Search Clients Daily
Introduction
search_clients_daily is designed to enable client-level search analyses.
Querying this dataset can be slow;
consider using search_aggregates for coarse analyses.
Contents
search_clients_daily has one row for each unique combination of:
(client_id, submission_date, engine, source).
In addition to the standard search count aggregations,
this dataset includes some descriptive data for each client.
For example, we include country and channel for each row of data.
In the event that a client sends multiple pings on a given submission_date
we choose an arbitrary value from the pings for that (client_id, submission_date),
unless otherwise noted.
There are five standard search count aggregation columns:
sap, tagged-sap, and tagged-follow-on, organic and unknown.
Note that, if there were no such searches in a row's segment
(i.e. the count would be 0),
the column value is null.
Each of these columns represent different types of searches.
For more details, see the search data documentation
Background and Caveats
search_clients_daily does not include
(client_id submission_date) pairs
if we did not receive a ping for that submission_date.
We impute a NULL engine and source for pings with no search counts.
This ensures users who never search are included in this dataset.
This dataset is large.
Consider using Spark on Databricks.
If you're querying this dataset from re:dash,
heavily limit the data you read using submission_date or sample_id.
Accessing the Data
Access to search_clients_daily is heavily restricted.
You will not be able to access this table without additional permissions.
For more details see the search data documentation.
Data Reference
Example Queries
This query
calculates searches per normalized_channel for US clients on an arbitrary day.
If you have trouble viewing this query,
it's likely you don't have the proper permissions.
For more details see the search data documentation.
Scheduling
This dataset is scheduled on Airflow (source).
Schema
As of 2019-11-27, the current version of search_clients_daily is v8,
and has a schema as follows.
It's backfilled through 2016-03-12
root
|-- client_id: string (nullable = true)
|-- submission_date: date (nullable = true)
|-- submission_date_s3: date (nullable = true)
|-- engine: string (nullable = true)
|-- source: string (nullable = true)
|-- country: string (nullable = true)
|-- app_version: string (nullable = true)
|-- distribution_id: string (nullable = true)
|-- locale: string (nullable = true)
|-- search_cohort: string (nullable = true)
|-- addon_version: string (nullable = true)
|-- os: string (nullable = true)
|-- os_version: string (nullable = true)
|-- channel: string (nullable = true)
|-- profile_creation_date: long (nullable = true)
|-- default_search_engine: string (nullable = true)
|-- default_search_engine_data_load_path: string (nullable = true)
|-- default_search_engine_data_submission_url: string (nullable = true)
|-- default_private_search_engine: string (nullable = true)
|-- default_private_search_engine_data_load_path: string (nullable = true)
|-- default_private_search_engine_data_submission_url: string (nullable = true)
|-- sample_id: long (nullable = true)
|-- sessions_started_on_this_day: long (nullable = true)
|-- profile_age_in_days: integer (nullable = true)
|-- subsession_hours_sum: double (nullable = true)
|-- active_addons_count_mean: double (nullable = true)
|-- max_concurrent_tab_count_max: integer (nullable = true)
|-- tab_open_event_count_sum: long (nullable = true)
|-- active_hours_sum: double (nullable = true)
|-- total_uri_count: long (nullable = true)
|-- tagged-sap: long (nullable = true)
|-- tagged-follow-on: long (nullable = true)
|-- sap: long (nullable = true)
|-- tagged_sap: long (nullable = true)
|-- tagged_follow_on: long (nullable = true)
|-- organic: long (nullable = true)
|-- search_with_ads: long (nullable = true)
|-- ad_click: long (nullable = true)
|-- unknown: long (nullable = true)
Code Reference
The search_clients_daily job is
defined in bigquery-etl