6.9 KiB
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
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'
Arrays
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.
Links:
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:
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.
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
Scalar measurement (how many users with more than 100 tabs)
WITH samples AS
(SELECT
client_id,
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)
WHERE
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
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.
Working offline
It's often useful to keep a local sample of the l10l 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 cp [filename] .
To request AWS credentials, see this
page.
To initiate your AWS config, try aws configure
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.