本頁中的查詢範例適用於 Google Analytics (分析) 4 的 BigQuery 使用者資料匯出功能。BigQuery 使用者資料匯出功能每天都會建立兩個資料表:
users_YYYYMMDD
資料表,其中包含每個已變更使用者 ID 的資料列。pseudonymous_users_YYYYMMDD
資料表,每個變更的匿名 ID 自成一列。
詳情請參閱 BigQuery Export 使用者資料結構定義。
查詢特定日期範圍
如要從 BigQuery 使用者資料匯出資料集中查詢特定日期範圍,請在查詢的 WHERE
子句中使用 _TABLE_SUFFIX
虛擬資料欄。
舉例來說,下列查詢會計算在 2023 年 8 月 1 日至 2023 年 8 月 15 日之間更新的不重複使用者人數,且生命週期參與率至少為五分鐘。
使用者
-- 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;
每個示例都使用下列兩項功能,將資料限制為 2023 年 8 月 1 日至 2023 年 8 月 15 日:
FROM
子句中的萬用字元202308*
。WHERE
子句中的_TABLE_SUFFIX
條件,可根據資料表名稱的萬用字元部分篩選資料表。202308*
的萬用字元部分是日期,
您可以使用類似方法查詢多個月的資料。舉例來說,如要查詢 2023 年 1 月到 10 月的資料,請將查詢修改為:
- 萬用字元
2023*
。 _TABLE_SUFFIX BETWEEN '0101' AND '1031'
的_TABLE_SUFFIX
條件。
也可以查詢數年的資料。舉例來說,如要查詢 2022 年 10 月到 2023 年 2 月的資料,請將查詢修改為:
- 萬用字元
202*
。 _TABLE_SUFFIX BETWEEN '21001' AND '30331'
的_TABLE_SUFFIX
條件。
近期使用者屬性變更的使用者 ID
以下查詢說明如何擷取最近變更特定使用者屬性的所有使用者的 user_id
和 pseudo_user_id
。
使用者
-- 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;
更新項目摘要
請使用這項查詢,瞭解系統為何要納入或排除不同使用者類別的使用者資料。
使用者
-- 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;