שאילתות בנושא ייצוא נתוני משתמשים ב-Google Analytics

השאילתות לדוגמה בדף הזה חלות על ייצוא נתוני המשתמשים ב-BigQuery עבור ב-Google Analytics. בייצוא נתוני המשתמשים ב-BigQuery נוצרת שתי טבלאות לכל day:

1. הטבלה users_YYYYMMDD, שמכילה שורה לכל מזהה ��שתמש השתנה.
2. טבלה pseudonymous_users_YYYYMMDD, שמכילה שורה לכל מזהה פסאודונימי שהשתנה..

מידע על נתוני המשתמשים ב-BigQuery Export של לפרטים נוספים.

שאילתה לגבי טווח תאריכים ספציפי

כדי לשלוח שאילתה על טווח תאריכים ספציפי מתוך מערך נתונים של ייצוא נתוני משתמשים ב-BigQuery, צריך להשתמש בפונקציה _TABLE_SUFFIX עמודת פסאודו בקטע WHERE של השאילתה.

לדוגמה, השאילתה הבאה סופרת את מספר המשתמשים הייחודיים שעודכנו. בין 1 באוגוסט 2023 ל-15 באוגוסט 2023, כאשר משך החיים הוא לפחות חמש דקות.

-- 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;

-- 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;

כל דוגמה מגבילה את הנתונים ל-1 באוגוסט 2023 עד 15 באוגוסט 2023 באמצעות שתי תכונות:

1. התו הכללי לחיפוש 202308* בסעיף FROM.
2. תנאי _TABLE_SUFFIX בסעיף WHERE שמסננים לפי טבלאות ב��לק של התו הכללי לחיפוש בשם הטבלה. עבור התו הכללי לחיפוש 202308*, החלק של התו הכללי לחיפוש הוא היום בחודש.

אפשר להשתמש בשיטה דומה כדי להריץ שאילתות על נתונים במשך כמה חודשים. לדוגמה, כדי שאילתה מינואר עד אוקטובר 2023, משנים את השאילתה כך:

1. התו הכללי לחיפוש 2023*.
2. תנאי _TABLE_SUFFIX של _TABLE_SUFFIX BETWEEN '0101' AND '1031'.

אתם יכולים גם להריץ שאילתות על נתונים במשך כמה שנים. לדוגמה, כדי לשלוח שאילתה באוקטובר 2022 עד פברואר 2023, משנים את השאילתה כך שתהיה:

1. התו הכללי לחיפוש 202*.
2. תנאי _TABLE_SUFFIX של _TABLE_SUFFIX BETWEEN '21001' AND '30331'.

מזהי משתמשים של שינויים שבוצעו לאחרונה בנכסי המשתמשים

השאילתה הבאה מראה איך לאחזר את הנתונים של user_id ו-pseudo_user_id כל המשתמשים ששינו לאחרונה מאפיין משתמש מסוים.

-- 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;

-- 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;

סיכום העדכונים

אפשר להשתמש בשאילתה הזו כדי להבין למה ייצוא של נתוני המשתמשים נכלל או הוחרג קטגוריות שונות של משתמשים.

-- 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;

-- 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;