Sql 我会帮你的。它更简单、更便宜、更快，而且希望更易于维护。

我在

它应该会起作用：）

我在上找到了这个查询

它应该会起作用：）

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您好，欢迎来到Stack Overflow，请花点时间浏览“了解您在这里的道路”（以及赢得您的第一个徽章），阅读如何创建示例并进行检查，以增加获得反馈和有用答案的机会。您好，欢迎来到Stack Overflow，请花点时间浏览“了解您在这里的方式”（以及赢得您的第一枚徽章），阅读如何创建示例，并进行检查，以增加获得反馈和有用答案的机会。谢谢Mikhail！这让我尝到了味道。我已经提出了我的问题（或者我必须在上面提到的地方），如果你能查出来并让我知道你的想法？谢谢你的回复！谢谢你，米哈伊尔！这让我尝到了味道。我已经提出了我的问题（或者我必须在上面提到的地方），如果你能查出来并让我知道你的想法？谢谢你的回复！这个查询是正确的，但它只会给你一个对24日采取行动的群体的分析。在他的问题中，用户要求提供一些可以同时对几个队列进行分析的信息。您可以通过在步骤1的子查询中添加日期来改进此查询，以及在按天加入参与的用户时，包括参与的用户队列的日期（来自新的用户队列）。此查询是正确的，但它只会为您分析在24日执行操作的队列。在他的问题中，用户要求提供一些可以同时对几个队列进行分析的信息。您可以通过在步骤1的子查询中添加日期，以及按天加入参与的用户时，包括参与的用户队列的日期（来自新的用户队列），来改进此查询。

`WITH
cohort_items AS (
SELECT 
MIN( TIMESTAMP_TRUNC(TIMESTAMP_MICROS((visitStartTime*1000000 + 
h.time*1000)), DAY) ) AS cohort_day, fullVisitorID
FROM
TABLE123 AS U,
UNNEST(hits) AS h
WHERE _TABLE_SUFFIX BETWEEN "20170701" AND "20170731"
AND 'ACTION TAKEN'
GROUP BY 2
),


user_activites AS (
SELECT
A.fullVisitorID,
DATE_DIFF(DATE(TIMESTAMP_TRUNC(TIMESTAMP_MICROS((visitStartTime*1000000 + h.time*1000)), DAY)), DATE(C.cohort_day), DAY) AS day_number 
FROM `Table123` A

LEFT JOIN cohort_items C ON A.fullVisitorID = C.fullVisitorID,
UNNEST(hits) AS h

WHERE
A._TABLE_SUFFIX BETWEEN "20170701 AND "20170731"

AND 'ACTION TAKEN'
GROUP BY 1,2),

cohort_size AS (
SELECT 
cohort_day,
count(1) as number_of_users
FROM 
cohort_items
GROUP BY 1
ORDER BY 1
),

retention_table AS (
SELECT
C.cohort_day,
A.day_number,
COUNT(1) AS number_of_users
FROM
user_activites A

LEFT JOIN cohort_items C ON A.fullVisitorID = C.fullVisitorID
GROUP BY 1,2
)


SELECT
B.cohort_day,
S.number_of_users as total_users,
B.day_number,
B.number_of_users  /  S.number_of_users as percentage
FROM retention_table B

LEFT JOIN cohort_size S ON B.cohort_day = S.cohort_day

WHERE B.cohort_day IS NOT NULL
ORDER BY 1, 3
`

SELECT
  init_date,
  ARRAY((SELECT AS STRUCT days, freq, ROUND(freq * 100 / MAX(freq) OVER(), 2) FROM UNNEST(data) ORDER BY days)) data
FROM(
  SELECT
  init_date,
  ARRAY_AGG(STRUCT(days, freq)) data
FROM(
  SELECT
    init_date,
    data AS days,
    COUNT(data) freq
FROM(
  SELECT
    init_date,
    ARRAY(SELECT DATE_DIFF(PARSE_DATE("%Y%m%d", dts), PARSE_DATE("%Y%m%d", init_date), DAY) AS dt FROM UNNEST(dts) dts) data
  FROM(
    SELECT 
      MIN(date) init_date,
      ARRAY_AGG(DISTINCT date) dts
    FROM `Table123`
    WHERE TRUE
    AND EXISTS(SELECT 1 FROM UNNEST(hits) where eventinfo.eventCategory = 'recommendation') -- This is your 'ACTION TAKEN' filter
    AND _TABLE_SUFFIX BETWEEN "20170724" AND "20170731"
    GROUP BY fullvisitorid
    )
    ),
    UNNEST(data) data
    GROUP BY init_date, days
   )
  GROUP BY init_date
)

#standardSQL

###################################################
# Part 1: Cohort of New Users Starting on DEC 24
###################################################
WITH 
new_user_cohort AS (
  SELECT DISTINCT
    user_pseudo_id as new_user_id
  FROM
    `[your_project].[your_firebase_table].events_*`
  WHERE
    event_name =  `[chosen_event] ` AND
    #set the date from when starting cohort analysis
    FORMAT_TIMESTAMP("%Y%m%d", TIMESTAMP_TRUNC(TIMESTAMP_MICROS(event_timestamp), DAY, "Etc/GMT+1")) = '20191224' AND
    _TABLE_SUFFIX BETWEEN '20191224' AND '20191230'
),

num_new_users AS (
  SELECT count(*) as num_users_in_cohort FROM new_user_cohort
),

#############################################
# Part 2: Engaged users from Dec 24 cohort
#############################################
engaged_users_by_day AS (
  SELECT
    FORMAT_TIMESTAMP("%Y%m%d", TIMESTAMP_TRUNC(TIMESTAMP_MICROS(event_timestamp), DAY, "Etc/GMT+1")) as event_day,
    COUNT(DISTINCT user_pseudo_id) as num_engaged_users
  FROM
    `[your_project].[your_firebase_table].events_*`
  INNER JOIN
    new_user_cohort ON new_user_id = user_pseudo_id
  WHERE
    event_name = 'user_engagement' AND
    _TABLE_SUFFIX BETWEEN '20191224' AND '20191230'
  GROUP BY
    event_day
)


####################################################################
# Part 3: Daily Retention = [Engaged Users / Total Users]
####################################################################
SELECT
  event_day,
  num_engaged_users,
  num_users_in_cohort,
  ROUND((num_engaged_users / num_users_in_cohort), 3) as retention_rate
FROM
  engaged_users_by_day
CROSS JOIN
  num_new_users
ORDER BY
  event_day