牛客SQL—大厂（七大场景）面试真题

一、某音短视频

-- 各个视频的平均完播率
select t1.video_id,
round(count(if(timestampdiff(second,start_time,end_time) >= duration,1,null)) / count(start_time),3) as avg_comp_play_rate
from tb_user_video_log t1
join tb_video_info t2 using(video_id)
where year(start_time) = '2021'
group by t1.video_id
order by avg_comp_play_rate desc;

-- 平均播放进度大于60%的视频类别
select tag,
concat(round(avg_play_progress*100, 2), '%') avg_play_progress from(
select tag,
avg(case when timestampdiff(second, start_time,end_time)>duration then 1
	 else timestampdiff(second, start_time,end_time)/duration end) avg_play_progress
from tb_user_video_log t1
join tb_video_info t2 using(video_id)
group by tag) t
where avg_play_progress>0.6
group by tag
order by avg_play_progress desc;

-- 每类视频近一个月的转发量/率
select tag, round(retweet_cut/play_num, 3) retweet_rate from(
select tag,
sum(if_retweet) retweet_cut,
count(*) play_num,
start_time,
date_add(max(date(start_time)) over(),interval -29 day) min_date,
max(start_time) over() max_date
from tb_user_video_log t1
join tb_video_info t2 
using(video_id)
group by tag) t
where start_time between min_date and max_date
group by tag
order by retweet_rate desc;


-- 每个创作者每月的涨粉率及截止当前的总粉丝量
select author, month,fans_growth_rate, sum(t_fans) over(partition by author order by author, month) as total_fans
from (select author, month, round(sum(new_if_follow)/count(*), 3) as fans_growth_rate, 
     sum(new_if_follow) as t_fans
     from (select a.video_id, b.author, date_format(a.start_time, '%Y-%m') as month, 
          if(a.if_follow=2, -1, a.if_follow) as new_if_follow
          from tb_user_video_log a 
          join tb_video_info b on a.video_id = b.video_id
          where year(a.start_time)='2021') a
     group by author, month) a
order by author, total_fans;

-- 国庆期间每类视频点赞量和转发量
WITH new AS(
    SELECT t2.tag,DATE(t1.start_time) as dt,
    sum(t1.if_like) as like_cnt,sum(t1.if_retweet) as retweet_cnt
    FROM tb_user_video_log AS t1 
    LEFT JOIN tb_video_info AS t2 USING(video_id)
    WHERE DATE(t1.start_time) BETWEEN '2021-09-25' and '2021-10-03'
    GROUP BY t2.tag,dt
    ORDER BY dt)
SELECT * FROM(
    SELECT tag,dt,
    sum(like_cnt) over(partition by tag order by dt rows 6 preceding) AS sum_like_cnt_7d,
    max(retweet_cnt) over(partition by tag order by dt rows 6 preceding) AS retweet_cnt 
    FROM new) AS t
WHERE t.dt BETWEEN '2021-10-01' and '2021-10-03'
ORDER BY t.tag DESC,t.dt;

-- 近一个月发布的视频中热度最高的top3视频
SELECT
	t1.video_id,
	round((100 * t1.play_rate + 5 * t1.likes + 3 * t1.comments + 2 * t1.retweets )*(1 /(t1.days + 1 )),0 ) AS hot_index 
from (
select a.video_id,
	sum(if(TIMESTAMPDIFF(SECOND,start_time,end_time)>= duration,1,0))/count(a.video_id) play_rate, 
	sum(if_like) likes,count(comment_id) comments,sum(if_retweet) retweets,
    DATEDIFF((select max(end_time) from tb_user_video_log), max(end_time)) days
	from tb_user_video_log a
	inner join tb_video_info b
	on a.video_id = b.video_id
	WHERE DATEDIFF(DATE((SELECT MAX(end_time) FROM tb_user_video_log)), DATE(release_time)) <= 29
	group by a.video_id) t1
order by hot_index desc
limit 3;

二、用户增长场景（某度信息流）

--  2021年11月每天的人均浏览文章时长
select
  date_format(in_time, '%Y-%m-%d') dt,
  round(sum(timestampdiff(second, in_time, out_time))/count(distinct uid), 1) avg_viiew_len_sec
from tb_user_log
where
  date_format(in_time, '%Y%m')=202111 and artical_id !=0
group by dt
order by avg_viiew_len_sec;

-- 每篇文章同一时刻最大在看人数
select artical_id,max(cnt) max_uv from 
(select artical_id,
sum(num) over (partition by artical_id order by dt asc,num desc) as cnt
from
(select artical_id,in_time dt,1 num from tb_user_log
where artical_id != 0
union all 
select artical_id,out_time dt,-1 num from tb_user_log
where artical_id != 0 ) as a) as b
group by artical_id
order by max_uv desc;

-- 2021年11月每天新用户的次日留存率
select a.dt, round(count(b.uid)/count(a.uid), 2) uv_left_rate from(
-- 求最小活跃日期
select uid, min(date_format(in_time, '%Y-%m-%d')) dt
from tb_user_log
group by uid) a
left join
-- 求所有活跃日期
(select uid, date_format(in_time, '%Y-%m-%d') dt
from tb_user_log
union
select uid, date_format(out_time, '%Y-%m-%d') dt
from tb_user_log) b
on a.uid=b.uid and a.dt=date_sub(b.dt, interval 1 day)
where date_format(a.dt,"%Y-%m")='2021-11'
group by dt;

-- 统计活跃间隔对用户分级结果
with temp as 
(
select 
(
    case when datediff('2021-11-04',first_in)<7 then '新晋用户'
    when datediff('2021-11-04',first_in)>=7 and 
    datediff('2021-11-04',last_in)<7 then '忠实用户'
    when datediff('2021-11-04',last_in)>=30 then '流失用户'
    else '沉睡用户' end
) as user_grade
from
 (
select
      uid,
      date(min(in_time)) as first_in,
      date(max(out_time)) as last_in
 from tb_user_log
 group by uid
) t1 
)

select 
user_grade,
round(count(user_grade)/
     (select count(*) from temp)
      ,2)as ratio
from temp
group by user_grade
order by ratio desc;

-- 每天的日活数及新用户占比
select t1.dt, dau, ifnull(round(uv/dau, 2), 0) uv_new_ratio from
(select dt, count(distinct uid) dau from(
-- 求所有活跃日期
select uid, date_format(in_time, '%Y-%m-%d') dt
from tb_user_log
union
select uid, date_format(out_time, '%Y-%m-%d') dt
from tb_user_log) b
group by dt) t1
left join 
(select dt, count(distinct uid) uv from(
-- 求最小活跃日期
select uid, min(date_format(in_time, '%Y-%m-%d')) dt
from tb_user_log
group by uid) a
group by dt) t2
on t1.dt=t2.dt
order by dt;

-- 连续签到领金币
select uid, month, sum(coin) coin from(
select uid, month, dt,
(case rk%7 when 0 then 7
		  when 3 then 3
          else 1 end) coin from(
select t1.*,
dense_rank() over(partition by uid,drk order by drk) rk from(
-- 连续签到日期
select uid,DATE_FORMAT(dt,'%Y%m') month ,sum(grade) from
     (select uid ,dt,  
     case 
     when   mod( rank() over  (partition by uid,rank_day order by dt),7) =3 then 3     
     when   mod( rank() over (partition by uid,rank_day order by dt) ,7)=0 then 7 
     else 1   end   grade    
     from 
        (
        select
        uid,
        dt,
        date_sub(dt,INTERVAL RANK() over(PARTITION by uid order by dt) day )  rank_day  from
            (
            select DISTINCT uid, date(in_time) dt  #查出所有签到记录 过滤掉可能重复的记录
            from tb_user_log 
            where artical_id=0 and sign_in=1 and date(in_time) BETWEEN '2021-07-07' and '2021-10-31' 
            ) t1
        ) t2
     )t3
group by uid,month;

三、电商场景（某东商城）

-- 计算商城中2021年每月的GMV
select
  date_format(event_time, '%Y-%m') month,
  sum(total_amount) GMV
from tb_order_overall
where year(event_time)=2021 and status in (0,1)
group by month having GMV > 100000
order by GMV;

-- 统计2021年10月每个退货率不大于0.5的商品各项指标
SELECT
    product_id,
    IF(COUNT(event_time) = 0, 0, ROUND(SUM(if_click) / COUNT(event_time), 3)) AS ctr,
    IF(SUM(if_click) = 0, 0, ROUND(SUM(if_cart) / SUM(if_click), 3)) AS cart_rate,
    IF(SUM(if_cart) = 0, 0, ROUND(SUM(if_payment) / SUM(if_cart), 3)) AS payment_rate,
    IF(SUM(if_payment) = 0, 0, ROUND(SUM(if_refund) / SUM(if_payment), 3)) AS refund_rate
FROM tb_user_event
WHERE DATE_FORMAT(event_time, '%Y-%m') = '2021-10'
GROUP BY product_id
HAVING refund_rate <= 0.5
ORDER BY product_id;

-- 某店铺的各商品毛利率及店铺整体毛利率
with new_tab as(select a.product_id,a.shop_id,a.in_price
                ,b.order_id,b.price,b.cnt
                from tb_product_info a , tb_order_detail b ,tb_order_overall c 
                where b.order_id=c.order_id and a.product_id=b.product_id
                and substring(c.event_time,1,7)>='2021-10'
                and a.shop_id=901 and c.status in (1,2))
                
(select '店铺汇总' product_id,CONCAT(round((1-sum(in_price*cnt)/sum(price*cnt))*100,1),'%')
from new_tab)
union all 
(select product_id,CONCAT(round(ratio*100,1),'%')
from (select distinct product_id ,(1-sum(in_price*cnt)/sum(price*cnt)) as ratio
from new_tab group by product_id order by product_id) t where t.ratio>0.249);

-- 零食类商品中复购率top3高的商品
select product_id,round(sum(if(t_cnt=2,1,0))/count(distinct uid),3) repurchase_rate
from(
    select product_id,uid,row_number()over(partition by product_id,uid order by event_time) t_cnt
    from tb_order_detail
    left join tb_order_overall using(order_id)
    left join tb_product_info using(product_id)
    where tag='零食'
    and datediff((select max(event_time) 
                  from tb_order_overall)
                 ,event_time)<90
    and status<>2
) t
where t_cnt<3
group by product_id
order by repurchase_rate desc,product_id
limit 3;

-- 10月的新户客单价和获客成本
with t as(
	select
		order_id,
		avg(total_amount) total_amount,
		sum(price*cnt)-avg(total_amount) diff
	from tb_order_overall t1
	left join tb_order_detail t2 using(order_id)
	where month(event_time)='10' and (uid,event_time) in (
		select uid,min(event_time) from tb_order_overall group by uid
	) group by order_id
)
select 
	round(avg(total_amount),1) avg_amount,
	round(avg(diff),1) avg_cost from t;

-- 店铺901国庆期间的7日动销率和滞销率
select a.dt,
       round(count(distinct b.product_id)/onsale_cnt,3) as sale_rate,
       round(1-(count(distinct b.product_id)/onsale_cnt),3) as unsale_rate
from
(select date(event_time) as dt
from tb_order_overall 
where date(event_time) BETWEEN '2021-10-01' and '2021-10-03') as a
left JOIN
(select date(t2.event_time) as dt,
       t3.product_id
from tb_order_overall t2
join tb_order_detail t3
on t2.order_id=t3.order_id and t2.status=1
join tb_product_info t1
on t1.product_id=t3.product_id and t1.shop_id=901) as b
on datediff(a.dt,b.dt) BETWEEN 0 and 6
JOIN
(select date(event_time) as dt,
       count(distinct case when datediff(date(event_time),date(release_time))>=0 then product_id end) as onsale_cnt
from tb_product_info,tb_order_overall
where shop_id=901
group by dt) as c
on a.dt=c.dt
group by a.dt
order by a.dt;

四、出行场景（某滴打车）

-- 2021年国庆在北京接单3次及以上的司机统计信息
SELECT city, round(avg(cnt), 3) as avg_order_num,
    round(avg(sum_fare), 3) as avg_income
FROM (
    SELECT city, driver_id, count(t1.order_id) as cnt,
        sum(fare) as sum_fare
    FROM tb_get_car_order as t1 JOIN tb_get_car_record as t2
    ON t1.order_id = t2.order_id
    WHERE date(order_time) between '2021-10-01' and '2021-10-07'
    AND city = '北京'
    GROUP BY city, driver_id
    HAVING cnt >= 3
) as t
GROUP BY city;

-- 有取消订单记录的司机平均评分
select IFNULL(driver_id,'总体') driver_id,round(avg(grade),1) avg_grade from tb_get_car_order
where driver_id in (select distinct driver_id from tb_get_car_order
where start_time is NULL and date_format(order_time,'%Y-%m')='2021-10')
and start_time is not null  -- 成功订单才有评分
group by driver_id
with rollup;

-- 每个城市中评分最高的司机信息
with temp as 
(
select
city,
driver_id,
round(avg(grade),1) as avg_grade,
round(count(order_time)/count(distinct date(order_time)),1) 
      as avg_order_num,
round(sum(mileage)/count(distinct date(order_time)),3) 
      as avg_mileage
from tb_get_car_order
join tb_get_car_record using(order_id)
group by city,driver_id
)

select 
city,driver_id,avg_grade,avg_order_num,avg_mileage
from
(select
*,
dense_rank() over (partition by city order by avg_grade desc)
    as rk
from temp) t1
where rk =1
order by avg_order_num;

-- 国庆期间近7日日均取消订单量
select time1,
round(num3/7,2) finish_num_7d,
round(num4/7,2) cancel_num_7d
FROM
(
    select time1,
    sum(num1) over(order by time1 rows between 6 preceding and current row) num3,
    sum(num2) over(order by time1 rows between 6 preceding and current row) num4
    from
    (
        select date(order_time) time1,
        count(*)-sum(if(start_time is null,1,0)) num1,
        sum(if(start_time is null,1,0)) num2
        from tb_get_car_order
        where date(order_time) between '2021-09-25' and '2021-10-03'
        group by date(order_time)
    ) a
) b
where time1 between '2021-10-01' and '2021-10-03';

-- 工作日各时段叫车量、等待接单时间和调度时间
select 
(case when substring_index(r.event_time,' ',-1) between '07:00:00' and '08:59:59' then '早高峰'
    when substring_index(r.event_time,' ',-1) between '09:00:00' and '16:59:59' then '工作时间'
    when substring_index(r.event_time,' ',-1) between '17:00:00' and '19:59:59' then '晚高峰'
    else '休息时间' end) period,
count(r.order_id) get_car_num, 
round(avg(timestampdiff(second,r.event_time,o.order_time))/60,1) avg_wait_time,
round(avg(timestampdiff(second,o.order_time,o.start_time))/60,1) avg_dispatch_time
from tb_get_car_order o inner join tb_get_car_record r on r.order_id=o.order_id
where weekday(r.event_time) not in (5,6)
group by period
order by get_car_num;

-- 各城市最大同时等车人数
with tb as (
    select a.uid,
           a.city,
           a.event_time                                                                              as start_time,
           if(a.order_id is null, a.end_time, if(b.start_time is null, b.finish_time, b.start_time)) as end_time
    from tb_get_car_record as a
             left join tb_get_car_order as b on a.order_id = b.order_id
    where date_format(a.event_time, "%Y-%m") = '2021-10'
)

select t.city,
       max(t.wait_cnt) as max_wait_uv
from (
         select t1.city,
                t2.dt,
                count(t1.uid) as wait_cnt
         from tb as t1
                  inner join (select start_time as dt
                              from tb
                              union
                              select end_time as dt
                              from tb
         ) as t2 on (t2.dt between t1.start_time and t1.end_time)
         group by t1.city, t2.dt
     ) as t
group by t.city
order by max_wait_uv, t.city;

五、某宝店铺分析（电商模式）

-- 某宝店铺的SPU数量
select style_id, count(item_id) SPU_num
from product_tb
group by style_id
order by SPU_num desc;

-- 某宝店铺的实际销售额与客单价
select
  sum(sales_price) sales_total,
  round(sum(sales_price)/count(distinct user_id), 2) per_trans
from sales_tb;

-- 某宝店铺折扣率
select
  round(sum(sales_price)/sum(sales_num*tag_price)*100, 2) 'discount_rate(%)'
from sales_tb s
left join product_tb p
on s.item_id=p.item_id;

--  某宝店铺动销率与售罄率
with a as
(
    select
    style_id
    ,sum(inventory) inventory_total
    ,sum(tag_price * inventory) inventory_price
    from product_tb
    group by 1
),
    b as
(
    select
    style_id
    ,sum(sales_num) sales_num_total
    ,sum(sales_price) gmv
    from sales_tb
    left join product_tb p using(item_id)
    group by 1
)
  
select
style_id
,round(sales_num_total/(inventory_total - sales_num_total)*100,2) pin_rate
,round(gmv/inventory_price*100,2) sell_through_rate
from a
join b 
using(style_id)
order by 1;

-- 某宝店铺连续2天及以上购物的用户及其对应的天数
select user_id, days_count from(
select user_id, dt, count(*) days_count from(
select t1.*, adddate(sales_date, -rk) dt from(
select user_id, sales_date,
dense_rank() over(partition by user_id order by sales_date) rk
from sales_tb
group by user_id, sales_date) t1) t2
group by user_id, dt) t
where days_count>=2;

六、牛客直播课分析（在线教育行业）

-- 牛客直播转换率
select b.course_id,  b.course_name, 
round((a.count1/a.count2)*100,2) as 'sign_rate(%)' from 
(select distinct course_id, sum(if_sign) over (partition by course_id) 
 as count1,
 sum(if_vw) over (partition by course_id) 
 as count2
from behavior_tb) a
join course_tb b on a.course_id=b.course_id
order by b.course_id;

-- 牛客直播开始时各直播间在线人数
select c.course_id,
c.course_name,
count(distinct a.user_id) online_num
from course_tb c
inner join attend_tb a on a.course_id=c.course_id
where time(a.in_datetime) <= '19:00:00'
and time(a.out_datetime) >= '19:00:00'
group by c.course_id,c.course_name
order by c.course_id;

-- 牛客直播各科目平均观看时长
-- 牛客直播各科目平均观看时长
select c.course_name,
       round(avg(timestampdiff(minute,a.in_datetime,a.out_datetime)),2) avg_Len
from attend_tb a join course_tb c on a.course_id = c.course_id
group by c.course_name
order by avg_Len desc;

-- 牛客直播各科目出勤率
select d.course_id, c.course_name, `attend_rate(%)` from(
select a.course_id, round(online_num/attend_num*100, 2) 'attend_rate(%)' from
(select course_id, count(distinct user_id) online_num
from attend_tb
where timestampdiff(minute, in_datetime, out_datetime)>=10
group by course_id
order by course_id) a
left join
(select course_id, count(distinct user_id) attend_num
from behavior_tb
where if_sign=1
group by course_id) b
on a.course_id=b.course_id) d
left join course_tb c
using(course_id);

-- 牛客直播各科目同时在线人数
select b.course_id, c.course_name, max(num) max_num from(
select course_id,
sum(flag) over(partition by course_id order by dt) num from(
select user_id, course_id, in_datetime dt, 1 flag
from attend_tb
union all
select user_id, course_id, out_datetime dt, -1 flag
from attend_tb) a) b
left join course_tb c
using(course_id)
group by b.course_id, c.course_name
order by course_id;

七、某乎回答（内容行业）

-- 某乎问答11月份日人均回答量
SELECT 
    answer_date,
    ROUND(COUNT(issue_id) / COUNT(DISTINCT author_id),
            2) per_num
FROM
    answer_tb
where date_format(answer_date, '%Y-%m')='2021-11'
GROUP BY answer_date
ORDER BY answer_date;

-- 某乎问答高质量的回答中用户属于各级别的数量
select level_cut,count(level_cut) ct
from
(
    select 
    (case when author_level in (1,2) then '1-2级'
          when author_level in (3,4) then '3-4级'
          when author_level in (5,6) then '5-6级' end) level_cut
    from answer_tb an
    left join author_tb au
    on an.author_id = au.author_id
    where char_len >= 100
) t
group by level_cut
order by ct desc;

-- 某乎问答单日回答问题数大于等于3个的所有用户
select answer_date, author_id,
count(issue_id) answer_cnt
from answer_tb
group by answer_date, author_id
having answer_cnt>=3
order by answer_date, author_id;

-- 某乎问答回答过教育类问题的用户里有多少用户回答过职场类问题
SELECT 
  COUNT(DISTINCT author_id) num
FROM answer_tb
WHERE issue_id LIKE 'E%'
      AND
      author_id IN (SELECT 
                      author_id
                    FROM answer_tb
                    WHERE issue_id LIKE 'C%');

-- 某乎问答最大连续回答问题天数大于等于3天的用户及其对应等级
select author_id, author_level, max(days_cnt) days_cnt from(
select t2.author_id, author_level,
count(*) days_cnt from
(select t1.*, adddate(answer_date, -rk) dt from(
select author_id, answer_date,
dense_rank() over(partition by author_id order by answer_date) rk
from answer_tb
group by author_id, answer_date) t1) t2
left join author_tb
using(author_id)
group by author_id, author_level, dt
having days_cnt>=3
order by author_id) t3
group by author_id, author_level;

牛客SQL—大厂（七大场景）面试真题

一、某音短视频

二、用户增长场景（某度信息流）

三、电商场景（某东商城）

四、出行场景（某滴打车）

五、某宝店铺分析（电商模式）

六、牛客直播课分析（在线教育行业）

七、某乎回答（内容行业）

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