// node tree에 설정(부모-자식 관계 설정) bookmarkItem.appendChild(bookmarkInfo); bookmarkItem.appendChild(bookmarkDelBtn); bookmarkInfo.appendChild(bookmarkUrl); bookmarkUrl.appendChild(urlIcon); bookmarkUrl.appendChild(nameElement); urlIcon.append(urlIconImg); bookmarkItemList.appendChild(bookmarkItem); 섹션 7의 6강 수강중에 해당 코드에 의문이 생겨서 질문 드립니다. append와 appendchild 2가지 메소드를 활용하셨는데 두 가지 차이가 검색해 봤을 땐 append는 노드뿐만 아니라 텍스트도 추가 가능하다고해서 appendchild로 바꿔봤더니 오류가 발생했습니다. 두가지 차이가 무엇인지 알 수 있을까요? 그리고 메소드 호출시 괄호가 있는 것과 없는 것의 차이가 궁금합니다. addEbentListener같은 곳에 사용되는 콜백함수에는 괄호를 안 붙여도 되는 건지 궁금합니다.// 1번 document.getElementById("cancel-btn").addEventListener("click", newBookmarkToggle); // 2번 document.getElementById("cancel-btn").addEventListener("click", newBookmarkToggle());

4:19 에서 스크린에 이렇게 나오네요.그리고 , 잠시후 정상적으로 표시됩니다.

22강 모델링 및 평가(회귀) 강의에서 선생님이 푸신 것에서는 랜덤포레스트에서 베이스라인보다 스탠다드스켈러에서 점수가 더 안좋아지는 결과가 나왔는데, 제가 따라서 풀어보면 베이스라인과 스탠다드스켈러의 점수도 동일하게 나오지 않고, 오히려 스탠다드스켈러의 점수가 더 좋게 나옵니다. 이렇게 다른 결과가 나오는 이유가 무엇일까요?

1. ARRAY, STRUCT ### 1) array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요 # 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 사용한다. 기존의 ARRAY_COLUMN은 사용하지 않는다! -- SELECT -- title -- , genre -- FROM `advanced.array_exercises` -- CROSS JOIN UNNEST(genres) AS genre -- ORDER BY 1, 2 ## 같은 결과를 출력하기 위해 정렬함. -- ; ### 2) array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야 합니다. # 직접 접근하려면 actors = [ STRUCT(STRING, STRING)] # actors[SAFE_OFFSET(0)].actor # actors[SAFE_OFFSET(0)].character -- SELECT -- title -- , act.actor# AS actor -- , act.character# AS character -- FROM `advanced.array_exercises` -- CROSS JOIN UNNEST(actors) AS act -- ORDER BY 1 -- ; ### 3) array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르(genre)를 출력하세요. 한 Row에 배우, 배역, 장르가 모두 표시되어야 합니다. # 데이터의 중복이 어느정도 생기는데, 그것은 어쩔 수 없는 이슈(CROSS JOIN) -- SELECT -- title -- -- actors, # ARRAY<STRUCT(STRING, STRING)> -- , act.actor# AS actor -- , act.character# AS character -- -- genres # ARRAY<STRING> -- , genre -- FROM `advanced.array_exercises` -- CROSS JOIN UNNEST(actors) AS act -- CROSS JOIN UNNEST(genres) AS genre -- -- WHERE 1=1 -- -- ## 강의 촬영 시점 이후에 수정된 듯 두 쿼리 모두 오류없이 실행 되는 것 같아요 ! -- -- AND act.actor = "Chris Evans" -- -- AND actor = "Chris Evans" -- ORDER BY 1 -- ; ### 4) 앱 로그 데이터(app_logs)의 배열을 풀어주세요. -- SELECT -- user_id -- , event_date -- , event_name -- , user_pseudo_id -- , evt_prm.key AS key -- , evt_prm.value.string_value AS string_value -- , evt_prm.value.int_value AS int_value -- FROM `advanced.app_logs` -- CROSS JOIN UNNEST(event_params) AS evt_prm -- WHERE 1=1 -- AND event_date = "2022-08-01" -- ORDER BY 2 -- ; ### WITH 문 변경 WITH base AS ( SELECT user_id , event_date , event_name , user_pseudo_id , evt_prm.key AS key , evt_prm.value.string_value AS string_value , evt_prm.value.int_value AS int_value FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS evt_prm WHERE 1=1 AND event_date = "2022-08-01" ) SELECT event_date , event_name , COUNT(DISTINCT user_id) AS cnt FROM base GROUP BY ALL ORDER BY cnt DESC 2. PIVOT# 1) orders 테이블에서 유저(user_id)별로 주문금액(amount)의 합계를 PIVOT해주세요. 날짜(order_date)를 행(Row)으로, user_id를 열(Column)으로 만들어야 합니다. -- 기대하는 output의 형태 -- order_date | user_1 | user_2 | user_3 -- PIVOT : MAX(IF(조건, TRUE일 때의 값, FALSE일 때의 값)) AS new_column + GROUP BY -- MAX 대신 집계 함수를 사용할 수도 있음. SUM -- FALSE일 때의 값은 NULL -- 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)의 합계를 PIVOT 해주세요.user_id를 행(Row)으로, order_date를 열(Column)으로 만들어야 합니다. -- 기대하는 output의 형태 -- user_id | 2023-05-01 | 2023-05-02 | 2023-05-03 | 2023-05-04 | 2023-05-05 -- 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` -- 컬럼의 이름을 지정할 때, 영어 제외하고 backtick(`) -- ANY_VALUE : 그훕화 할 대상 중에 임의의 값을 선택한다 (NULL을 제외하고). ANY_VALUE에선 나머지 값들이 NULL이거나 확정적으로 값을 기대할 수 있을 때 사용한다! -- ANY_VALUE(IF(order_date="2023-05-01", amount, NULL)) AS `2023-05-01` -- FROM `advanced.orders` -- GROUP BY 1 -- ORDER 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 -- ORDER BY 1 -- ; ## 앱 로그 PIVOT WITH base AS( SELECT -- * EXCEPT(event_params) # * EXCEPT(column) : 컬럼을 제외하고 다 보여줘! event_date , event_timestamp , event_name , user_id , user_pseudo_id , MAX(IF(param.key = "fierbase_screen", param.value.string_value, NULL)) AS fierbase_screen -- , MAX(IF(param.key = "food_id", param.value.string_value, NULL)) AS food_id # string_value엔 food_id 값들이 저장되어 있지 않음. , 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 sessioon_id FROM `advanced.app_logs` CROSS JOIN UNNEST(event_params) AS param WHERE 1=1 AND event_date = "2022-08-01" GROUP BY ALL ) SELECT event_date , COUNT(user_id) AS user_cnt FROM base WHERE 1=1 AND event_name = "click_cart" -- AND food_id = 1544 GROUP BY event_date 3. 퍼널 연습 문제# 퍼널 분석 -- 퍼널 데이터 -- 우리가 사용할 이벤트 => 단계 -- - screen_view : welcome, home, food_category, restaurant, cart -- - click_payment -- step_number : 추후에 정렬을 위해 만들 것 -- 사용할 데이터 : 앱 로그 데이터, GA/Firebase => UNNEST => PIVOT -- 기간 : 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.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 ), base2 as ( SELECT * , CONCAT(event_name, "-", firebase_screen) as event_screen FROM base WHERE 1=1 AND event_name IN ("screen_view", "click_payment") ), base3 as ( SELECT event_screen , event_date , CASE WHEN event_screen = "screen_view-welcome" THEN 1 WHEN event_screen = "screen_view-home" THEN 2 WHEN event_screen = "screen_view-food_category" THEN 3 WHEN event_screen = "screen_view-restaurant" THEN 4 WHEN event_screen = "screen_view-cart" THEN 5 WHEN event_screen = "click_payment-cart" THEN 6 ELSE NULL END as step_number , COUNT(DISTINCT user_pseudo_id) as cnt FROM base2 GROUP BY ALL HAVING step_number is not NULL ORDER BY event_date ) SELECT event_date , MAX(IF(base3.event_screen ="screen_view-welcome", cnt, NULL)) AS screen_view_welcome , MAX(IF(base3.event_screen ="screen_view-home", cnt, NULL)) AS screen_vie_home , MAX(IF(base3.event_screen ="screen_view-food_category", cnt, NULL)) AS screen_view_food_category , MAX(IF(base3.event_screen ="screen_view-restaurant", cnt, NULL)) AS screen_view_restaurant , MAX(IF(base3.event_screen ="screen_view-cart", cnt, NULL)) AS screen_view_cart FROM base3 GROUP BY ALL ORDER BY event_date

[ARRAY, STRUCT] 문제 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 genre쿼리 결과 1)문제 2) array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요.SELECT title, actor.actor, actor.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor쿼리 결과 2) 문제 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(genres) AS genre CROSS JOIN UNNEST(actors) AS actor쿼리 결과 3)문제 4) 앱 로그 데이터(app_logs) 배열 풀기SELECT 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 WHERE event_date = "2022-08-01" LIMIT 100쿼리 결과 4)[PIVOT]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.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 ALL ORDER BY order_date쿼리 결과 1)문제 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 합니다SELECT 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 ALL ORDER BY user_id 쿼리 결과 2)문제 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 처리합니다SELECT 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 ALL ORDER BY user_id 쿼리 결과 3)문제 4)user_id = 32888이 카트 추가하기(click_cart)를 누를때 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 CROSS JOIN UNNEST(event_params) AS event_param GROUP BY ALL ) SELECT user_id, event_date, COUNT(user_id) AS user_cnt, food_id FROM base WHERE user_id = 32888 and event_name = 'click_cart' GROUP BY ALL쿼리 결과 4) [퍼널분석]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 쿼리 결과

Array, Struct 연습문제# 1) array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요 SELECT title, ge FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(genres) AS ge # 2) array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야 합니다 SELECT title, ac.actor, ac.character FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(actors) AS ac # 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, ac.actor AS actor, ac.character AS character, genre FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(actors) AS ac CROSS JOIN UNNEST(genres) AS genre # 4) 앱 로그 데이터(app_logs)의 배열을 풀어주세요 SELECT event_date, event_timestamp, event_name, ep.key AS key, ep.value.string_value AS string_value, ep.value.int_value AS int_value, user_id FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS ep최근에 입사해서 야근 때문에 시간이 부족해 Array와 Struct까지만 제출합니다.입문편과 활용편을 병행하고 있는데, 2주차는 2주차 과제와 병행하며 1주차 과제에 추가하겠습니다.

1. ARRAY, STRUCT 1)SELECT title, movie_genres FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS movie_genres LIMIT 1002)SELECT title, actor.actor, actor.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor3)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'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 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_date2)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_id3)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_id4)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 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 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  

ARRAY, STRUCT 연습 문제각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요.SELECT title, genre FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(genres) AS genre각 영화(title)별 배우(actor)와 배역(character)을 보여주세요.(별도 칼럼)SELECT title, actor.actor, actor.character FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(actors) AS actor각 영화(title)별로 배우(actor),배역(character),장르(genre)를 출력하세요. SELECT title, actor, character, genre FROM advanced.array_exercises AS ae CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre 앱 로그 데이터(app_logs)의 배열을 풀어주세요.SELECT event_date, event_timestamp, 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 PIVOT 연습 문제유저(user_id)별로 주문 금액(amount)의 합계를 PIVOT (날짜를 행, user_id를 열)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_date날짜별로 유저들의 주문금액의 합계를 PIVOT (user_id를 행, order_date를 열)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_id사용자별, 날짜별로 주문이 있다면 1, 없다면 0으로 PIVOT 해주세요 (user_id를 행, order_date를 열)SELECT user_id, IF(SUM(IF(order_date = '2023-05-01', amount, 0))>0,1,0) AS `2023-05-01`, IF(SUM(IF(order_date = '2023-05-02', amount, 0))>0,1,0) AS `2023-05-03`, IF(SUM(IF(order_date = '2023-05-03', amount, 0))>0,1,0) AS `2023-05-02`, IF(SUM(IF(order_date = '2023-05-04', amount, 0))>0,1,0) AS `2023-05-04`, IF(SUM(IF(order_date = '2023-05-05', amount, 0))>0,1,0) AS `2023-05-05` FROM advanced.orders GROUP BY user_id ORDER BY user_iduser_id = 32888이 카트 추가하기(click_cart)를 누를때 어떤 음식(food_id)을 담았나요?SELECT user_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 FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param WHERE user_id = 32888 AND event_name = 'click_cart' GROUP BY user_id, event_timestamp -- 카트에 담은 음식(food_id): 1559, 1942퍼널 쿼리 연습 문제일자별 이벤트 집계 후 PIVOTWITH funnel_data 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 ( SELECT event_date, event_timestamp, user_pseudo_id, concat(event_name, '-', event_param.value.string_value) AS event_name_with_screen FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event_param WHERE event_param.key = 'firebase_screen' ) AS unnested_app_logs WHERE event_name_with_screen IN ( 'screen_view-welcome', 'screen_view-home', 'screen_view-food_category', 'screen_view-restaurant', 'screen_view-cart', 'click_payment-cart' ) )SELECT event_date, COUNT(IF(step_number = 1, user_pseudo_id, NULL)) AS `screen_view-welcome`, COUNT(IF(step_number = 2, user_pseudo_id, NULL)) AS `screen_view-home`, COUNT(IF(step_number = 3, user_pseudo_id, NULL)) AS `screen_view-food_category`, COUNT(IF(step_number = 4, user_pseudo_id, NULL)) AS `screen_view-restaurant`, COUNT(IF(step_number = 5, user_pseudo_id, NULL)) AS `screen_view-cart`, COUNT(IF(step_number = 6, user_pseudo_id, NULL)) AS `click_payment-cart` FROM funnel_data GROUP BY event_date ORDER BY event_date

1. ARRAY, STRUCT 연습문제문제 1) array_exercise테이블에서 각 영화(title)별로 장르(genres)를 UNNEST 해서 보여주세요SELECT title, genres FROM `analystic-project.advanced.array_exercises` , UNNEST(genres) AS genres ; 문제 2) array_exercises 테이블에서 각 영화(title)별로 배우(actor)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야합니다SELECT title, actors.actor, actors.character FROM `analystic-project.advanced.array_exercises` , UNNEST(actors) AS actors ; 문제 3) array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르 (genre)를 출력하세요. 한 Row에 배우, 배역, 장르가 모두 표시되어야 합니다SELECT title, actors.actor, actors.character, genres FROM `analystic-project.advanced.array_exercises` , UNNEST(actors) AS actors, UNNEST(genres) genres ; 문제 4) 앱 로그 데이터(app_logs) 배열 풀기SELECT user_id, event_date, event_name, user_pseudo_id, pr.key, pr.value.string_value, pr.value.int_value FROM `analystic-project.advanced.app_logs` , UNNEST(event_params) AS pr WHERE event_date = "2022-08-01" LIMIT 1000 ;  2. PIVOT 연습문제 풀이문제 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 합니다SELECT order_date, COALESCE(SUM(IF(user_id = 1, amount, null)),0) AS user_1, COALESCE(SUM(IF(user_id = 2, amount, null)),0) AS user_2, COALESCE(SUM(IF(user_id = 3, amount, null)),0) AS user_3 FROM advanced.orders GROUP BY order_date ORDER BY order_date ; 문제 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 합니다SELECT user_id, COALESCE(SUM(IF(order_date = '2023-05-01', amount, null)),0) AS `2023-05-01`, COALESCE(SUM(IF(order_date = '2023-05-02', amount, null)),0) AS `2023-05-02`, COALESCE(SUM(IF(order_date = '2023-05-03', amount, null)),0) AS `2023-05-03`, COALESCE(SUM(IF(order_date = '2023-05-04', amount, null)),0) AS `2023-05-04`, COALESCE(SUM(IF(order_date = '2023-05-05', amount, null)),0) AS `2023-05-05`, FROM advanced.orders GROUP BY user_id ORDER BY user_id ; 문제 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 처리합니다SELECT user_id, MAX(IF(order_date = '2023-05-01' AND order_id is not null, 1, 0)) AS `2023-05-01`, MAX(IF(order_date = '2023-05-02' AND order_id is not null, 1, 0)) AS `2023-05-02`, MAX(IF(order_date = '2023-05-03' AND order_id is not null, 1, 0)) AS `2023-05-03`, MAX(IF(order_date = '2023-05-04' AND order_id is not null, 1, 0)) AS `2023-05-04`, MAX(IF(order_date = '2023-05-05' AND order_id is not null, 1, 0)) AS `2023-05-05`, FROM advanced.orders GROUP BY user_id ORDER BY user_id ; 문제 4) user_id = 32888이 카트 추가하기(click_cart)를 누를때 어떤 음식(food_id)을 담았나요?WITH app_order_raw AS ( SELECT user_id, event_date, event_name, user_pseudo_id, pr.key, pr.value.string_value, pr.value.int_value FROM advanced.app_logs, UNNEST(event_params) AS pr WHERE event_date = '2022-08-01' ) 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_order_raw GROUP BY user_id, event_date, event_name, user_pseudo_id ;  3. 퍼널분석문제 1) 각 퍼널의 유저 수를 집계 / 데이터 기준: 2022-08-01 ~ 2022-08-18WITH funnel_data_raw AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(pr.key = 'firebase_screen', pr.value.string_value, null)) AS screen_name, CONCAT(event_name, '-', MAX(IF(pr.key = 'firebase_screen', pr.value.string_value, null))) AS event_name_with_screen FROM advanced.app_logs, UNNEST(event_params) AS pr WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' GROUP BY 1,2,3,4,5 ) SELECT 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 END AS step_number, COUNT(DISTINCT user_pseudo_id) AS cnt FROM funnel_data_raw WHERE event_name IN ('screen_view', 'click_payment') AND screen_name IN ('welcome', 'home', 'food_category', 'restaurant', 'cart') GROUP BY 1,2 ORDER BY 2 ; 문제 2) 일자별 퍼널 유저 수 집계WITH funnel_data_raw AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(pr.key = 'firebase_screen', pr.value.string_value, null)) AS screen_name, CONCAT(event_name, '-', MAX(IF(pr.key = 'firebase_screen', pr.value.string_value, null))) AS event_name_with_screen FROM advanced.app_logs, UNNEST(event_params) AS pr WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' GROUP BY 1,2,3,4,5 ) 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 END AS step_number, COUNT(DISTINCT user_pseudo_id) AS cnt FROM funnel_data_raw WHERE event_name IN ('screen_view', 'click_payment') AND screen_name IN ('welcome', 'home', 'food_category', 'restaurant', 'cart') GROUP BY 1,2,3 ORDER BY 1,3 ; 문제 3) 일자별 퍼널 유저 수 집계 형태를 PIVOT형태로 전환하기WITH funnel_data_raw AS ( SELECT event_date, event_timestamp, event_name, user_id, user_pseudo_id, MAX(IF(pr.key = 'firebase_screen', pr.value.string_value, null)) AS screen_name, CONCAT(event_name, '-', MAX(IF(pr.key = 'firebase_screen', pr.value.string_value, null))) AS event_name_with_screen FROM advanced.app_logs, UNNEST(event_params) AS pr WHERE event_date BETWEEN '2022-08-01' AND '2022-08-18' GROUP BY 1,2,3,4,5 ), daily_funnel_user_count 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 END AS step_number, COUNT(DISTINCT user_pseudo_id) AS cnt FROM funnel_data_raw WHERE event_name IN ('screen_view', 'click_payment') AND screen_name IN ('welcome', 'home', 'food_category', 'restaurant', 'cart') GROUP BY 1,2,3 ORDER BY 1,3 ) SELECT event_date, MAX(IF(step_number = 1, cnt, null)) AS `screen_view-welcome`, MAX(IF(step_number = 2, cnt, null)) AS `screen_view-home`, MAX(IF(step_number = 3, cnt, null)) AS `screen_view-food_category`, MAX(IF(step_number = 4, cnt, null)) AS `screen_view-restaurant`, MAX(IF(step_number = 5, cnt, null)) AS `screen_view-cart`, MAX(IF(step_number = 6, cnt, null)) AS `click_payment-cart`, FROM daily_funnel_user_count GROUP BY ALL ORDER BY 1 ;  

<PART 1> ARRAY, STRUCT 연습문제Q1. array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요.-- 출제의도: 배열 UNNEST의 기본 형태를 사용할 수 있는가? SELECT title , genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS genre ORDER BY title;Q2. array_exercise 테이블에서 각 영화(title)별로 배우(actors)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야 합니다.-- 출제의도: 다중 배열 구조에서 UNNEST를 사용할 수 있는가? SELECT title , actor.actor , actor.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor ORDER BY title; Q3. array_exercises 테이블에서 각 영화(title)별로 배우(actor), 배역(character), 장르(genre)를 출력하세요. 한 Row에 배우, 배역, 장르가 모두 표시되어야 합니다.-- 출제의도: 여러 칼럼을 동시에 UNNEST할 수 있는가? SELECT title , actor.actor , actor.character , genre FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre ORDER BY title;Q4. 앱 로그 데이터(app_logs)의 배열을 풀어주세요.-- 출제의도: 다중 struct 구조의 데이터를 평면화하여 쿼리로 호출할 수 있는가? SELECT user_id , event_date , event_name , user_pseudo_id , event.key , event.value.string_value , event.value.int_value FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event WHERE event_date = '2022-08-01';<PART 2> PIVOT 연습문제Q1. 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.-- 출제의도: 집계 함수와 조건 함수를 결합하여 PIVOT 테이블을 만들 수 있는가? 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 ALL ORDER BY order_date; Q2. orders 테이블에서 날짜(order_date)별로 유저들의 주문 금액(amount)의 합계를 PIVOT 해주세요. user_id 를 행(Row)으로, order_date를 열(Column)으로 만들어야 합니다.-- 출제의도 : PIVOT 테이블 구성 시, 날짜 칼럼을 이용하여 시계열 방식을 구성할 수 있는가? 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 ALL ORDER BY user_id; Q3. 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.-- 출제의도 : PIVOT 테이블 구성 시, 집계 함수로 MAX를 사용할 수 있는가? 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 ALL ORDER BY user_id; Q4. user_id = 32888 이 카트 추가하기(click_cart)를 누를 때 어떤 음식 (food_id)을 담았는지 구해주세요. key 를 Column 으로 두고, string_value 나 int_value를 Column의 값으로 설정해서 풀어주세요.-- 출제의도 : PIVOT 테이블을 앱로그 데이터에 사용하여, 조건문으로 개별 유저 데이터를 특정할 수 있는가? WITH base 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 = '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 ) SELECT * FROM base WHERE event_name = 'click_cart' and user_id = 32888 -- 실행결과 : food_id = 1942<PART 3> 퍼널 연습문제-- 출제의도: 앱 로그 데이터에서 원하는 이벤트를 추출해, 퍼널 분석을 위한 전처리를 진행할 수 있는가? -- step 1. UNNEST를 통한 base 데이터 준비 WITH base AS( SELECT event_date , event_timestamp , event_name , event.key AS event_key , event.value.string_value AS event_string_value , event.value.int_value AS event_int_value , user_id , user_pseudo_id , platform FROM advanced.app_logs CROSS JOIN UNNEST(event_params) AS event WHERE event_date BETWEEN '2022-08-01' AND '2022-08-22' ), -- step 2. 필요한 퍼널 이벤트에만 step_number를 세팅하여 준비 sorted_events AS( SELECT event_date , CONCAT(event_name, "-", event_string_value) AS event_name_with_screen , CASE WHEN event_name = 'screen_view' AND event_string_value = 'welcome' THEN 1 WHEN event_name = 'screen_view' AND event_string_value = 'home' THEN 2 WHEN event_name = 'screen_view' AND event_string_value = 'food_category' THEN 3 WHEN event_name = 'screen_view' AND event_string_value = 'restaurant' THEN 4 WHEN event_name = 'screen_view' AND event_string_value = 'cart' THEN 5 WHEN event_name = 'click_payment' AND event_string_value = 'cart' THEN 6 ELSE NULL END AS step_number , user_pseudo_id FROM base WHERE event_key = 'firebase_screen' ) -- step 3. 최종 조회 쿼리 SELECT event_date , event_name_with_screen , step_number , COUNT(DISTINCT user_pseudo_id) AS cnt FROM sorted_events WHERE step_number IS NOT NULL GROUP BY ALL ORDER BY event_date, step_number 

ARRAY-- 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 AS actor, actor.character AS character, genre FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre WHERE actor.actor = 'Chris Evans' AND genre = 'Action'es) AS genre -- 4) 앱 로그 데이터(app_logs)의 배열을 풀어주세요 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 WHERE event_date = '2022-08-01')PIVOT--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 만들어아 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 ) 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, 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 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 퍼널WITH 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 NULL아직 SQL 익숙지 않아서, 강의 들으면서 코드를 이해하려고 했습니다.얼른 빅쿼리 SQL입문 강의도 다 듣고, 2주차에 더 실력이 올라갔으면 좋겠습니다!

1. ARRAY, STRUCT 문제1)SELECT title, genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS genre;2)SELECT title , actor.actor , actor.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor;3)SELECT title , actor.actor , actor.character , genre FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre4)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 = '2022-08-01'2. PIVOT 연습문제1)

1.ARRAY, STRUCT 연습문제/* UNNEST를 사용하는 이유 : 중첩된 데이터를 평평하게 만들어 집계 및 분석을 쉽게 하기 위해 UNNEST된 결과를 사용하여 분석을 실행 : 1.프로그래밍 언어 선호도, 2.지역별 언어 선호도 분석을 통해 Action Itme을 도출 : 프로그래밍 강좌를 제공한다면 선호하는 언어 순으로 영상 제작 등 */ SELECT name, pref_lang, hometown FROM example_data CROSS JOIN UNNEST(preferred_language) AS pref_lang; # UNNEST란 장바구니(배열)에 있는 과일(배열의 값)을 모두 다 꺼내는 것 /* 연습문제 1 UNNEST된 결과를 사용하여 분석을 실행 : 1.영화 장르 선호도 분석을 통해 Action Itme을 도출 : 영화 제작사라면 어떤 장르가 선호되는 것을 보고 영화 제작 */ SELECT title, genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS genre; /* 연습문제 2 UNNEST된 결과를 사용하여 분석을 실행 : X 분석을 통해 Action Itme을 도출 : X */ SELECT title, actor.actor, actor.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor /* 연습문제 3 UNNEST된 결과를 사용하여 분석을 실행 : 1.배우의 영화 장르 선호도 분석을 통해 Action Itme을 도출 : 영화 제작시 배우의 장르 선호도 확인 후 */ SELECT title, actor.actor, actor.character, genre FROM advanced.array_exercises ,UNNEST(actors) AS actor, UNNEST(genres) AS genre /* 연습문제 4 */ WITH base AS ( 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, event_param.value.int_value FROM advanced.app_logs AS al CROSS JOIN UNNEST(event_params) AS event_param WHERE 1=1 AND event_date = '2022-08-01' ) SELECT event_date, event_name, COUNT(DISTINCT user_id) AS cnt FROM base GROUP BY ALL ORDER BY cnt DESC2.PIVOT 연습문제SELECT 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, #Amount의 합 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; SELECT order_id, order_date, user_id, IF(order_date = '2023-05-01', amount, NULL) AS `2023-05-01`, IF(order_date = '2023-05-02', amount, NULL) AS `2023-05-02`, IF(order_date = '2023-05-03', amount, NULL) AS `2023-05-03`, IF(order_date = '2023-05-04', amount, NULL) AS `2023-05-04`, IF(order_date = '2023-05-05', amount, NULL) AS `2023-05-05` FROM advanced.orders; SELECT user_id, # amount 대신 1이라고 표시. IF 문 안에 TRUE 일 때의 값이 항상 특정 컬럼이 아니라 1이라고 할 수도 있음(유무에 따라서) 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; WITH base AS ( SELECT # * EXCEPT(event_params), # * EXCEPT(컬럼) : 컬럼을 제외하고 모두 다 보여줘! 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_id2, 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 event_date, COUNT(user_id) AS user_cnt FROM base WHERE event_name = "click_cart" GROUP BY event_date 3.퍼널 분석 연습문제WITH 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 1=1 AND 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") ) #일자별로 퍼널별 유저 수 쿼리 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

Q1 STRUCT, UNNEST 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 -- genres는 평면화가 된 데이터를 의미 -- genres가 지금 배열 -- ARRAY : 같은 타입의 여러 데이터를 저장하고 싶을 때 -- ARRAY를 풀때 Flattten(평면화) -> UNNEST -- UNNEST릃 할 때는 CROSS JOIN + UNNEST(ARRAY_COLUMN) 컬럼 명시 2) array_exercises 테이블에서 각 영화(title)q별로 배우(actor)와 배역(character)을 보여주세요. 배우와 배역은 별도의 컬럼으로 나와야 함. SELECT title, -- actors -- actor에 직접 접근하면 어떨까 -> 새로운 컬럼으로 가능하나, 매번 SAFE_OFFSET을 지정해야 함 -- actors = [STRUCT(STRING,STRING)] actors[SAFE_OFFSET(0)].actor AS first_actor, actors[SAFE_OFFSET(0)].actor AS first_character, actors[SAFE_OFFSET(1)].actor AS second_actor, actors[SAFE_OFFSET(1)].actor AS second_character -- 배열에 직접 접근이 아닌 UNNEST로 풀어야 편리할 듯 FROM advanced.array_exercises as ae --------------------------------------------------------------- --------------------------------------------------------------- SELECT title, actor.actor, actor.character FROM advanced.array_exercises as ae CROSS JOIN UNNEST(actors) AS actor -- actors가 배열 3) array_exercises 테이블에서 각 영화(title) 별로 배우(actor), 배역(character), 장르(genre)를 출력. 한 row에 배우, 배역, 장르가 모두 표시되어야 함. SELECT title, -- actors, #ARRAY<STRUCT(STRING, STRING)> actor.actor as actor, actor.character as character, -- genres # ARRAY<STRING> genre FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor CROSS JOIN UNNEST(genres) AS genre    Q2 PIVOT 1-1) 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, IF(user_id = 1, amount , NULL) AS user_1, IF(user_id = 2, amount , NULL) AS user_2, IF(user_id = 3, amount , NULL) AS user_3 FROM( SELECT order_date, user_id, amount FROM advanced.orders GROUP BY order_date, user_id, amount ORDER BY order_date ) --------------------------------------------------------------- --------------------------------------------------------------- 1-2) SELECT order_date, MAX(IF(user_id = 1, amount , NULL)) AS user_1, MAX(IF(user_id = 2, amount , NULL)) AS user_2, MAX(IF(user_id = 3, amount , NULL)) AS user_3 FROM( SELECT order_date, user_id, amount FROM advanced.orders GROUP BY order_date, user_id, amount ORDER BY order_date ) 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 order_date, SUM(IF(user_id = 1, amount , NULL)) AS user_1, SUM(IF(user_id = 2, amount , NULL)) AS user_2, SUM(IF(user_id = 3, amount , NULL)) AS user_3 FROM advanced.orders GROUP BY order_date ORDER BY order_date backtick 활용 any value는 어디에 활용할 수 있을지? -> 데이터는 믿을수 없기에 일부 데이터만 보고 사용 판단하기엔 위험할 것 같음. 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 3-1) 주문 여부 1,0 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 3-2) 횟수 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 -- 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 = "food_id", param.value.int_value, NULL)) AS food_id, MAX(IF(param.key = "session_id", param.value.string_value, NULL)) AS session_id -- * EXCEPT(event_params) 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" AND food_id = 1544 GROUP BY event_date    Q3 퍼널 데이터-- 이중 WITH 문 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 BETWEEN "2022-08-01" AND "2022-08-18" GROUP BY ALL ) --event_name + screen (필요한 이벤트만 조건 걸어서 사용) ,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") ) --step_number + COUNT --CASE WHEN 사용 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 -- food_detail, search, search_result도 파악   STRUCT 과 UNNEST 처음 접해보는 내용이라, 복습 필요.PIVOT 내용 중 ANY_VALUE는 데이터 양이 많고, 어떤 데이터들이 어떤 특성을 가지고 담겨있는지 정확하게 모른다면 활용하면 위험하겠다는 생각이 들었음.     

UNNEST1) array_exercises 테이블에서 각 영화(title)별로 장르를(genres) unnest 해서 보여주세요SELECT title, genre FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS genre2) 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; PIVOT1) 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 ASC2) 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_id4) 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_date3. 퍼널WITH 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 

print(df['f1'].dropna())작성하면, 널값 삭제된 f1을 볼 수 있는데 이 값을df['f1']에 대입 후 프린트를 하면 적용이 안됩니다.왜 그런 것인가요?

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?  

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1. ARRAY, STRUCT 1) array_exercises 테이블에서 각 영화(title)별로 장르(genres)를 UNNEST해서 보여주세요SELECT 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 나와야합니다SELECT 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 합니다SELECT 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 풀어주세요SELECT 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. PIVOT1) 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. 퍼널WITH 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 연습문제1-1SELECT title, movie_genres FROM advanced.array_exercises CROSS JOIN UNNEST(genres) AS movie_genres LIMIT 1001-2SELECT title, actor.actor, actor.character FROM advanced.array_exercises CROSS JOIN UNNEST(actors) AS actor1-3SELECT 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'1-4select 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 연습문제2-1select 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_date2-2select 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_id2-3select 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_id2-4select 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 연습문제3-1with 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_date3-2with 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