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طلبات البحث لتصدير بيانات المستخدِمين في "إحصاءات Google 4"

تنطبق نماذج طلبات البحث في هذه الصفحة على عملية تصدير بيانات المستخدِمين إلى BigQuery لخدمة "إحصاءات Google 4". تُنشئ عملية تصدير بيانات المستخدمين في BigQuery جدولين لكل يوم:

جدول users_YYYYMMDD يحتوي على صف لكل رقم تعريف مستخدم تم تغييره.
جدول pseudonymous_users_YYYYMMDD يحتوي على صف لكل معرّف بيانات بدون صاحبه تم تغييره

اطّلِع على مخطط بيانات المستخدِمين في BigQuery Export للحصول على مزيد من التفاصيل.

طلب بحث في نطاق زمني محدّد

للاستعلام عن نطاق زمني محدّد من مجموعة بيانات تصدير بيانات المستخدِمين في BigQuery، استخدِم العمود الزائف _TABLE_SUFFIX في عبارة WHERE من طلب البحث.

على سبيل المثال، يحتسب طلب البحث التالي عدد المستخدمين الفريدين الذين تم تعديل بياناتهم في الفترة بين 1 آب (أغسطس) 2023 و15 آب (أغسطس) 2023 مع تفاعل لا يقل عن خمس دقائق.

المستخدمون

-- 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;

باستخدام كل مثال، يتم حصر البيانات في الفترة من 1 آب (أغسطس) 2023 إلى 15 آب (أغسطس) 2023 باستخدام ميزتَين:

حرف البدل 202308* في البند FROM
شرط _TABLE_SUFFIX في عبارة WHERE الذي يصفي الجداول استنادًا إلى جزء حرف البدل من اسم الجدول. بالنسبة إلى حرف البدل 202308*، يكون جزء حرف البدل هو يوم الشهر.

يمكنك استخدام نهج مشابه للاستعلام عن بيانات أشهر متعددة. على سبيل المثال، للاستعلام عن شهر كانون الثاني (يناير) حتى أكتوبر 2023، قم بتعديل الاستعلام ليتضمن ما يلي:

حرف البدل 2023*.
شرط _TABLE_SUFFIX لـ _TABLE_SUFFIX BETWEEN '0101' AND '1031'.

يمكنك أيضًا الاستعلام عن بيانات سنوات متعددة. على سبيل المثال، للاستعلام عن أكتوبر 2022 حتى فبراير 2023، قم بتعديل طلب البحث ليتضمن ما يلي:

حرف البدل 202*.
شرط _TABLE_SUFFIX لـ _TABLE_SUFFIX BETWEEN '21001' AND '30331'.

أرقام تعريف المستخدمين للتغييرات الأخيرة في خصائص المستخدمين

يعرض طلب البحث التالي كيفية استرداد user_id وpseudo_user_id لجميع المستخدمين الذين غيّروا خاصيّة مستخدم معيّنة مؤخرًا.

المستخدمون

-- 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;

ملخّص التعديلات

استخدِم طلب البحث هذا لفهم سبب تضمين أو استبعاد فئات مختلفة من المستخدمين في عملية تصدير بيانات المستخدمين.

المستخدمون

-- 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;

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