocklist.url):

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

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

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

• 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

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

CREATE OR REPLACE VIEW view_name AS ...

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

Find more information here.

FAQ

I'm getting an error, "... cannot be resolved"

For some reason, re:dash has trouble parsing SQL strings with double quotes. Try using single quotes instead.

Other Resources

• Presto Docs
• Helpful FAQ covering perf/distribution
• Longitudinal schema definition
• Custom_dashboards_with_re:dash