UNNEST 1) array_exercises 테이블에서 각 영화(title)별로 장르를(genres) unnest 해서 보여주세요 SELECT title, genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS genre 2) array_exercieses 테이블에서 각 영화(title) 별로 배우 (actor)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야 합니다. SELECT title, actor_info.actor, actor_info.character, FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor_info; 3) array_exercises 테이블에서 각 영화(title) 별로 배우(actor), 배역(character), 장르(genre)를 출력하세요. 한 row 에 배우, 배역, 장르가 모두 표시되어야 합니다. SELECT title, actor_info.actor, actor_info.character, genre, FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor_info 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, params.key AS key, params.value.string_value AS string_value, params.value.int_value AS int_value, FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS params ORDER BY event_date; PIVOT 1) orders 테이블에서 유저(user_id)별로 주문금액(amount)의 합계를 PIVOT 해주세요 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 ASC 2) orders 테이블에서 날짜(order_date)별로 유저들의 주문금액(amount)의 합계를 PIVOT 해주세요. user_id를 행으로, order_date를 열으로 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 행으로, order_date를 열로 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) user_id = 32888 이 카트 추가하기 (click_cart)를 누를 때 어떤 음식(food_id)을 담았나요? WITH base AS ( SELECT event_date, event_name, user_pseudo_id, event_timestamp, user_id, MAX(IF(param.key = "firebase_screen", param.value.string_value, NULL)) AS firebase_screen, MAX(IF(param.key = "food_id", param.value.int_value, NULL)) AS food_id, MAX(IF(param.key = "session_id", param.value.string_value, NULL)) AS session_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS param WHERE event_date = "2022-08-01" GROUP BY ALL ) SELECT select_date, COUNT(user_id) AS user_cnt FROM base WHERE event_name = "click_cart" GROUP BY event_date 3. 퍼널 WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, platform, -- event_param MAX(IF(event_param.key = "firebase_screen", event_param.value.string_value, NULL)) AS firebase_screen, -- MAX(IF(event_param.key = "food_id", event_param.value.int_value, NULL)) AS food_id, 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 = "2022-08-01" # 적은 데이터로 쿼리를 작성하기 위해 만들어둔 조건 event_date BETWEEN "2022-08-01" AND "2022-08-18" GROUP BY ALL ), filter_event_and_concat_event_and_screen AS ( -- (1) event_name + screen (필요한 이벤트만 WHERE 조건에 걸어서 사용) 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") ), funnel AS ( SELECT event_date, 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 ) SELECT event_date, MAX(IF(funnel.event_name_with_screen = 'screen_view-welcome', cnt, null)) as `screen_view-welcome`, MAX(IF(funnel.event_name_with_screen = 'screen_view-home', cnt, null)) as `screen_view-home`, MAX(IF(funnel.event_name_with_screen = 'screen_view-food_category', cnt, null)) as `screen_view-food_category`, MAX(IF(funnel.event_name_with_screen = 'screen_view-restaurant', cnt, null)) as `screen_view-restaurant`, MAX(IF(funnel.event_name_with_screen = 'screen_view-cart', cnt, null)) as `screen_view-cart`, MAX(IF(funnel.event_name_with_screen = 'click_payment-cart', cnt, null)) as `click_payment-cart` FROM funnel GROUP BY ALL ORDER BY event_date
강사님 안녕하세요! 최적화 강의 1,2 잘 듣고 있습니다 :) 3-4) 이미지 사이즈 최적화 강의에서 강의에서 설명하신 방식대로 진행했는데, 제 경우는 다른 결과가 나와서 질문 드립니다. 강사님이 작성하신 코드대로 webp 형식을 지원하는 브라우저이면 source 태그를 통해 로드하고 그렇지 않으면 jpg 형식으로 로드하도록 코드를 그대로 작성했습니다. <picture> <source data-srcset={props.webp + "s"} type="image/webp" /> <img data-src={props.image} ref={imgRef} /> </picture> 그러나 고의적으로 data-srcset 에서 에러를 발생시켰을 때, img 태그에 해당하는 jpg 형식의 이미지가 로드되지 않고, 이미지 엑박이 나옵니다. 저와 동일한 질문을 봤는데 jpg 형식 이미지가 담긴 img 태그는 실제로 화면에 잘 나오는 것을 확인했습니다. webp 형식과 Jpg 형식 이미지 모두 지연 로딩을 적용해서 이러한 에러가 발생하는건가요?
1. ARRAY, STRUCT 1) array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요 SELECT title, -- genres, genre FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(genres) AS genre 2) array_exercises테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요.배우와 배역은 별도의 컬럼으로 나와야합니다 SELECT title, actor.actor, actor.character -- actors, -- actors[SAFE_OFFSET(0)].actor AS actor, -- actors[SAFE_OFFSET(1)].character AS 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 표시 되어야 합니다 SELECT title, -- actors, 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 AS value, 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, sum_of_amount, 0)) AS user_1, SUM(IF(user_id = 2, sum_of_amount, 0)) AS user_2, SUM(IF(user_id = 3, sum_of_amount, 0)) AS user_3 FROM (SELECT order_date, user_id, SUM(amount) as sum_of_amount FROM advanced.orders GROUP BY order_date, user_id ) GROUP BY order_date ORDER BY order_date 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 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) user_id = 32888이 카트 추가하기(click_cart)를 누를때 어떤 음식(food_id)을 담았나요? SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(param.key = 'firebase_screen', param.value.string_value, null)) AS firebase_screen, MAX(IF(param.key = 'food_id', param.value.int_value, null)) AS food_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS param WHERE user_id = 32888 and event_name = 'click_cart' GROUP BY ALL ORDER BY event_date 3. 퍼널 WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, platform, -- event_param MAX(IF(event_param.key = "firebase_screen", event_param.value.string_value, NULL)) AS firebase_screen, -- MAX(IF(event_param.key = "food_id", event_param.value.int_value, NULL)) AS food_id, 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 = "2022-08-01" # 적은 데이터로 쿼리를 작성하기 위해 만들어둔 조건 event_date BETWEEN "2022-08-01" AND "2022-08-18" GROUP BY ALL ), filter_event_and_concat_event_and_screen AS ( -- (1) event_name + screen (필요한 이벤트만 WHERE 조건에 걸어서 사용) 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") ), funnel AS ( SELECT event_date, 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 ) SELECT event_date, MAX(IF(funnel.event_name_with_screen = 'screen_view-welcome', cnt, null)) as `screen_view-welcome`, MAX(IF(funnel.event_name_with_screen = 'screen_view-home', cnt, null)) as `screen_view-home`, MAX(IF(funnel.event_name_with_screen = 'screen_view-food_category', cnt, null)) as `screen_view-food_category`, MAX(IF(funnel.event_name_with_screen = 'screen_view-restaurant', cnt, null)) as `screen_view-restaurant`, MAX(IF(funnel.event_name_with_screen = 'screen_view-cart', cnt, null)) as `screen_view-cart`, MAX(IF(funnel.event_name_with_screen = 'click_payment-cart', cnt, null)) as `click_payment-cart` FROM funnel GROUP BY ALL ORDER BY event_date
1. ARRAY, STRUCT 연습문제 1-1 SELECT title, movie_genres FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS movie_genres LIMIT 100 1-2 SELECT title, actor.actor, actor.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor 1-3 SELECT title, actors.actor, actors.character, genres FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actors CROSS JOIN UNNEST(genres) AS genres WHERE actor = 'ChrisEvans' 1-4 select user_id , event_date , event_name , user_pseudo_id , param.key as key , param.value.string_value as string_value , param.value.int_value as int_value from advanced.app_logs , unnest(event_params) as param 2. PIVOT 연습문제 2-1 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 2-2 select user_id , max(if(order_date = '2023-05-01',amount,0)) as `2023-05-01` , max(if(order_date = '2023-05-02',amount,0)) as `2023-05-02` , max(if(order_date = '2023-05-03',amount,0)) as `2023-05-03` , max(if(order_date = '2023-05-04',amount,0)) as `2023-05-04` , max(if(order_date = '2023-05-05',amount,0)) as `2023-05-05` from advanced.orders group by user_id order by user_id 2-3 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 2-4 select user_id , event_date , event_name , user_pseudo_id , max(if(param.key = 'firebase_screen', param.value.string_value, null)) as firebase_screen , max(if(param.key = 'food_id', param.value.int_value, null)) as food_id , max(if(param.key = 'session_id', param.value.string_value, null)) as session_id from advanced.app_logs , unnest(event_params) as param where event_date = '2022-08-01' group by all 3. 퍼널 연습문제 3-1 with base as ( select event_date , event_name , event_timestamp , user_id , user_pseudo_id , platform , max(if(param.key = 'firebase_screen', param.value.string_value, null)) as firebase_screen from advanced.app_logs , unnest(event_params) as param where event_date between '2022-08-01' and '2022-08-18' group by all ), filter_event as ( select * except(event_name, firebase_screen) , concat(event_name, "-", firebase_screen) as event_name_with_screen from base where event_name in ('screen_view', 'click_payment') ) select event_date , 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 group by all having step_number is not null order by event_date 3-2 with base as ( select event_date , event_name , event_timestamp , user_id , user_pseudo_id , platform , max(if(param.key = 'firebase_screen', param.value.string_value, null)) as firebase_screen from advanced.app_logs , unnest(event_params) as param where event_date between '2022-08-01' and '2022-08-18' group by all ), filter_event as ( select * except(event_name, firebase_screen) , concat(event_name, "-", firebase_screen) as event_name_with_screen from base where event_name in ('screen_view', 'click_payment') ), daily_group as ( select event_date , 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 group by all having step_number is not null order by event_date ) select event_date , max(if(event_name_with_screen = 'screen_view-welcome',cnt,null)) as screen_view_welcome , max(if(event_name_with_screen = 'screen_view-home',cnt,null)) as screen_view_home , max(if(event_name_with_screen = 'screen_view-food_category',cnt,null)) as screen_view_food_category , max(if(event_name_with_screen = 'screen_view-restaurant',cnt,null)) as screen_view_restaurant , max(if(event_name_with_screen = 'screen_view-cart',cnt,null)) as screen_view_cart , max(if(event_name_with_screen = 'click_payment-cart',cnt,null)) as click_payment_cart from daily_group group by all order by event_date
1. ARRAY, STRUCT 연습 문제 # 1. array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요. # 쿼리를 작성하는 목표, 확인할 지표: title, genre # 쿼리 계산 방법: UNNEST # 데이터의 기간: X # 사용할 테이블: advanced.array_exercises # JOIN KEY: X # 데이터 특징: genres는 배열임 SELECT title, genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS genre ORDER BY title # 2. array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야 합니다. # 쿼리를 작성하는 목표, 확인할 지표: title, actors.actor, actors.character # 쿼리 계산 방법: UNNEST # 데이터의 기간: X # 사용할 테이블: advanced.array_exercises # JOIN KEY: X # 데이터 특징: actors는 actor, character로 구성된 struct이고, actor, character은 배열임 SELECT title, a.actor, a.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS a ORDER BY title # 3. array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르(genre)를 출력하세요. 한 Row에 배우, 배역, 장르가 모두 표시되어야 합니다. # 쿼리를 작성하는 목표, 확인할 지표: title, actors.actor, actors.character, genres # 쿼리 계산 방법: UNNEST # 데이터의 기간: X # 사용할 테이블: advanced.array_exercises # JOIN KEY: X # 데이터 특징: -- actors는 actor, charactor로 구성된 struct이고, actor, charactor은 배열임 -- genres는 배열임 SELECT title, a.actor, a.character, genre FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS a CROSS JOIN UNNEST(genres) AS genre ORDER BY title # 4. 앱 로그 데이터(app_logs)의 배열을 풀어주세요 # 쿼리를 작성하는 목표, 확인할 지표: app_logs 데이터 평면화 # 쿼리 계산 방법: UNNEST # 데이터의 기간: X # 사용할 테이블: advanced.app_logs # JOIN KEY: X # 데이터 특징: -- event_params는 key, value로 구성된 struct이고, -- key는 배열이며, -- value는 string_value, int_value로 구성된 struct이고, -- string_value, int_value 배열임 SELECT user_id, event_date, event_name, user_pseudo_id, ep.key, ep.value.string_value, ep.value.int_value FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS ep 2. PIVOT 연습 문제 # 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. # 쿼리를 작성하는 목표, 확인할 지표: user_id 별 amount PIVOT # 쿼리 계산 방법: PIVOT # 데이터의 기간: X # 사용할 테이블: advanced.orders # JOIN KEY: X # 데이터 특징: 원본 테이블에 order_date, user_id, amount가 열로 저장되어 있음 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. # 쿼리를 작성하는 목표, 확인할 지표: order_date 별 SUM(amount) # 쿼리 계산 방법: PIVOT # 데이터의 기간: X # 사용할 테이블: advanced.orders # JOIN KEY: X # 데이터 특징: 원본 테이블에 order_date, user_id, amount가 열로 저장되어 있음 SELECT user_id, SUM(IF(order_date = '2023-05-01', amount, 0)), SUM(IF(order_date = '2023-05-02', amount, 0)), SUM(IF(order_date = '2023-05-03', amount, 0)), SUM(IF(order_date = '2023-05-04', amount, 0)), SUM(IF(order_date = '2023-05-05', amount, 0)) 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. # 쿼리를 작성하는 목표, 확인할 지표: user_id 별 order_date에 주문했는지 안 했는지 출력 # 쿼리 계산 방법: PIVOT # 데이터의 기간: X # 사용할 테이블: advanced.orders # JOIN KEY: X # 데이터 특징: 원본 테이블에 order_date, user_id가 열로 저장되어 있음 SELECT user_id, MAX(IF(order_date = '2023-05-01', 1, 0)), MAX(IF(order_date = '2023-05-02', 1, 0)), MAX(IF(order_date = '2023-05-03', 1, 0)), MAX(IF(order_date = '2023-05-04', 1, 0)), MAX(IF(order_date = '2023-05-05', 1, 0)) FROM advanced.orders GROUP BY user_id ORDER BY user_id # 4. user_id = 32888이 카트 추가하기(click_cart)를 누를때 어떤 음식(food_id)을 담았나요? # 쿼리를 작성하는 목표, 확인할 지표: user_id = 32888의 click_cart 할 때 food_id # 쿼리 계산 방법: PIVOT # 데이터의 기간: X # 사용할 테이블: advanced.app_logs # JOIN KEY: X # 데이터 특징: -- event_params-> key/value-> string_value/int_value -- key: firebase_screen/food_id/session_id SELECT user_id, event_date, event_name, event_timestamp, user_pseudo_id, MAX(IF(key = 'firebase_screen', string_value, NULL)) AS firevase_screen, MAX(IF(key = 'food_id', int_value, NULL)) AS food_id, MAX(IF(key = 'session_id', string_value, NULL)) AS session_id FROM ( SELECT user_id, event_date, event_name, event_timestamp, 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 user_id = 32888 AND event_name = 'click_cart' ) GROUP BY ALL ORDER BY event_date 3. 퍼널 쿼리 연습 문제 # 1. 퍼널 별 유저 수 집계 # 쿼리를 작성하는 목표, 확인할 지표: 퍼널 별 유저 수 집계 # 쿼리 계산 방법: UNNEST -> event_name/string_value, event_date에 조건 설정 -> user_pseudo_id를 DISTINCT하게 COUNT # 데이터의 기간: 2022-08-01 ~ 2022-08-18 # 사용할 테이블: advanced.app_logs # JOIN KEY: X # 데이터 특징: -- screen name의 경우 event_params.key = 'firebase_screen'인 event_params.value.string_value를 추출 SELECT CONCAT(event_name, '-', string_value) AS event_name_with_screen, CASE WHEN event_name = 'screen_view' AND string_value = 'welcome' THEN 1 WHEN event_name = 'screen_view' AND string_value = 'home' THEN 2 WHEN event_name = 'screen_view' AND string_value = 'food_category' THEN 3 WHEN event_name = 'screen_view' AND string_value = 'restaurant' THEN 4 WHEN event_name = 'screen_view' AND string_value = 'cart' THEN 5 WHEN event_name = 'click_payment' AND string_value = 'cart' THEN 6 END AS step_number, COUNT(DISTINCT(user_pseudo_id)) AS cnt FROM ( SELECT event_date, event_name, event_param.value.string_value AS string_value, user_pseudo_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_param.key = 'firebase_screen' AND ( (event_name = 'screen_view' AND event_param.value.string_value = 'welcome') OR (event_name = 'screen_view' AND event_param.value.string_value = 'home') OR (event_name = 'screen_view' AND event_param.value.string_value = 'food_category') OR (event_name = 'screen_view' AND event_param.value.string_value = 'restaurant') OR (event_name = 'screen_view' AND event_param.value.string_value = 'cart') OR (event_name = 'click_payment' AND event_param.value.string_value = 'cart') ) ) GROUP BY ALL ORDER BY step_number # 2. 일자 별 퍼널 별 유저 수 집계 # 쿼리를 작성하는 목표, 확인할 지표: 일자별 퍼널 별 유저 수 집계 # 쿼리 계산 방법: UNNEST -> event_name/string_value, event_date에 조건 설정 -> user_pseudo_id를 DISTINCT하게 COUNT # 데이터의 기간: 2022-08-01 ~ 2022-08-18 # 사용할 테이블: advanced.app_logs # JOIN KEY: X # 데이터 특징: -- screen name의 경우 event_params.key = 'firebase_screen'인 event_params.value.string_value를 추출 SELECT event_date, CONCAT(event_name, '-', string_value) AS event_name_with_screen, CASE WHEN event_name = 'screen_view' AND string_value = 'welcome' THEN 1 WHEN event_name = 'screen_view' AND string_value = 'home' THEN 2 WHEN event_name = 'screen_view' AND string_value = 'food_category' THEN 3 WHEN event_name = 'screen_view' AND string_value = 'restaurant' THEN 4 WHEN event_name = 'screen_view' AND string_value = 'cart' THEN 5 WHEN event_name = 'click_payment' AND string_value = 'cart' THEN 6 END AS step_number, COUNT(DISTINCT(user_pseudo_id)) AS cnt FROM ( SELECT event_date, event_name, event_param.value.string_value AS string_value, user_pseudo_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_param.key = 'firebase_screen' AND ( (event_name = 'screen_view' AND event_param.value.string_value = 'welcome') OR (event_name = 'screen_view' AND event_param.value.string_value = 'home') OR (event_name = 'screen_view' AND event_param.value.string_value = 'food_category') OR (event_name = 'screen_view' AND event_param.value.string_value = 'restaurant') OR (event_name = 'screen_view' AND event_param.value.string_value = 'cart') OR (event_name = 'click_payment' AND event_param.value.string_value = 'cart') ) ) GROUP BY ALL ORDER BY event_date # 2-1. 일자 별 퍼널 별 유저 수 집계 PIVOT하기 SELECT event_date, MAX(IF(event_name_with_screen='screen_view-welcome', cnt, NULL)) AS `screen_view-welcome`, MAX(IF(event_name_with_screen='screen_view-home', cnt, NULL)) AS `screen_view-home`, MAX(IF(event_name_with_screen='screen_view-food_category', cnt, NULL)) AS `screen_view-food_category`, MAX(IF(event_name_with_screen='screen_view-restaurant', cnt, NULL)) AS `screen_view-restaurant`, MAX(IF(event_name_with_screen='screen_view-cart', cnt, NULL)) AS `screen_view-cart`, MAX(IF(event_name_with_screen='click_payment-cart', cnt, NULL)) AS `click_payment-cart`, FROM ( SELECT event_date, 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 ) GROUP BY ALL ORDER BY event_date
시간관리의 실패로 퀄리티있는 학습을 못 한 것 같습니다. 과제 제출 시간 이후에 복습하며 다시 꼼꼼하게 살피겠습니다! ARRAY, STRUCT CREATE OR REPLACE TABLE advanced.array_exercises AS SELECT movie_id, title, actors, genres FROM ( SELECT 1 AS movie_id, 'Avengers: Endgame' AS title, ARRAY<STRUCT<actor STRING, character STRING>>[ STRUCT('Robert Downey Jr.', 'Tony Stark'), STRUCT('Chris Evans', 'Steve Rogers') ] AS actors, ARRAY<STRING>['Action', 'Adventure', 'Drama'] AS genres UNION ALL SELECT 2, 'Inception', ARRAY<STRUCT<actor STRING, character STRING>>[ STRUCT('Leonardo DiCaprio', 'Cobb'), STRUCT('Joseph Gordon-Levitt', 'Arthur') ], ARRAY<STRING>['Action', 'Adventure', 'Sci-Fi'] UNION ALL SELECT 3, 'The Dark Knight', ARRAY<STRUCT<actor STRING, character STRING>>[ STRUCT('Christian Bale', 'Bruce Wayne'), STRUCT('Heath Ledger', 'Joker') ], ARRAY<STRING>['Action', 'Crime', 'Drama'] ) 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 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`, UNNEST(actors) as actor 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 AS actor, actor.character AS character, genre FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre 앱 로그 데이터(app_logs)의 배열을 풀어주세요. SELECT event_timestamp, event_name, event_param.key AS key, event_param.value.string_value AS string_value, event_param.value.int_value AS int_value, user_id FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param PIVOT 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 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, MAX(IF(order_date = "2023-05-01", amount, 0)) AS `2023-05-01`, MAX(IF(order_date = "2023-05-02", amount, 0)) AS `2023-05-02`, MAX(IF(order_date = "2023-05-03", amount, 0)) AS `2023-05-03`, MAX(IF(order_date = "2023-05-04", amount, 0)) AS `2023-05-04`, MAX(IF(order_date = "2023-05-05", amount, 0)) AS `2023-05-05` FROM `advanced.orders` GROUP BY user_id ORDER BY user_id 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, 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 앱 로그 PIVOT WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(param.key = "firebase_screen", param.value.string_value, NULL)) AS firebase_screen, MAX(IF(param.key = "sesstion_id", param.value.string_value, NULL)) AS sesstion_id FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS param WHERE event_date = "2022-08-01" GROUP BY ALL ) SELECT event_date, COUNT(user_id) AS user_cnt FROM base WHERE event_name = "click_cart" GROUP BY event_date Funnel 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") ) SELECT event_date, 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
1. ARRAY, STRUCT 연습문제 - 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 AS actor , actor.character AS 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 AS actor , actor.character AS 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 , params.key AS key , params.value.string_value AS str_value , params.value.int_value AS int_value FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS params WHERE event_date = '2022-08-01' 2. PIVOT 연습문제 -- 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 WITH step1 AS ( SELECT order_date , user_id , sum(amount) AS sum_of_amount FROM advanced.orders GROUP BY ALL ) SELECT order_date , MAX(IF(user_id = 1, sum_of_amount, 0)) AS user_1 , MAX(IF(user_id = 2, sum_of_amount, 0)) AS user_2 , MAX(IF(user_id = 3, sum_of_amount, 0)) AS user_3 FROM step1 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 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 SELECT user_id , event_date , event_name , user_pseudo_id , MAX(IF(params.key = 'firebase_screen', params.value.string_value, NULL)) AS firebase_screen , MAX(IF(params.key = 'food_id', params.value.int_value, NULL)) AS food_id , MAX(IF(params.key = 'session_id', params.value.string_value, NULL)) AS session_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS params WHERE event_date = '2022-08-01' GROUP BY ALL 3. 퍼널분석 WITH step1 AS ( SELECT event_date , event_timestamp , event_name , user_id , user_pseudo_id , MAX(IF(params.key = 'firebase_screen', params.value.string_value, NULL)) AS firebase_screen , MAX(IF(params.key = 'session_id', params.value.string_value, NULL)) AS session_id , platform FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS params WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' GROUP BY ALL ), step2 AS ( SELECT * EXCEPT(event_timestamp) , CONCAT(event_name, '-', firebase_screen) AS event_name_with_screen , DATETIME(TIMESTAMP_MICROS(event_timestamp), 'Asia/Seoul') AS event_datetime FROM step1 ), step3 AS ( SELECT * , 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 END AS step_number FROM step2 ), step3_1 AS ( -- 1) 각 퍼널별 유저 수 집계 SELECT event_name_with_screen , step_number , COUNT(DISTINCT user_pseudo_id) AS cnt FROM step3 GROUP BY ALL HAVING step_number IS NOT NULL ORDER BY step_number ) , step3_2 AS ( -- 2) 일자별 각 퍼널별 유저 수 집계 SELECT event_date , event_name_with_screen , step_number , COUNT(DISTINCT user_pseudo_id) AS cnt FROM step3 GROUP BY ALL HAVING step_number IS NOT NULL ORDER BY event_date , step_number ) -- 3) 2) 데이터를 PIVOT SELECT event_date , MAX(IF(event_name_with_screen = 'screen_view-welcome', cnt, NULL)) AS `screen_view-welcome` , MAX(IF(event_name_with_screen = 'screen_view-home', cnt, NULL)) AS `screen_view-home` , MAX(IF(event_name_with_screen = 'screen_view-food_category', cnt, NULL)) AS `screen_view-food_category` , MAX(IF(event_name_with_screen = 'screen_view-restaurant', cnt, NULL)) AS `screen_view-restaurant` , MAX(IF(event_name_with_screen = 'screen_view-cart', cnt, NULL)) AS `screen_view-cart` , MAX(IF(event_name_with_screen = 'click_payment-cart', cnt, NULL)) AS `click_payment-cart` FROM step3_2 GROUP BY event_date ORDER BY event_date
1. ARRAY, STRUCT 연습문제 array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요. SELECT title, genre FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(genres) AS genre 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 AS ae CROSS JOIN UNNEST(actors) AS actor array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 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 AS ae , UNNEST(actors) AS actor , UNNEST(genres) AS genre 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 , UNNEST(event_params) AS event_param -- WHERE -- event_date = "2022-08-01" 2. PIVOT 연습 문제 orders 테이블에서 유저(user_id)별로 주문 금액(amount)의 합계를 PIVOT 해주세요. 날짜(order_date)를 행(row)으로, user_id를 열(column)으로 만들어야 합니다. SELECT order_date, MAX(IF(user_id = 1, sum_of_amount, 0)) AS user_1, MAX(IF(user_id = 2, sum_of_amount, 0)) AS user_2, MAX(IF(user_id = 3, sum_of_amount, 0)) AS user_3 FROM ( SELECT order_date, user_id, SUM(amount) AS sum_of_amount FROM advanced.orders GROUP BY order_date, user_id ORDER BY order_date ) GROUP BY order_date ORDER BY order_date 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, MAX(IF(order_date = '2023-05-01', amount, 0)) AS `2023-05-01`, MAX(IF(order_date = '2023-05-02', amount, 0)) AS `2023-05-02`, MAX(IF(order_date = '2023-05-03', amount, 0)) AS `2023-05-03`, MAX(IF(order_date = '2023-05-04', amount, 0)) AS `2023-05-04`, MAX(IF(order_date = '2023-05-05', amount, 0)) AS `2023-05-05`, FROM advanced.orders GROUP BY user_id ORDER BY user_id orders 테이블에서 사용자(user_id)별, 날짜(order_date)별로 주문이 있다면 1, 없다면 0으로 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 user_id = 32888이 카트 추가하기(click_cart)를 누를 때 어떤 음식(food_id)을 담았나요? WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(event_param.key = 'firebase screen', event_param.value.string_value, NULL)) AS firebase_screen, MAX(IF(event_param.key = 'food_id', event_param.value.int_value, null)) AS food_id, MAX(IF(event_param.key = 'session_id', event_param.value.string_value, null)) AS session_id FROM advanced.app_logs , UNNEST(event_params) AS event_param GROUP BY ALL ) SELECT food_id FROM base WHERE user_id = 32888 AND event_name = 'click_cart' GROUP BY food_id 3. 퍼널 분석 연습문제 각 퍼널의 유저 수를 집계 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 = "food_id", event_param.value.int_value, NULL)) AS food_id, MAX(IF(event_param.key = "session_id", event_param.value.int_value, NULL)) AS session_id FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param GROUP BY ALL ), filter_event_and_concat_event_and_screen AS( SELECT * EXCEPT(event_name, firebase_screen), CONCAT(event_name, "-", firebase_screen) AS event_name_with_screen, DATETIME(TIMESTAMP_MICROS(event_timestamp), "Asia/Seoul") AS event_datetime FROM base WHERE event_date BETWEEN "2022-08-01" AND "2022-08-18" AND event_name IN ("screen_view", "click_payment") AND firebase_screen IN ("welcome", "home", "food_category", "restaurant", "cart") ) SELECT event_date, 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 ORDER BY event_date, step_number
1. ARRAY, STRUCT -- 1. array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요. -- genres 꺼내기 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. -- arrary 안의 struct 영화/배우 꺼내기 SELECT title , ac.actor , ac.character FROM `advanced.array_exercises` CROSS JOIN UNNEST(actors) AS ac -- actor.actor도 가능 -- 3. array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character), 장르(genre) 출력 -- actors, genres 각각 꺼내기 (2번 조인) SELECT -- title, actor, character, genre title , ac.actor , ac.character , genre FROM `advanced.array_exercises` CROSS JOIN UNNEST(actors) AS ac CROSS JOIN UNNEST(genres) AS genre -- 4. 앱로그 데이터(app_logs)의 배열을 풀어주세요. -- event_params 꺼내기 SELECT user_id , event_date , event_name , user_pseudo_id , event_param.key , event_param.value.string_value , event_param.value.int_value FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param -- WHERE event_date = '2022-08-01' -- test 2. PIVOT -- 1. orders 테이블에서 유저(user_id)별로 주문 금액(amount)의 합계를 PIOVT해주세요. -- 날짜(order_date)를 행, user_id를 열, sum(amount) 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 1 ORDER BY 1 -- 2. orders 테이블에서 날짜(order_date)별로 유저들의 주문 금액(amount) 합계를 PIVOT -- user_id 행, order_date 열, sum(주문 금액), '-' 포함 날짜 별칭은 backtick(``) 활용 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 사용자별 날짜별로 주문이 있다면 1, 없으면 0으로 PIOVT -- user_id 행, order_date 열, if(날짜, 1, 0) 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 3. 퍼널 3-1. 퍼널별 유저 수 -- 1. 퍼널별 유저 수 : 2022-08-01 ~ 2022-08-18, 오픈 퍼널, COUNT(DISTINCT user_pseudo_id) WITH funnels AS ( -- 조건/컬럼 필터링, event_params UNNEST, event name + screen 문자열 컬럼 병합 SELECT CONCAT(event_name, '-', event_param.value.string_value) AS event_name_with_screen , COUNT(DISTINCT user_pseudo_id) AS cnt FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_name IN ('screen_view', 'click_payment') AND event_param.key = 'firebase_screen' AND event_param.value.string_value NOT IN ('food_detail', 'search', 'search_result') GROUP BY ALL ) SELECT event_name_with_screen -- step_number 생성 , (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 END ) AS step_number , cnt FROM funnels ORDER BY step_number 3-2. 퍼널별 유저 수(일자별) -- 2. 퍼널별 유저 수(일자별) : 2022-08-01 ~ 2022-08-18, 오픈 퍼널, COUNT(DISTINCT user_pseudo_id) WITH funnels AS ( -- 조건/컬럼 필터링, event_params UNNEST, event name + screen 문자열 컬럼 병합 SELECT -- 일자별 event_date , CONCAT(event_name, '-', event_param.value.string_value) AS event_name_with_screen , COUNT(DISTINCT user_pseudo_id) AS cnt FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_name IN ('screen_view', 'click_payment') AND event_param.key = 'firebase_screen' AND event_param.value.string_value NOT IN ('food_detail', 'search', 'search_result') GROUP BY ALL ) SELECT event_date , event_name_with_screen -- step_number 생성 , (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 END ) AS step_number , cnt FROM funnels ORDER BY event_date, step_number -- 정렬 변경 3-3. 퍼널별 유저 수(일자별 PIVOT) -- 3. 퍼널별 유저 수(일자별) 집계 PIVOT WITH funnels AS ( -- 조건/컬럼 필터링, event_params UNNEST, event name + screen 문자열 컬럼 병합 SELECT -- 일자별 event_date , CONCAT(event_name, '-', event_param.value.string_value) AS event_name_with_screen , COUNT(DISTINCT user_pseudo_id) AS cnt FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_name IN ('screen_view', 'click_payment') AND event_param.key = 'firebase_screen' AND event_param.value.string_value NOT IN ('food_detail', 'search', 'search_result') GROUP BY ALL ) SELECT event_date -- event PIVOT , MAX(IF(event_name_with_screen = 'screen_view-welcome', cnt, NULL)) AS `screen_view-welcome` , MAX(IF(event_name_with_screen = 'screen_view-home', cnt, NULL)) AS `screen_view-home` , MAX(IF(event_name_with_screen = 'screen_view-food_category', cnt, NULL)) AS `screen_view-food_category` , MAX(IF(event_name_with_screen = 'screen_view-restaurant', cnt, NULL)) AS `screen_view-restaurant` , MAX(IF(event_name_with_screen = 'screen_view-cart', cnt, NULL)) AS `screen_view-cart` , MAX(IF(event_name_with_screen = 'click_payment-cart', cnt, NULL)) AS `click_payment-cart` FROM funnels GROUP BY ALL ORDER BY event_date
ARRAY, STRUCT 연습문제 1) SELECT title, genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS genre 2) SELECT title, actors, actors[SAFE_OFFSET(0)].actor AS frist_actor, actors[SAFE_OFFSET(0)].character AS first_character, actors[SAFE_OFFSET(1)].actor AS second_actor, actors[SAFE_OFFSET(1)].character AS second_character FROM advanced.array_exercises AS ae 3) SELECT title, actor.actor, actor.character FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(actors) AS actor 4) SELECT event_Date, event_timestamp, event_name, event_params, user_id, event_param.key AS key, event_param.value AS value, event_param.value.string_value AS string_value, event_param.value.int_value AS int_value FROM advanced.app_logs_temp CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date = '2022-08-01' PIVOT 연습문제 1) SELECT order_date, SUM(IF(user_id = 1, sum_of_amount, 0)) AS user_1, SUM(IF(user_id = 2, sum_of_amount, 0)) AS user_2, SUM(IF(user_id = 3, sum_of_amount, 0)) AS user_3, FROM ( SELECT order_date, user_id, SUM(amount) AS sum_of_amount FROM advanced.orders GROUP BY order_date, user_id ORDER BY order_date) GROUP BY order_date 2) SELECT user_id, SUM(IF(order_date = '2023-05-01', sum_of_amount, 0)) AS `2023-05-01`, SUM(IF(order_date = '2023-05-02', sum_of_amount, 0)) AS `2023-05-02`, SUM(IF(order_date = '2023-05-03', sum_of_amount, 0)) AS `2023-05-03`, SUM(IF(order_date = '2023-05-04', sum_of_amount, 0)) AS `2023-05-04`, SUM(IF(order_date = '2023-05-05', sum_of_amount, 0)) AS `2023-05-05` FROM ( SELECT user_id, order_date, SUM(amount) AS sum_of_amount FROM advanced.orders GROUP BY user_id,order_date) GROUP BY user_id 3) SELECT user_id, MAX(IF(order_date = '2023-05-01' AND user_id IS NOT NULL, 1, 0)) AS `2023-05-01`, MAX(IF(order_date = '2023-05-02' AND user_id IS NOT NULL, 1, 0)) AS `2023-05-02`, MAX(IF(order_date = '2023-05-03' AND user_id IS NOT NULL, 1, 0)) AS `2023-05-03`, MAX(IF(order_date = '2023-05-04' AND user_id IS NOT NULL, 1, 0)) AS `2023-05-04`, MAX(IF(order_date = '2023-05-05' AND user_id IS NOT NULL, 1, 0)) AS `2023-05-05` FROM advanced.orders GROUP BY user_id 4) SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(param.key = 'firebase_screen', param.value.string_value, NULL)) AS firebase_screen, MAX(IF(param.key = 'food_id', param.value.string_value, NULL)) AS food_id, MAX(IF(param.key = 'food_id', param.value.int_value, NULL)) AS food_id2, MAX(IF(param.key = 'session_id', param.value.string_value, NULL)) AS session_id FROM advanced.app_logs_temp CROSS JOIN UNNEST(event_params) AS param WHERE event_date = '2022-08-01' GROUP BY ALL 퍼널분석 WITH Funnel AS (SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(param.key = 'firebase_screen', param.value.string_value, NULL)) AS firebase_screen, MAX(IF(param.key = 'food_id', param.value.string_value, NULL)) AS food_id, MAX(IF(param.key = 'food_id', param.value.int_value, NULL)) AS food_id2, MAX(IF(param.key = 'session_id', param.value.string_value, NULL)) AS session_id FROM advanced.app_logs_temp CROSS JOIN UNNEST(event_params) AS param WHERE event_date = '2022-08-01' GROUP BY ALL ) SELECT event_date, COUNT(user_id) AS user_cnt FROM Funnel WHERE event_name = 'click_cart' GROUP BY event_date;
ARRAY, STRUCT, UNNEST 연습 문제 array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요 SELECT title, genre FROM `plucky-catfish-394207.advanced.array_exercises` , unnest(genres) as genre ORDER BY 1 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 `plucky-catfish-394207.advanced.array_exercises` , unnest(genres) as genre ORDER BY 1 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, genre, FROM `plucky-catfish-394207.advanced.array_exercises` , unnest(genres) as genre , unnest(actors) as actors ORDER BY 1 앱 로그 데이터(app_logs)의 배열을 풀어주세요 SELECT user_id, event_date, event_name, user_pseudo_id, ep.key, ep.value.string_value, ep.value.int_value FROM `plucky-catfish-394207.advanced.array_exercises` , unnest(event_params) as ep ORDER BY 2 PIVOT 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 `plucky-catfish-394207.advanced.array_exercises` , unnest(event_params) as ep GROUP BY 1 ORDER BY 1 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 `plucky-catfish-394207.advanced.array_exercises` , unnest(event_params) as ep GROUP BY 1 ORDER BY 1 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, SUM(IF(order_date = '2023-05-01', amount > 0, 1, 0) as `2023-05-01`, SUM(IF(order_date = '2023-05-02', amount > 0, 1, 0) as `2023-05-02`, SUM(IF(order_date = '2023-05-03', amount > 0, 1, 0) as `2023-05-03`, SUM(IF(order_date = '2023-05-04', amount > 0, 1, 0) as `2023-05-04`, SUM(IF(order_date = '2023-05-05', amount > 0, 1, 0) as `2023-05-05`, FROM `plucky-catfish-394207.advanced.array_exercises` , unnest(event_params) as ep GROUP BY 1 ORDER BY 1 user_id = 32888이 카트 추가하기(click_cart)를 누를때 어떤 음식(food_id)을 담았나요? SELECT event_date, event_name, event_timestamp, user_id, user_pseudo_id, MAX(IF(ep.key = 'firebase_screen', ep.value.string_value, NULL)) as firebase_screen, MAX(IF(ep.key = 'food_id', ep.value.int_value, NULL)) as food_id, MAX(IF(ep.key = 'session_id', ep.value.int_value, NULL)) as session_id, FROM `plucky-catfish-394207.advanced.array_exercises` , unnest(event_params) as ep WHERE event_date = '2022-08-01' AND user_id = 32888 GROUP BY 1, 2, 3, 4, 5 퍼널 분석 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") ), add_step_number AS ( SELECT event_date, 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
ARRAY, STRUCT -- array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주기. SELECT title, genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) as genre -- array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주기. 단, 배우와 배역은 별도의 컬럼으로 나와야 함. SELECT title, actor.actor, actor.character FROM `advanced.array_exercises` CROSS JOIN UNNEST(actors) as actor -- array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르(genre) 출력하기 한 행에 배우, 배역, 장르가 모두 표시되어야 된다. SELECT title, actor.actor, actor.character, genre FROM `advanced.array_exercises` CROSS JOIN UNNEST(actors) as actor CROSS JOIN UNNEST(genres) as genre -- 앱 로그 데이터 (app_logs)의 배열 풀기 SELECT user_id, event_date, event_name, user_pseudo_id, event_param.key, event_param.value.string_value, event_param.value.int_value FROM advanced.app_logs CROSS JOIN UNNEST(event_params) as event_param limit 500 데이터 PIVOT -- orders 테이블에서 유저(user_id)별로 주문 금액(amount)의 합계를 PIVOT하기. 날짜(order_date)를 행(Row)으로, user_id를 열(Column)으로 만들어야됨. 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 asc; -- orders 테이블에서 날짜(order_date)별로 유저들의 주문 금액(amount)의 합계를 PIVOT하기. user_id를 행으로, order_date를 열로 만들어야 됨. 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 ASC -- orders 테이블에서 사용자(user_id)별, 날짜(order_date)별로 주문이 있다면 1, 없다면 0으로 피벗하기. user_id를 행으로, order_date를 열로 만들고 주문이 많아도 1로 처리. SELECT user_id, sum(if(order_date = '2023-05-01',1, 0)) AS `2023-05-01`, sum(if(order_date = '2023-05-02',1, 0)) AS `2023-05-02`, sum(if(order_date = '2023-05-03',1, 0)) AS `2023-05-03`, sum(if(order_date = '2023-05-04',1, 0)) AS `2023-05-04`, sum(if(order_date = '2023-05-05',1, 0)) AS `2023-05-05` FROM advanced.orders GROUP BY user_id ORDER BY user_id ASC -- 앱 로그 데이터 배열 PIVOT 하기 ( user_id = 32888이 카트 추가하기 (click_cart)를 누를때 어떤 음식(food_id)을 담았나?) WITH app_pivot 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 app_pivot WHERE user_id = 32888 AND event_name = 'click_cart' GROUP BY user_id,event_date, event_name, user_pseudo_id ORDER BY event_date ASC; 퍼널 분석 -- 일자별 이벤트 별 집계형태를 PIVOT 형태로 전환하기 WITH param_pivot AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(key = 'firebase_screen', event_param.value.string_value,NULL)) AS `firebase_screen`, MAX(IF(key = 'food_id',event_param.value.int_value,NULL)) AS `food_id`, MAX(IF(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), event_table AS (SELECT * EXCEPT(event_name,firebase_screen), CONCAT(event_name,'_', firebase_screen) AS event_name_with_screen, FROM param_pivot WHERE event_name IN ('screen_view','click_payment') GROUP BY all ORDER BY event_date ASC ) , final AS (SELECT event_date, 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 = 'clik_payment_cart' THEN 6 ELSE NULL END ) AS step_number, COUNT( DISTINCT user_pseudo_id) AS cnt FROM event_table GROUP BY event_date, event_name_with_screen HAVING step_number IS NOT NULL ORDER BY event_date) SELECT event_date, SUM(IF(event_name_with_screen = 'screen_view_welcome', cnt, 0)) AS `screen_view_welcom`, SUM(IF(event_name_with_screen = 'screen_view_home', cnt, 0)) AS `screen_view_home`, SUM(IF(event_name_with_screen = 'screen_view_food_category', cnt, 0)) AS `screen_view_food_category`, SUM(IF(event_name_with_screen = 'screen_view_restaurant', cnt, 0)) AS `screen_view_restaurant`, SUM(IF(event_name_with_screen = 'screen_view_cart', cnt, 0)) AS `screen_view_cart`, SUM(IF(event_name_with_screen = 'click_payment_cart', cnt, 0)) AS `click_payment_cart` FROM final GROUP BY event_date ORDER BY event_date ASC;
1. ARRAY, STRUCT 연습문제 (1) array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요. SELECT title, genre FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(genres) AS genre *UNNEST(ARRAY_Column) = UNNEST(배열) (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 AS ae CROSS JOIN UNNEST(actors) AS actor (3) array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 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 AS ae , UNNEST(actors) AS actor , UNNEST(genres) AS genre *연속해서 CROSS JOIN UNNEST 사용 가능 (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 , UNNEST(event_params) AS event_param -- WHERE -- event_date = "2022-08-01" *실습 시, 파티션 사용 필요 2. PIVOT 연습문제 (1) orders 테이블에서 유저(user_id)별로 주문 금액(amount)의 합계를 PIVOT 해주세요. 날짜(order_date)를 행(row)으로, user_id를 열(column)으로 만들어야 합니다. SELECT order_date, MAX(IF(user_id = 1, sum_of_amount, 0)) AS user_1, MAX(IF(user_id = 2, sum_of_amount, 0)) AS user_2, MAX(IF(user_id = 3, sum_of_amount, 0)) AS user_3 FROM ( SELECT order_date, user_id, SUM(amount) AS sum_of_amount FROM advanced.orders GROUP BY order_date, user_id ORDER BY order_date ) GROUP BY order_date ORDER BY order_date *첫번째 풀이 내 집계 함수 사용 시, GROUP BY 잊지않기 *ctrl+d 사용 (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, MAX(IF(order_date = '2023-05-01', amount, 0)) AS `2023-05-01`, MAX(IF(order_date = '2023-05-02', amount, 0)) AS `2023-05-02`, MAX(IF(order_date = '2023-05-03', amount, 0)) AS `2023-05-03`, MAX(IF(order_date = '2023-05-04', amount, 0)) AS `2023-05-04`, MAX(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 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) user_id = 32888이 카트 추가하기(click_cart)를 누를 때 어떤 음식(food_id)을 담았나요? WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(event_param.key = 'firebase screen', event_param.value.string_value, NULL)) AS firebase_screen, MAX(IF(event_param.key = 'food_id', event_param.value.int_value, null)) AS food_id, MAX(IF(event_param.key = 'session_id', event_param.value.string_value, null)) AS session_id FROM advanced.app_logs , UNNEST(event_params) AS event_param GROUP BY ALL ) SELECT food_id FROM base WHERE user_id = 32888 AND event_name = 'click_cart' GROUP BY food_id 3. 퍼널 분석 연습문제 (1) 각 퍼널의 유저 수를 집계 데이터 : 2022-08-01 ~ 2022-08-18 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 = "food_id", event_param.value.int_value, NULL)) AS food_id, MAX(IF(event_param.key = "session_id", event_param.value.int_value, NULL)) AS session_id FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param GROUP BY ALL ), filter_event_and_concat_event_and_screen AS( SELECT * EXCEPT(event_name, firebase_screen), CONCAT(event_name, "-", firebase_screen) AS event_name_with_screen, DATETIME(TIMESTAMP_MICROS(event_timestamp), "Asia/Seoul") AS event_datetime FROM base WHERE event_date BETWEEN "2022-08-01" AND "2022-08-18" AND event_name IN ("screen_view", "click_payment") AND firebase_screen IN ("welcome", "home", "food_category", "restaurant", "cart") ) SELECT event_date, 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 ORDER BY event_date, step_number
연습문제 CREATE OR REPLACE TABLE advanced.array_excercised AS #DDL SELECT movie_id, title, actors, genres FROM ( SELECT 1 AS movie_id, 'Avengers: Endgame' AS title, ARRAY<STRUCT<actor STRING, character STRING>>[ STRUCT('Robert Downey Jr.', 'Tony Stark'), STRUCT('Chris Evans', 'Steve Rogers') ] AS actors, ARRAY<STRING>['Action', 'Adventure', 'Drama'] AS genres UNION ALL SELECT 2, 'Inception', ARRAY<STRUCT<actor STRING,character STRING>>[ STRUCT('leonardo DiCaprio', 'Cobb'), STRUCT('Joseph Gordon-Levitt', 'Arthur') ], ARRAY<STRING>['Action', 'Adventure', 'Sci-Fi'] UNION ALL SELECT 3, 'The Dark Knight', ARRAY<STRUCT<actor STRING, character STRING>>[ STRUCT('Christian Bale', 'Bruce Wayne'), STRUCT('Heath Ledger', 'Joker') ], ARRAY<STRING>['Action', 'Crime', 'Drama'] ) → actors라는 STRUCT 구조체를 만들고 그 안에 2개의 필드 actor와 character를 지정하고, STRUCT 구조체 2개를 list처럼 ARRAY에 넣은 것. 위의 테이블을 가지고 연습문제 1~4번 진행 array_excercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST 해 서 보여주세요. SELECT title, genre FROM advanced.array_excercised CROSS JOIN UNNEST(genres) AS genre array_excercised 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_excercised CROSS JOIN UNNEST(actors) AS actor actors ARRAY안에 2개의 STRUCT 구조체가 있는 구조이므로 ARRAY 데이터에 접근하는 방법으로 데이터에는 접근 가능 actors[SAFE_OFFSET(0)].actor AS first_actor array_excercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르(genre)를 출력하세요. 한 Ro w에 배우, 배역, 장르가 모두 표시되 어야 합니다. SELECT title, actor.actor, actor.character, genre FROM advanced.array_excercised CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre *UNNEST를 2번 연속 사용할 수 있다. *쿼리문의 실행순서는 FROM → JOIN → SELEC T 이다. UNNES T를 통해 만들어진 actor는 현재 actor.actor가 아니라 actor라는 STRUCT 구조체이므로 구조체에 바로 접근할 수 없다는 에러가 뜰 수 있다. 앱 로그 데이터(app_logs)의 배열을 풀어주세요. SELECT event_date, event_timestamp, event_name, event_param.key AS key, event_param.value AS value, user_id, user_pseudo_id, platform FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param event_params는 RECORD 라고 되어있는데 STRUCT 이다. 중첩된 구조 라는 의미. PIVOT 연습문제 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 PIVOT해주세요. 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 orders 테이블에서 사용자(user_id)별, 날짜(order_date)별로 주문이 있다면 1, 없다면 0으로 PIVOT해주세요. 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 4. user_id = 32888이 카트 추가하기(click_cart)를 누를 때 어떤 음식(food_id)을 담았나요? WITH base AS( SELECT #* EXCEPT(event_params), event_date, event_timestamp, event_name, event_param.key AS key, event_param.value.string_value AS string_value, event_param.value.int_value AS int_value, user_id, user_pseudo_id, platform FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param ) SELECT user_id, event_date, event_name, 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 user_id, event_date, event_name 퍼널별 전환율을 쉽게 구할 수 있도록 PIVOT 해보기 WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, platform, #event_param, MAX(IF(event_param.key = 'firebase_screen', event_param.value.string_value, NULL)) AS firebase_screen, #MAX(IF(event_param.key = 'food_id', event_param.value.int_value, NULL)) AS food_id, 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 = "2022-08-01" #적은 데이터로 쿼리를 작성하기 위해 만들어둔 조건 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") ), funnel_analysis AS ( SELECT event_date, #일자별로 퍼널별 유저수 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 ORDER BY event_date ) SELECT event_date, MAX(IF (event_name_with_screen = "screen_view-welcome", cnt, NULL)) AS `screen_view-welcome`, MAX(IF (event_name_with_screen = "screen_view-home", cnt, NULL)) AS `screen_view-home`, MAX(IF (event_name_with_screen = "screen_view-food_category", cnt, NULL)) AS `screen_view-food_category`, MAX(IF (event_name_with_screen = "screen_view-restaurant", cnt, NULL)) AS `screen_view-restaurant`, MAX(IF (event_name_with_screen = "screen_view-cart", cnt, NULL)) AS `screen_view-cart`, MAX(IF (event_name_with_screen = "click_payment-cart", cnt, NULL)) AS `click_payment-cart` FROM funnel_analysis GROUP BY ALL ORDER BY event_date
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) -- 동일한 단어에 대해 선택할 수 있는 함수 : cmd+d 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 , event_param.value.string_value , event_param.value.int_value , platform FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param ; 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, NULL)) AS `user_id_1` , SUM(IF(user_id = 2, amount, NULL)) AS `user_id_2` , SUM(IF(user_id = 3, amount, NULL)) AS `user_id_3` FROM advanced.orders GROUP BY 1 ORDER BY 1 ; 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 1 ; 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 1 ; 4) user_id = 32888이 카트 추가하기(click_cart)를 누를 때 어떤 음식(food_id)을 담았나요? WITH app_logs_info AS ( SELECT user_id , event_name , MAX(IF(event_param.key = 'firebase_screen', event_param.value.string_value, NULL)) AS firebase_screen , MAX(IF(event_param.key = 'food_id', event_param.value.int_value, NULL)) AS food_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param GROUP BY 1, 2 ) SELECT food_id FROM app_logs_info WHERE user_id = 32888 AND event_name = 'click_cart' ; 3. Funnel 1) 일자별, 이벤트별 집계 WITH app_logs_info AS ( SELECT user_id , event_date , event_timestamp , event_name , user_pseudo_id , event_param.key , MAX(IF(event_param.key = 'firebase_screen', event_param.value.string_value, NULL)) AS firebase_screen , MAX(IF(event_param.key = 'food_id', event_param.value.int_value, NULL)) AS food_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_name IN ('screen_view', 'click_payment') GROUP BY ALL ) , add_step_number AS ( SELECT event_date , DATETIME(TIMESTAMP_MICROS(event_timestamp), 'Asia/Seoul') AS event_date_time , user_id , user_pseudo_id , CONCAT(event_name, '-', firebase_screen) AS event_name_with_screen , CASE CONCAT(event_name, '-', firebase_screen) WHEN 'screen_view-welcome' THEN 1 WHEN 'screen_view-home' THEN 2 WHEN 'screen_view-food_category' THEN 3 WHEN 'screen_view-restaurant' THEN 4 WHEN 'screen_view-cart' THEN 5 WHEN 'click_payment-cart' THEN 6 ELSE NULL END AS step_number FROM app_logs_info ) SELECT event_date , step_number , event_name_with_screen , COUNT(DISTINCT user_pseudo_id) AS user_cnt FROM add_step_number WHERE step_number IS NOT NULL GROUP BY 1, 2, 3 ORDER BY 1, 2 ; 2) 집계 데이터 PIVOT WITH app_logs_info AS ( SELECT user_id , event_date , event_timestamp , event_name , user_pseudo_id , event_param.key , MAX(IF(event_param.key = 'firebase_screen', event_param.value.string_value, NULL)) AS firebase_screen , MAX(IF(event_param.key = 'food_id', event_param.value.int_value, NULL)) AS food_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 ) , add_step_number AS ( SELECT event_date , DATETIME(TIMESTAMP_MICROS(event_timestamp), 'Asia/Seoul') AS event_date_time , user_id , user_pseudo_id , CONCAT(event_name, '-', firebase_screen) AS event_name_with_screen , CASE CONCAT(event_name, '-', firebase_screen) WHEN 'screen_view-welcome' THEN 1 WHEN 'screen_view-home' THEN 2 WHEN 'screen_view-food_category' THEN 3 WHEN 'screen_view-restaurant' THEN 4 WHEN 'screen_view-cart' THEN 5 WHEN 'click_payment-cart' THEN 6 ELSE NULL END AS step_number FROM app_logs_info WHERE event_name IN ('screen_view', 'click_payment') ) , agg_user_cnt AS ( SELECT event_date , step_number , event_name_with_screen , COUNT(DISTINCT user_pseudo_id) AS user_cnt FROM add_step_number WHERE step_number IS NOT NULL GROUP BY 1, 2, 3 ORDER BY 1, 2 ) SELECT event_date , MAX(IF(step_number = 1, user_cnt, NULL)) AS `screen_view-welcome` , MAX(IF(step_number = 2, user_cnt, NULL)) AS `screen_view-home` , MAX(IF(step_number = 3, user_cnt, NULL)) AS `screen_view-food_category` , MAX(IF(step_number = 4, user_cnt, NULL)) AS `screen_view-restaurant` , MAX(IF(step_number = 5, user_cnt, NULL)) AS `screen_view-cart` , MAX(IF(step_number = 6, user_cnt, NULL)) AS `click_payment-cart` FROM agg_user_cnt GROUP BY 1 ORDER BY 1 ;
1. ARRAY, STRUCT 연습문제 연습문제 1 SELECT title , genre FROM `advanced.array_exercises` CROSS JOIN UNNEST(genres) AS genre 연습문제 2 SELECT title , actor.actor , actor.character FROM `advanced.array_exercises` CROSS JOIN UNNEST(actors) AS actor 연습문제 3 SELECT title , actor.actor AS actor , actor.character AS character , genre FROM `advanced.array_exercises` CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre [메모] 여기서 CROSS JOIN 다음 라인에 WHERE actor = 'Chris Evans' 이렇게 조건을 걸어줄 경우 오류가 발생한다. 오류는 실행 순서와 관련이 있다. 실행 순서: FROM -> JOIN -> SELECT 따라서 SELECT 에서 알리아스로 이름 붙인 actor 가 아닌, CROSS JOIN 결과를 사용하여 조건을 만들어야 한다. => WHERE actor.actor = 'Chris Evans' 연습문제 4 SELECT user_id , event_date , event_name , user_pseudo_id , parameter.key AS key , parameter.value.string_value AS string_value , parameter.value.int_value AS int_value FROM `inflearn-bigquery-437203.advanced.app_logs` CROSS JOIN UNNEST (event_params) AS parameter WHERE event_date = "2022-08-01" 2. PIVOT 연습문제 연습문제 1 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 `inflearn-bigquery-437203.advanced.orders` GROUP BY order_date ORDER BY order_date 연습문제 2 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 `inflearn-bigquery-437203.advanced.orders` GROUP BY user_id ORDER BY user_id [메모] 알리아스로 컬럼명 지정할 때, 영어 제외하고 다른 문자열가 포함될 경우 → backtick (`) 으로 감싸준다. 연습문제 3 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 `inflearn-bigquery-437203.advanced.orders` GROUP BY user_id ORDER BY user_id 연습문제 4 SELECT event_date , event_timestamp , event_name , user_id , user_pseudo_id , MAX(IF(params.key = 'firebase_screen', params.value.string_value, NULL)) AS firebase_screen , MAX(IF(params.key = 'food_id', params.value.int_value, NULL)) AS food_id , MAX(IF(params.key = 'session_id', params.value.string_value, NULL)) AS session_id FROM `inflearn-bigquery-437203.advanced.app_logs` CROSS JOIN UNNEST(event_params) AS params WHERE event_date = '2022-08-01' GROUP BY ALL 3. 퍼널 쿼리 연습문제 연습문제 1 : 각 퍼널별 유저 수 집계 -- 퍼널 단계: 6 -- screen_view(welcome) -- screen_view(home) -- screen_view(food_category) -- screen_view(restaurant) -- screen_view(cart) -- click_payment(cart) WITH funnel AS ( SELECT CONCAT(event_name, '-', param.value.string_value) AS event_name_with_screen , CASE WHEN event_name = 'screen_view' AND param.value.string_value = 'welcome' THEN 1 WHEN event_name = 'screen_view' AND param.value.string_value = 'home' THEN 2 WHEN event_name = 'screen_view' AND param.value.string_value = 'food_category' THEN 3 WHEN event_name = 'screen_view' AND param.value.string_value = 'restaurant' THEN 4 WHEN event_name = 'screen_view' AND param.value.string_value = 'cart' THEN 5 WHEN event_name = 'click_payment' AND param.value.string_value = 'cart' THEN 6 END AS step_number , user_pseudo_id FROM `inflearn-bigquery-437203.advanced.app_logs` CROSS JOIN UNNEST(event_params) AS param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_name IN ('screen_view', 'click_payment') AND param.key = 'firebase_screen' AND param.value.string_value IN ('welcome', 'home', 'food_category', 'restaurant', 'cart') ) SELECT event_name_with_screen , MAX(step_number) AS step_number , COUNT(DISTINCT user_pseudo_id) AS cnt FROM funnel GROUP BY event_name_with_screen 연습문제 2 : 일자별 각 퍼널의 유저 수 집계 -- 퍼널 단계: 6 -- screen_view(welcome) -- screen_view(home) -- screen_view(food_category) -- screen_view(restaurant) -- screen_view(cart) -- click_payment(cart) WITH funnel AS ( SELECT event_date -- 날짜 컬럼 추가 , CONCAT(event_name, '-', param.value.string_value) AS event_name_with_screen , CASE WHEN event_name = 'screen_view' AND param.value.string_value = 'welcome' THEN 1 WHEN event_name = 'screen_view' AND param.value.string_value = 'home' THEN 2 WHEN event_name = 'screen_view' AND param.value.string_value = 'food_category' THEN 3 WHEN event_name = 'screen_view' AND param.value.string_value = 'restaurant' THEN 4 WHEN event_name = 'screen_view' AND param.value.string_value = 'cart' THEN 5 WHEN event_name = 'click_payment' AND param.value.string_value = 'cart' THEN 6 END AS step_number , user_pseudo_id FROM `inflearn-bigquery-437203.advanced.app_logs` CROSS JOIN UNNEST(event_params) AS param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_name IN ('screen_view', 'click_payment') AND param.key = 'firebase_screen' AND param.value.string_value IN ('welcome', 'home', 'food_category', 'restaurant', 'cart') ) SELECT event_date -- 날짜 컬럼 추가 , event_name_with_screen , MAX(step_number) AS step_number , COUNT(DISTINCT user_pseudo_id) AS cnt FROM funnel GROUP BY ALL ORDER BY event_date, step_number 연습문제 3 : 일자별 각 퍼널의 유저 수 집계한 결과 → PIVOT 하기 -- 퍼널 단계: 6 -- screen_view(welcome) -- screen_view(home) -- screen_view(food_category) -- screen_view(restaurant) -- screen_view(cart) -- click_payment(cart) WITH funnel AS ( SELECT event_date , CONCAT(event_name, '-', param.value.string_value) AS event_name_with_screen , CASE WHEN event_name = 'screen_view' AND param.value.string_value = 'welcome' THEN 1 WHEN event_name = 'screen_view' AND param.value.string_value = 'home' THEN 2 WHEN event_name = 'screen_view' AND param.value.string_value = 'food_category' THEN 3 WHEN event_name = 'screen_view' AND param.value.string_value = 'restaurant' THEN 4 WHEN event_name = 'screen_view' AND param.value.string_value = 'cart' THEN 5 WHEN event_name = 'click_payment' AND param.value.string_value = 'cart' THEN 6 END AS step_number , user_pseudo_id FROM `inflearn-bigquery-437203.advanced.app_logs` CROSS JOIN UNNEST(event_params) AS param WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' AND event_name IN ('screen_view', 'click_payment') AND param.key = 'firebase_screen' AND param.value.string_value IN ('welcome', 'home', 'food_category', 'restaurant', 'cart') ), -- 일자별 각 퍼널의 유저수 집계 funnel_daily AS( SELECT event_date , event_name_with_screen , MAX(step_number) AS step_number , COUNT(DISTINCT user_pseudo_id) AS cnt FROM funnel GROUP BY ALL ORDER BY event_date, step_number ) -- 일자별 각 퍼널의 유저수 집계 -> 피벗하기 SELECT event_date , SUM(IF(event_name_with_screen = 'screen_view-welcome', cnt, 0)) AS `screen_view-welcome` , SUM(IF(event_name_with_screen = 'screen_view-home', cnt, 0)) AS `scree_view-home` , SUM(IF(event_name_with_screen = 'screen_view-food_category', cnt, 0)) AS `screen_view-food_category` , SUM(IF(event_name_with_screen = 'screen_view-restaurant', cnt, 0)) AS `screen_view-restaurant` , SUM(IF(event_name_with_screen = 'screen_view-cart', cnt, 0)) AS `screen_view-cart` , SUM(IF(event_name_with_screen = 'click_payment-cart', cnt, 0)) AS `click_payment-cart` FROM funnel_daily GROUP BY event_date ORDER BY event_date
1-4. Array, Struct 연습문제 (1~4번) 연습문제1 문제 array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요 쿼리 select title, genres2 from advanced.array_exercises cross join unnest(genres) as genres2 ; 결과 연습문제2 문제 array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야 합니다 쿼리 select title, actor.actor, actor.character from advanced.array_exercises cross join unnest(actors) as actor order by movie_id ; 결과 연습문제3 문제 array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르 (genre)를 출력하세요. 한 Row에 배우, 배역, 장르가 모두 표시되어야 합니다 쿼리 select title, actor.actor, actor.character genre2 # array<string> from advanced.array_exercises cross join unnest (actors) as actor cross join unnest (genres) as genre order by movie_id ; 결과 연습문제4 문제 앱 로그 데이터(app_logs)의 배열을 풀어주세요 쿼리 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" limit 10 ; 결과 1-9. 퍼널 SQL 쿼리 작성하기 연습문제1 문제 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 쿼리 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 1 ; 결과 연습문제2 문제 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 쿼리 select user_id, # 컬럼의 이름을 지정할때, 영어를 제외하고 backtick(`)사용 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 1 ; 결과 연습문제3 문제 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 쿼리 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 1 ; 결과 연습문제4 문제 앱 로그 데이터 배열 PIVOT하기 - user_id = 32888이 카트 추가하기(click_cart)를 누를때 어떤 음식(food_id)을 담았나요? 쿼리 select event_date, event_timestamp, event_name, user_id, user_pseudo_id, max(if(param.key = 'firebase_screen', param.value.string_value, null)) as firebase_screen, -- max(if(param.key = 'food_id', param.value.string_value, null)) as food_id, max(if(param.key = 'food_id', param.value.int_value, null)) as food_id, max(if(param.key = 'session_id', param.value.string_value, null)) as session_id from advanced.app_logs cross join unnest(event_params) as param where event_date = "2022-08-01" and user_id = 32888 and event_name = "click_cart" group by all limit 100 ; 결과 퍼널 분석 문제 step_number별 count, 일자별 퍼널별 유저 수 쿼리 쿼리 with base as (select event_date, event_timestamp, event_name, user_id, user_pseudo_id, max(if(event_param.key = 'firebase_screen', event_param.value.string_value, null)) as firebase_screen, -- max(if(event_param.key = 'food_id', event_param.value.int_value, null)) as food_id, 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 1=1 and event_date between "2022-08-01" and "2022-08-18" group by all ), filter_event_and_concat_event_and_acreen AS ( -- event_name + screen 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 1=1 and event_name in ("screen_view", "click_payment")) # 일자별로 퍼널별 유저 수 select -- distinct(event_name_with_screen) event_date, 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_acreen group by all having step_number is not null order by event_date ; 결과
1. ARRAY, STRUCT 연습문제 연습 문제 1번 # 1)array_exercises테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요 SELECT title, genre FROM `advanced.array_exercises` AS exercise CROSS JOIN UNNEST(genres) AS genre SELECT title, # 기존에 array_exercises에 저장되어 있던 컬럼 genre FROM `advanced.array_exercises` AS ae, UNNEST(genres) AS genre # ARRAY : 같은 타입의 여러 데이터를 저장하고 싶을 때 # ARRAY를 Flatten(평면화) => UNNEST # UNNEST를 할 때는 CROSS JOIN + UNNEST(ARRAY_COLUMN) # UNNEST(ARRAY_COLUMN) AS 새로운 이름 # SELECT 절에서 새로운 이름으로 시작한다. 기존의 ARRAY_COLUMN은 사용하지 않는다! 연습 문제 2번 # 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` AS ae, UNNEST(actors) AS actor FROM `advanced.array_exercises` AS ae CROSS JOIN UNNEST(actors) AS actor # actors = [STRUCT(STRING, STRING)] 연습 문제 3번 # 3) array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르(genre)를 출력하세요. 한 Row에 배우, 배역, 장르가 모두 표시 되어야 합니다 SELECT title, actor.actor, actor.character, genre FROM `advanced.array_exercises` AS ae ,UNNEST(actors) AS actor, UNNEST(genres) as genre -- FROM `advanced.array_exercises` AS ae -- CROSS JOIN UNNEST(actors) AS actor -- CROSS JOIN UNNEST(genres) as genre # 이 문제의 의도 : UNNEST를 2번 연속 사용할 수 있다. # CROSS JOIN => JOIN 연속 2번과 맥락은 동일한데, UNNEST라는 것이 어색할 수 있었다 # 데이터의 중복이 어느정도 생기는데, 그것은 어쩔 수 없는 이슈(CROSS JOIN) -- FROM `advanced.array_exercises` AS ae -- CROSS JOIN UNNEST(actors) AS actor, UNNEST(genres) as genre SELECT title, actor.actor, actor.character, genre FROM `advanced.array_exercises` AS ae ,UNNEST(actors) AS actor, UNNEST(genres) as genre -- FROM `advanced.array_exercises` AS ae -- CROSS JOIN UNNEST(actors) AS actor -- CROSS JOIN UNNEST(genres) as genre # 이 문제의 의도 : UNNEST를 2번 연속 사용할 수 있다. # CROSS JOIN => JOIN 연속 2번과 맥락은 동일한데, UNNEST라는 것이 어색할 수 있었다 # 데이터의 중복이 어느정도 생기는데, 그것은 어쩔 수 없는 이슈(CROSS JOIN) -- FROM `advanced.array_exercises` AS ae -- CROSS JOIN UNNEST(actors) AS actor, UNNEST(genres) as genre WHERE actor.actor = 'Chris Evans' AND genre = 'Action' -- WHERE actor = 'Chris Evans' (X) # 실행 순서 : FROM -> JOIN -> SELECT # actors : ARRAY<STRUCT> => UNNEST => STRUCT # genres : ARRAY<STRING> => STRING 연습 문제 4번 # 4) 앱 로그 데이터(app_logs)의 배열을 풀어주세요 SELECT event_date, event_timestamp, event_name, event_param.key as key, event_param.value as value, event_param.value.string_value as string_value, event_param.value.int_value as int_value, -- event_params, user_id, event_param FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS event_param WHERE event_date ='2022-08-01' 2. PIVOT PIVOT 연습 문제 1 ## SubQuery 방식 SELECT order_date, SUM(IF(user_id = 1, sum_of_amount, NULL)) AS user_1, SUM(IF(user_id = 2, sum_of_amount, NULL)) AS user_2, SUM(IF(user_id = 3, sum_of_amount, NULL)) AS user_3 -- MAX를 써도 동일한 결과 값이 나옴 -- 그룹화 할때 값이 하나밖에 없음 FROM ( SELECT order_date, user_id, # Amount의 합 SUM(amount) AS sum_of_amount FROM advanced.orders GROUP BY order_date, user_id -- ORDER BY order_date ) GROUP BY order_date ORDER BY order_date PIVOT 연습 문제 2 # ANY_VALUE 활용 -- ANY_VALUE : 그룹화 할 대상 중에 임의의 값을 선택한다 (NULL)을 제외하고 -- ANY_VALUE에선 나머지 값들이 NULL 이거나 확정적으로 이 값이 나올 것이다 기대할 때 사용한다 SELECT user_id, ANY_VALUE(IF(order_date = PARSE_DATE('%Y-%m-%d', '2023-05-01'), amount, NULL)) AS `2023-05-01`, ANY_VALUE(IF(order_date = date('2023-05-02'), amount, NULL)) AS `2023-05-02`, ANY_VALUE(IF(order_date = date('2023-05-03'), amount, NULL)) AS `2023-05-03`, ANY_VALUE(IF(order_date = date('2023-05-04'), amount, NULL)) AS `2023-05-04`, ANY_VALUE(IF(order_date = date('2023-05-05'), amount, NULL)) AS `2023-05-05` FROM `advanced.orders` GROUP BY user_id ORDER BY user_id PIVOT 연습 문제 3 # 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, # amount 대신 1 이라고 표시. IF 문 안에 TRUE 일 때의 값이 항상 특정 컬럼이 아니라 1이라고 할 수도 있음(유무에 따라서) MAX(IF(order_date = PARSE_DATE('%Y-%m-%d', '2023-05-01'), 1, 0)) AS `2023-05-01`, MAX(IF(order_date = date('2023-05-02'), 1, 0)) AS `2023-05-02`, MAX(IF(order_date = date('2023-05-03'), 1, 0)) AS `2023-05-03`, MAX(IF(order_date = date('2023-05-04'), 1, 0)) AS `2023-05-04`, MAX(IF(order_date = date('2023-05-05'), 1, 0)) AS `2023-05-05` FROM `advanced.orders` GROUP BY user_id PIVOT 연습 문제 4 앱 로그 데이터 배열 PIVOT 하기 WITH base AS ( SELECT event_date, event_name, user_id, user_pseudo_id, event_timestamp, MAX(IF(param.key = 'firebase_screen', param.value.string_value, NULL)) AS firebase_screen, MAX(IF(param.key = 'food_id', param.value.int_value, NULL)) AS food_id, MAX(IF(param.key = 'session_id', param.value.string_value, NULL)) AS session_id FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS param GROUP BY ALL -- WHERE event_name ='click_cart' ORDER BY user_pseudo_id LIMIT 100 ) SELECT event_date, COUNT(user_id) AS user_cnt FROM base WHERE event_name ='click_cart' GROUP BY event_date ORDER BY event_date 3. 퍼널 분석 -- event_name + screen (필요한 이벤트만 WHERE 조건에 걸어서 사용) -- step_number + COUNT WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, platform, -- param MAX(IF(param.key = 'firebase_screen', param.value.string_value, NULL)) AS firebase_screen, MAX(IF(param.key = 'food_id', param.value.int_value, NULL)) AS food_id, MAX(IF(param.key = 'session_id', param.value.string_value, NULL)) AS session_id, FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS param WHERE event_date = '2022-08-01' GROUP BY ALL -- LIMIT 100 ), filter_event_and_concat_event_and_screen AS ( SELECT * EXCEPT(event_name, firebase_screen), CONCAT(event_name, "-", firebase_screen) AS event_name_with_screen FROM base WHERE event_name IN ("screen_view", "click_payment") ) SELECT * FROM filter_event_and_concat_event_and_screen 최종 RESULT # 일자 상관 없이 퍼널의 유저 수를 집계한 쿼리 => 일자별로 하기 위해 event_date 추가 WITH base AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, platform, -- param MAX(IF(param.key = 'firebase_screen', param.value.string_value, NULL)) AS firebase_screen, MAX(IF(param.key = 'session_id', param.value.string_value, NULL)) AS session_id, FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS param WHERE -- event_date = '2022-08-01' # 적은 데이터로 쿼리를 작성하기 위해 만들어둔 조건 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), 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") ) -- step_number + COUNT -- step_number : CASE WHEN 을 사용해 숫자 지정, 조건문을 여러 개 하고싶을 때 사용하는 함수 # 일자 상관 없이 퍼널의 유저 수를 집계한 쿼리 => 일자별로 하기 위해 event_date 추가 SELECT event_date, # 일자별로 퍼널별 유저 수 쿼리 event_name_with_screen, -- event_name_with_screen, -- event_datetime, user_pseudo_id, 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 ORDER BY event_date
안녕하세요, 오랜만에 흥미로운 강의를 알게되어 주말동안 강의를 몰아보다보니 9강까지 듣게 되었네요. 23분 59초 정도에 setter를 지양하기 떄문에 custom setter를 잘 사용하지 않고, update함수를 만들어 사용한다 라는 내용에 예시가 있다면 알 수 있을까요? java 프로젝트를 활용할 때 setter를 커스텀하게 수정해서 쓰는 경우가 아주 간혹 값이 업데이트 될 때 다른 필드를 함께 업데이트 해야하는 케이스들 때문에 사용했던 기억이 있는데, setter를 사용하지 않고 update를 사용한다는게 어떤 말씀이신지 조금 더 상세히 알려주시면 감사하겠습니다!
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
