인프런 커뮤니티 질문&답변

#1

SELECT 
  user_id,
  visit_month,
  LEAD(visit_month,1) OVER(PARTITION BY user_id ORDER BY visit_month) AS next_month, 
  LEAD(visit_month,2) OVER(PARTITION BY user_id ORDER BY visit_month) AS the_month_after_next
FROM 
  `avdanced.analytics_function_01`

#2

SELECT 
  user_id,
  visit_month,
  LEAD(visit_month,1) OVER(PARTITION BY user_id ORDER BY visit_month) AS next_month, 
  LEAD(visit_month,2) OVER(PARTITION BY user_id ORDER BY visit_month) AS the_month_after_next,
  LAG(visit_month,1) OVER(PARTITION BY user_id ORDER BY visit_month) AS last_month
FROM 
  `avdanced.analytics_function_01` 

Frame

SELECT 
  SUM(amount) OVER() AS amount_total,
  #SUM(amount) OVER(partition by order_date) AS cumulative_sum,
  SUM(amount) OVER (ORDER BY order_date) AS cumulative_sum,
  #SUM(amount) OVER(partition by user_id) AS cumulative_sum_by_user,
  SUM(amount) OVER(partition by user_id ORDER BY order_id) AS cumulative_sum_by_user,
  AVG(amount) OVER(ROWS BETWEEN 5 PRECEDING AND 1 PRECEDING) AS last_5_orders_avg_amount, 
  AVG(amount) OVER(ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING) AS last_5_unbounded_orders_avg_amount, 
  AVG(amount) OVER(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS all_orders_avg_amount
FROM `avdanced.orders` 

2)

#1

SELECT
  user,
  team,
  query_date,
  count(user) over(PARTITION BY user) AS total_query_cnt
FROM
  advanced.query_logs
ORDER BY
  query_date, 
  user

#2

WITH query_cnt_by_team AS (
  SELECT 
    EXTRACT(WEEK FROM query_date) AS week_number, 
    team, 
    user, 
    COUNT(user) AS query_cnt
  FROM advanced.query_logs
  GROUP BY ALL

)

SELECT 
  *, 
  RANK() OVER (PARTITION BY week_number, team ORDER BY query_cnt DESC) AS rk
FROM query_cnt_by_team
QUALIFY rk = 1
ORDER BY 
  week_number, 
  team, 
  query_cnt DESC

#3

WITH query_cnt_by_team AS (
  SELECT 
    EXTRACT(WEEK FROM query_date) AS week_number, 
    team, 
    user, 
    COUNT(user) AS query_cnt
  FROM advanced.query_logs
  GROUP BY ALL

)

SELECT 
  *, 
  LAG(query_cnt, 1) OVER (PARTITION BY user ORDER BY week_number) AS prev_week_query_cnt
FROM query_cnt_by_team
ORDER BY 
  user, 
  week_number

#4

SELECT 
  query_date, 
  team, 
  user, 
  query_cnt, 
  SUM(query_cnt) OVER (PARTITION BY user ORDER BY query_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_sum

FROM (
  SELECT 
    query_date, 
    team, 
    user, 
    COUNT(user) AS query_cnt  
  FROM advanced.query_logs  
  GROUP BY ALL
)

ORDER BY 
    user, 
    query_date

#5

WITH  raw_data  AS(
  SELECT  DATE'2024-05-01'AS date,15 AS number_of_orders UNION ALL
  SELECT  DATE'2024-05-02', 13 UNION ALL
  SELECT  DATE'2024-05-03', NULL UNION ALL
  SELECT  DATE'2024-05-04', 16 UNION ALL
  SELECT  DATE'2024-05-05', NULL UNION ALL
  SELECT  DATE'2024-05-06', 18 UNION ALL
  SELECT  DATE'2024-05-07', 20 UNION ALL
  SELECT  DATE'2024-05-08', NULL UNION ALL
  SELECT  DATE'2024-05-09', 13 UNION ALL
  SELECT  DATE'2024-05-10', 14 UNION ALL
  SELECT  DATE'2024-05-11', NULL UNION ALL
  SELECT  DATE'2024-05-12', NULL
)

SELECT
  date,
  IF(number_of_orders is null , last_value(number_of_orders IGNORE NULLS) OVER(ORDER BY date asc), number_of_orders) AS number_of_orders_not_null
FROM
  raw_data;

#6

WITH  raw_data  AS(
  SELECT  DATE'2024-05-01'AS date,15 AS number_of_orders UNION ALL
  SELECT  DATE'2024-05-02', 13 UNION ALL
  SELECT  DATE'2024-05-03', NULL UNION ALL
  SELECT  DATE'2024-05-04', 16 UNION ALL
  SELECT  DATE'2024-05-05', NULL UNION ALL
  SELECT  DATE'2024-05-06', 18 UNION ALL
  SELECT  DATE'2024-05-07', 20 UNION ALL
  SELECT  DATE'2024-05-08', NULL UNION ALL
  SELECT  DATE'2024-05-09', 13 UNION ALL
  SELECT  DATE'2024-05-10', 14 UNION ALL
  SELECT  DATE'2024-05-11', NULL UNION ALL
  SELECT  DATE'2024-05-12', NULL
),
filled_data AS (

  SELECT 
    * EXCEPT(number_of_orders), 
    LAST_VALUE(number_of_orders IGNORE NULLS) OVER (ORDER BY date) AS number_of_orders
  FROM raw_data

)

SELECT 
  *, 
  AVG(number_of_orders) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg
FROM filled_data

#7

WITH base AS(
  SELECT 
    event_date, 
    event_timestamp, 
    DATETIME(TIMESTAMP_MICROS(event_timestamp), 'Asia/Seoul') AS event_datetime, 
    event_name, 
    user_id, 
    user_pseudo_id, 
    DATETIME(TIMESTAMP_MICROS(LAG(event_timestamp) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp)), 'Asia/Seoul') AS before_event_datetime
  FROM advanced.app_logs
  WHERE event_date = '2022-08-18'
    AND user_pseudo_id = '1997494153.8491999091'
),


session_info AS(
  SELECT 
    *, 
    TIMESTAMP_DIFF(event_datetime, before_event_datetime, SECOND) AS second_diff, 
    CASE 
      WHEN TIMESTAMP_DIFF(event_datetime, before_event_datetime, SECOND) >= 20 
      OR TIMESTAMP_DIFF(event_datetime, before_event_datetime, SECOND) IS NULL THEN 1 
      ELSE NULL 
    END AS session_start
  FROM base
)

SELECT 
  *, 
  SUM(session_start) OVER (PARTITION BY user_pseudo_id ORDER BY event_datetime) AS session_id
FROM session_info
ORDER BY event_date, event_timestamp;