Die Beispielabfragen auf dieser Seite gelten für den BigQuery-Nutzerdatenexport für Google Analytics Beim BigQuery-Nutzerdatenexport werden für jede Tag:
- Eine
users_YYYYMMDD-Tabelle, die eine Zeile für jede Nutzer-ID enthält, die geändert. - Eine
pseudonymous_users_YYYYMMDD-Tabelle, die eine Zeile für jede eine pseudonymisierte Kennung, die sich geändert hat.
Unter BigQuery Export-Nutzerdaten -Schema.
Einen bestimmten Zeitraum abfragen
Um einen bestimmten Zeitraum aus einem BigQuery-Dataset für den Datenexport eines BigQuery-Nutzers abzufragen, verwenden Sie die Methode
_TABLE_SUFFIX
Pseudospalte in der WHERE-Klausel Ihrer Abfrage verwenden.
Die folgende Abfrage zählt beispielsweise die Anzahl der einzelnen Nutzer, zwischen 1. und 15. August 2023 mit einer Gesamtdauer der Interaktionen von mindestens fünf Minuten.
Nutzer
-- Example: Query a specific date range for users meeting a lifetime engagement criterion.
--
-- Counts unique users that are in the BigQuery user-data exports for a specific date range and have
-- a lifetime engagement of 5 minutes or more.
SELECT
COUNT(DISTINCT user_id) AS user_count
FROM
-- Uses a table suffix wildcard to define the set of daily tables to query.
`PROJECT_ID.analytics_PROPERTY_ID.users_202308*`
WHERE
-- Filters to users updated between August 1 and August 15.
_TABLE_SUFFIX BETWEEN '01' AND '15'
-- Filters by users who have a lifetime engagement of 5 minutes or more.
AND user_ltv.engagement_time_millis >= 5 * 60 * 1000;
pseudonymous_users
-- Example: Query a specific date range for users meeting a lifetime engagement criterion.
--
-- Counts unique pseudonymous users that are in the BigQuery user-data exports for a specific date
-- range and have a lifetime engagement of 5 minutes or more.
SELECT
COUNT(DISTINCT pseudo_user_id) AS pseudo_user_count
FROM
-- Uses a table suffix wildcard to define the set of daily tables to query.
`PROJECT_ID.analytics_PROPERTY_ID.pseudonymous_users_202308*`
WHERE
-- Filters to users updated between August 1 and August 15.
_TABLE_SUFFIX BETWEEN '01' AND '15'
-- Filters by users who have a lifetime engagement of 5 minutes or more.
AND user_ltv.engagement_time_millis >= 5 * 60 * 1000;
In jedem Beispiel sind die Daten auf den Zeitraum vom 1. bis zum 15. August 2023 beschränkt, indem zwei Funktionen:
- Der Platzhalter
202308*in derFROM-Klausel. - Eine
_TABLE_SUFFIX-Bedingung in derWHERE-Klausel, die Tabellen basierend auf im Platzhalterteil des Tabellennamens. Für den Platzhalter202308*ist der Platzhalterteil der Tag des Monats.
Sie können einen ähnlichen Ansatz verwenden, um die Daten mehrerer Monate abzufragen. Wenn Sie beispielsweise von Januar bis Oktober 2023 abrufen möchten, ändern Sie die Abfrage so:
- Den Platzhalter
2023*. - Eine
_TABLE_SUFFIX-Bedingung von_TABLE_SUFFIX BETWEEN '0101' AND '1031'.
Sie können auch Daten aus mehreren Jahren abfragen. Wenn Sie beispielsweise Oktober 2022 abfragen möchten, bis Februar 2023 haben, ändern Sie die Abfrage so, dass Folgendes angezeigt wird:
- Den Platzhalter
202*. - Eine
_TABLE_SUFFIX-Bedingung von_TABLE_SUFFIX BETWEEN '21001' AND '30331'.
Nutzer-IDs für kürzlich vorgenommene Änderungen an Nutzereigenschaften
Die folgende Abfrage zeigt, wie user_id und pseudo_user_id von
Alle Nutzer, die vor Kurzem eine bestimmte Nutzereigenschaft geändert haben.
Nutzer
-- Example: Get the list of user_ids with recent changes to a specific user property.
DECLARE
UPDATE_LOWER_BOUND_MICROS INT64;
-- Replace timezone. List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
DECLARE
REPORTING_TIMEZONE STRING DEFAULT 'America/Los_Angeles';
-- Sets the variable for the earliest update time to include. This comes after setting
-- the REPORTING_TIMEZONE so this expression can use that variable.
SET UPDATE_LOWER_BOUND_MICROS = UNIX_MICROS(
TIMESTAMP_SUB(
TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), DAY, REPORTING_TIMEZONE),
INTERVAL 14 DAY));
-- Selects users with changes to a specific user property since the lower bound.
SELECT
users.user_id,
FORMAT_TIMESTAMP('%F %T',
TIMESTAMP_MICROS(
MAX(properties.value.set_timestamp_micros)),
REPORTING_TIMEZONE) AS max_set_timestamp
FROM
-- Uses a table prefix to scan all data for 2023. Update the prefix as needed to query a different
-- date range.
`PROJECT_ID.analytics_PROPERTY_ID.users_2023*` AS users,
users.user_properties properties
WHERE
properties.value.user_property_name = 'job_function'
AND properties.value.set_timestamp_micros >= UPDATE_LOWER_BOUND_MICROS
GROUP BY
1;
pseudonymous_users
-- Example: Get the list of pseudo_user_ids with recent changes to a specific user property.
DECLARE
UPDATE_LOWER_BOUND_MICROS INT64;
-- Replace timezone. List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
DECLARE
REPORTING_TIMEZONE STRING DEFAULT 'America/Los_Angeles';
-- Sets the variable for the earliest update time to include. This comes after setting
-- the REPORTING_TIMEZONE so this expression can use that variable.
SET UPDATE_LOWER_BOUND_MICROS = UNIX_MICROS(
TIMESTAMP_SUB(
TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), DAY, REPORTING_TIMEZONE),
INTERVAL 14 DAY));
-- Selects users with changes to a specific user property since the lower bound.
SELECT
users.pseudo_user_id,
FORMAT_TIMESTAMP('%F %T',
TIMESTAMP_MICROS(
MAX(properties.value.set_timestamp_micros)),
REPORTING_TIMEZONE) AS max_set_timestamp
FROM
-- Uses a table prefix to scan all data for 2023. Update the prefix as needed to query a different
-- date range.
`PROJECT_ID.analytics_PROPERTY_ID.pseudonymous_users_2023*` AS users,
users.user_properties properties
WHERE
properties.value.user_property_name = 'job_function'
AND properties.value.set_timestamp_micros >= UPDATE_LOWER_BOUND_MICROS
GROUP BY
1;
Zusammenfassung der Updates
Verwenden Sie diese Abfrage, um zu verstehen, warum der Export von Nutzerdaten ein- oder ausgeschlossen wurde verschiedenen Kategorien von Nutzenden.
Nutzer
-- Summarizes data by change type.
-- Defines the export date to query. This must match the table suffix in the FROM
-- clause below.
DECLARE EXPORT_DATE DATE DEFAULT DATE(2023,6,16);
-- Creates a temporary function that will return true if a timestamp (in micros) is for the same
-- date as the specified day value.
CREATE TEMP FUNCTION WithinDay(ts_micros INT64, day_value DATE)
AS (
(ts_micros IS NOT NULL) AND
-- Change the timezone to your property's reporting time zone.
-- List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
(DATE(TIMESTAMP_MICROS(ts_micros), 'America/Los_Angeles') = day_value)
);
-- Creates a temporary function that will return true if a date string in 'YYYYMMDD' format is
-- for the same date as the specified day value.
CREATE TEMP FUNCTION SameDate(date_string STRING, day_value DATE)
AS (
(date_string IS NOT NULL) AND
(PARSE_DATE('%Y%m%d', date_string) = day_value)
);
WITH change_types AS (
SELECT user_id,
WithinDay(user_info.last_active_timestamp_micros, EXPORT_DATE) AS user_activity,
WithinDay(user_info.user_first_touch_timestamp_micros, EXPORT_DATE) AS first_touch,
SameDate(user_info.first_purchase_date, EXPORT_DATE) as first_purchase,
(EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
WHERE WithinDay(aud.membership_start_timestamp_micros, EXPORT_DATE))) AS audience_add,
(EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
WHERE WithinDay(aud.membership_expiry_timestamp_micros, EXPORT_DATE))) AS audience_remove,
(EXISTS (SELECT 1 FROM UNNEST(user_properties) AS prop
WHERE WithinDay(prop.value.set_timestamp_micros, EXPORT_DATE))) AS user_property_change
FROM
-- The table suffix must match the date used to define EXPORT_DATE above.
`project_id.analytics_property_id.users_20230616`
)
SELECT
user_activity,
first_touch,
first_purchase,
audience_add,
audience_remove,
user_property_change,
-- This field will be true if there are no changes for the other change types.
NOT (user_activity OR first_touch OR audience_add OR audience_remove OR user_property_change) AS other_change,
COUNT(DISTINCT user_id) AS user_id_count
FROM change_types
GROUP BY 1,2,3,4,5,6,7;
pseudonymous_users
-- Summarizes data by change type.
-- Defines the export date to query. This must match the table suffix in the FROM
-- clause below.
DECLARE EXPORT_DATE DATE DEFAULT DATE(2023,6,16);
-- Creates a temporary function that will return true if a timestamp (in micros) is for the same
-- date as the specified day value.
CREATE TEMP FUNCTION WithinDay(ts_micros INT64, day_value DATE)
AS (
(ts_micros IS NOT NULL) AND
-- Change the timezone to your property's reporting time zone.
-- List at https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.
(DATE(TIMESTAMP_MICROS(ts_micros), 'America/Los_Angeles') = day_value)
);
-- Creates a temporary function that will return true if a date string in 'YYYYMMDD' format is
-- for the same date as the specified day value.
CREATE TEMP FUNCTION SameDate(date_string STRING, day_value DATE)
AS (
(date_string IS NOT NULL) AND
(PARSE_DATE('%Y%m%d', date_string) = day_value)
);
WITH change_types AS (
SELECT pseudo_user_id,
WithinDay(user_info.last_active_timestamp_micros, EXPORT_DATE) AS user_activity,
WithinDay(user_info.user_first_touch_timestamp_micros, EXPORT_DATE) AS first_touch,
SameDate(user_info.first_purchase_date, EXPORT_DATE) as first_purchase,
(EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
WHERE WithinDay(aud.membership_start_timestamp_micros, EXPORT_DATE))) AS audience_add,
(EXISTS (SELECT 1 FROM UNNEST(audiences) AS aud
WHERE WithinDay(aud.membership_expiry_timestamp_micros, EXPORT_DATE))) AS audience_remove,
(EXISTS (SELECT 1 FROM UNNEST(user_properties) AS prop
WHERE WithinDay(prop.value.set_timestamp_micros, EXPORT_DATE))) AS user_property_change
FROM
-- The table suffix must match the date used to define EXPORT_DATE above.
`PROJECT_ID.analytics_PROPERTY_ID.pseudonymous_users_20230616`
)
SELECT
user_activity,
first_touch,
first_purchase,
audience_add,
audience_remove,
user_property_change,
-- This field will be true if there are no changes for the other change types.
NOT (user_activity OR first_touch OR audience_add OR audience_remove OR user_property_change) AS other_change,
COUNT(DISTINCT pseudo_user_id) pseudo_user_id_count
FROM change_types
GROUP BY 1,2,3,4,5,6,7;