이 페이지의 샘플 쿼리는 Google 애널리틱스. BigQuery 사용자 데이터 내보내기를 실행하면 각각의 날에 두 개의 테이블이 생성됩니다.
users_YYYYMMDD
테이블 - 변경된 모든 사용자 ID가 표시되는 행이 있음pseudonymous_users_YYYYMMDD
테이블 - 변경된 모든 가명 식별자가 표시되는 행이 있음
자세한 내용은 BigQuery 사용자 데이터 내보내기 스키마를 참고하세요.
특정 기간 쿼리하기
BigQuery 사용자 데이터 가져오기 데이터 세트에서 특정 기간을 쿼리하려면 쿼리의 WHERE
절에 _TABLE_SUFFIX
의사 열을 사용합니다.
예를 들어 다음 쿼리는 전체 기간 중 참여 시간이 5분 이상인 사용자 중 2023년 8월 1일부터 2023년 8월 15일 사이에 업데이트된 순 사용자 수를 집계합니다.
users
-- 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;
각 예는 다음 두 기능을 사용하여 데이터 기간을 2023년 8월 1일~2023년 8월 15일로 제한합니다.
FROM
절의202308*
와일드카드- 테이블 이름 중 와일드카드 부분을 토대로 테이블을 필터링하는
WHERE
절의_TABLE_SUFFIX
조건.202308*
와일드카드의 경우, 와일드카드 부분이 그 달의 날짜입니다.
여러 달의 데이터를 쿼리할 때도 비슷한 방법을 사용할 수 있습니다. 예를 들어 2023년 1월부터 10월의 데이터를 쿼리하려면 쿼리를 다음과 같이 수정합니다.
2023*
와일드카드_TABLE_SUFFIX BETWEEN '0101' AND '1031'
의_TABLE_SUFFIX
조건
여러 해의 데이터를 쿼리할 수도 있습니다. 예를 들어 2022년 10월부터 2023년 2월까지의 데이터를 쿼리하려면 쿼리를 다음과 같이 수정합니다.
- 와일드카드
202*
_TABLE_SUFFIX BETWEEN '21001' AND '30331'
절의_TABLE_SUFFIX
조건
최근 사용자 속성 변경과 관련한 사용자 ID
다음 쿼리는 최근에 특정 사용자 속성을 변경한 모든 사용자의 user_id
및 pseudo_user_id
를 가져오는 방법을 보여줍니다.
users
-- 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;
업데이트 요약
사용자 데이터 가져오기에 각기 다른 범주의 사용자가 포함 또는 제외된 이유를 알아보려면 이 쿼리를 사용합니다.
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 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;