인프런 커뮤니티 질문&답변

1. ARRAY, STRUCT

1) array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요

SELECT
  title,
  genre
FROM advanced.array_exercises
CROSS JOIN UNNEST(genres) AS genre

2) array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요. (배우와 배역은 별도의 컬럼으로 나와야 합니다)

SELECT
  title,
  actor.actor,
  actor.character
FROM advanced.array_exercises
CROSS JOIN UNNEST(actors) AS actor

3) array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르(genre)를 출력하세요. 한 Row에 배우, 배역, 장르가 모두 표시되어야 합니다

SELECT
  title,
  actor.actor,
  actor.character,
  genre
FROM advanced.array_exercises
CROSS JOIN UNNEST(actors) AS actor
CROSS JOIN UNNEST(genres) AS genre

4) 앱 로그 데이터(app_logs)의 배열을 풀어주세요

SELECT
  user_id,
  event_date,
  event_name,
  user_pseudo_id,
  event_param.key AS key,
  event_param.value.string_value AS string_value,
  event_param.value.int_value AS int_value
FROM advanced.app_logs
CROSS JOIN UNNEST(event_params) AS event_param
WHERE
  event_date = '2022-08-01'

2. PIVOT

1) orders 테이블에서 유저(user_id)별로 주문 금액(amount)의 합계를 PIVOT해주세요. 날짜(order_date)를 행(Row)으로, user_id를 열(Column)으로 만들어야 합니다

SELECT
  order_date,
  SUM(IF(user_id = 1, amount, 0)) AS user_1,
  SUM(IF(user_id = 2, amount, 0)) AS user_2,
  SUM(IF(user_id = 3, amount, 0)) AS user_3,
FROM advanced.orders
GROUP BY
  order_date
ORDER BY
  order_date 

2) orders 테이블에서 날짜(order_date)별로 유저들의 주문 금액(amount)의 합계를 PIVOT 해주세요. user_id를 행(Row)으로, order_date를 열(Column)으로 만들어야 합니다

-- 2) orders 테이블에서 날짜(order_date)별로 유저들의 주문 금액(amount)의 합계를 PIVOT 해주세요. user_id를 행(Row)으로, order_date를 열(Column)으로 만들어야 합니다
SELECT
  user_id,
  SUM(IF(order_date = '2023-05-01', amount, 0)) AS `2023-05-01`,
  SUM(IF(order_date = '2023-05-02', amount, 0)) AS `2023-05-02`,
  SUM(IF(order_date = '2023-05-03', amount, 0)) AS `2023-05-03`,
  SUM(IF(order_date = '2023-05-04', amount, 0)) AS `2023-05-04`,
  SUM(IF(order_date = '2023-05-05', amount, 0)) AS `2023-05-05`,
FROM advanced.orders
GROUP BY
  user_id
ORDER BY
  user_id

3) orders 테이블에서 사용자(user_id)별, 날짜(order_date)별로 주문이 있다면 1, 없다면 0으로 PIVOT 해주세요. user_id를 행(Row)으로, order_date를 열(Column)로 만들고 주문을 많이 해도 1로 처리합니다

SELECT
  user_id,
  MAX(IF(order_date = '2023-05-01', 1, 0)) AS `2023-05-01`,
  MAX(IF(order_date = '2023-05-02', 1, 0)) AS `2023-05-02`,
  MAX(IF(order_date = '2023-05-03', 1, 0)) AS `2023-05-03`,
  MAX(IF(order_date = '2023-05-04', 1, 0)) AS `2023-05-04`,
  MAX(IF(order_date = '2023-05-05', 1, 0)) AS `2023-05-05`,
FROM advanced.orders
GROUP BY
  user_id
ORDER BY
  user_id

4) 앱 로그 데이터 배열 PIVOT하기

# 앱 로그 PIVOT
# 쿼리를 작성하는 목표, 확인할 지표 : user_id = 32888이 카트 추가하기(click_cart)를 누를때 어떤 음식(food_id)을 담았나요?
# 쿼리 계산 방법 : UNNEST -> PIVOT
# 데이터의 기간 : X
# 사용할 테이블 : app_logs
# Join KEY : X
# 데이터 특징: 
  -- event_params ARRAY, STRUCT / event_params.value ARRAY, STRUCT

WITH base AS (
  SELECT
    user_id,
    event_date,
    event_name,
    user_pseudo_id,
    event_param.key AS key,
    event_param.value.string_value AS string_value,
    event_param.value.int_value AS int_value
  FROM advanced.app_logs
  CROSS JOIN UNNEST(event_params) AS event_param
)

SELECT
  user_id,
  event_date,
  event_name,
  user_pseudo_id,
  MAX(IF(key = 'firebase_screen', string_value, NULL)) AS firebase_screen,
  MAX(IF(key = 'food_id', int_value, NULL)) AS food_id,
  MAX(IF(key = 'session_id', string_value, NULL)) AS session_id
FROM base
WHERE 
 user_id = 32888
 AND event_name = 'click_cart'
GROUP BY ALL

3. 퍼널

WITH base AS (
  SELECT
    event_date,
    event_timestamp,
    event_name,
    user_id,
    user_pseudo_id,
    platform,
    MAX(IF(event_param.key = 'firebase_screen', event_param.value.string_value, NULL)) AS firebase_screen,
    MAX(IF(event_param.key = 'session_id', event_param.value.string_value, NULL)) AS session_id
  FROM advanced.app_logs
  CROSS JOIN UNNEST(event_params) AS event_param
  WHERE
    event_date BETWEEN '2022-08-01' AND '2022-08-18'
  GROUP BY ALL
), filter_event_and_concat_event_and_screen AS (
  SELECT
    * EXCEPT(event_name, firebase_screen, event_timestamp),
    CONCAT(event_name, "-", firebase_screen) AS event_name_with_screen,
    DATETIME(TIMESTAMP_MICROS(event_timestamp), 'Asia/Seoul') AS event_datetime
  FROM base
  WHERE
    event_name IN ('screen_view', 'click_payment')
), daily_event_summary AS (
  SELECT
    event_date, # 일자별로 퍼널별 유저 수 쿼리
    event_name_with_screen,
    CASE
      WHEN event_name_with_screen = 'screen_view-welcome' THEN 1
      WHEN event_name_with_screen = 'screen_view-home' THEN 2
      WHEN event_name_with_screen = 'screen_view-food_category' THEN 3
      WHEN event_name_with_screen = 'screen_view-restaurant' THEN 4
      WHEN event_name_with_screen = 'screen_view-cart' THEN 5
      WHEN event_name_with_screen = 'click_payment-cart' THEN 6
    ELSE NULL
    END AS step_number,
    COUNT(DISTINCT user_pseudo_id) AS cnt
  FROM filter_event_and_concat_event_and_screen
  GROUP BY ALL
  HAVING step_number IS NOT NULL
)

-- 집계한 데이터 PIVOT
SELECT
  event_date,
  MAX(IF(event_name_with_screen = 'screen_view-welcome', cnt, 0)) AS screen_view_welcome,
  MAX(IF(event_name_with_screen = 'screen_view-home', cnt, 0)) AS screen_view_home,
  MAX(IF(event_name_with_screen = 'screen_view-food_category', cnt, 0)) AS screen_view_food_category,
  MAX(IF(event_name_with_screen = 'screen_view-restaurant', cnt, 0)) AS screen_view_restaurant,
  MAX(IF(event_name_with_screen = 'screen_view-cart', cnt, 0)) AS screen_view_cart,
  MAX(IF(event_name_with_screen = 'click_payment-cart', cnt, 0)) AS click_payment_cart
FROM daily_event_summary
GROUP BY ALL 
ORDER BY event_date