练习链接：
https://www.nowcoder.com/exam/oj?page=1&tab=SQL%E7%AF%87&topicId=240
1.增删改操作
插入记录
SQL110 插入记录（一）
（1. 插入：insert into 表名 values （第一行记录），（第二行记录）… ;
2.计算增加后的时间： date_add（被增加的时间，interval 增加多少时间）；）
INSERT into exam_record values
(DEFAULT,1001,9001,'2021-09-01 22:11:12',date_add('2021-09-01 22:11:12',interval 50 minute) ,90),
(DEFAULT,1002,9002,'2021-09-04 07:01:02',DEFAULT ,DEFAULT)
SQL111 插入记录（二）
（1.INSERT INTO table2(id, name, address) SELECT id, name, address FROM table1 ；
2.如果简写成INSERT INTO table2 SELECT id, name, address FROM table1形式，需要SELECT后面的列的顺序 必须和目标表中的列的定义顺序完全一致 才能完成正确的数据插入）
insert into exam_record_before_2021(uid,exam_id,start_time,submit_time,score)
select uid,exam_id,start_time,submit_time,score
from exam_record
where submit_time <'2021-01-01'
SQL112 插入记录（三）
（1.第二行字符串要加引号；
2.关键字NULL可以用DEFAULT替代;
3. replace into 表名 values （记录）;
4. REPLACE INTO 跟 INSERT 功能类似。不同点在于：REPLACE INTO 首先尝试插入数据到表中, 如果发现表中已经有此行数据（根据主键或者唯一索引判断）则先删除此行数据，然后插入新的数据;否则，直接插入新数据。要注意的是：插入数据的表必须有主键或者是唯一索引！否则的话，REPLACE INTO 会直接插入数据，这将导致表中出现重复的数据。）
replace into examination_info values
(null,9003,'SQL','hard',90,'2021-01-01 00:00:00')
更新记录
SQL113 更新记录（一）
（1. UPDATE table_name SET column1 = value1, column2 = value2, … WHERE condition;
2.replace与update的区别：replace对一整条记录的值做修改 update可以只修改某个或者某些记录的值）
update examination_info
set tag='Python'
where tag='PYTHON'
SQL114 更新记录（二）
update exam_record
set submit_time='2099-01-01 00:00:00',score=0
where start_time<'2021-09-01' and submit_time is null
删除记录
SQL115 删除记录（一）
（1.时间差函数 timestampdiff(unit, begin,end)，其中unit是(end-begin)的结果的单位，表示为整数。 以下是有效单位：MICROSECOND、SECOND、MINUTE、HOUR、DAY、WEEK、MONTH ；
2. 根据条件删除：DELETE FROM tb_name [where options ] [ [ ORDER BY fields ] LIMIT n ]
全部删除（表清空，包含自增计数器重置）：TRUNCATE tb_name）
delete from exam_record
where timestampdiff(minute,start_time,submit_time)<5
and score<60
SQL116 删除记录（二）
delete from exam_record
where submit_time is null or
timestampdiff(minute,start_time,submit_time) <5
order by start_time
limit 3
SQL117 删除记录（三）
（DROP TABLE, TRUNCATE TABLE, DELETE TABLE 三种删除语句的区别：
1.DROP TABLE 清除数据并且销毁表，是一种数据库定义语言(DDL Data Definition Language), 执行后不能撤销，被删除表格的关系，索引，权限等等都会被永久删除。
2.TRUNCATE TABLE 只清除数据，保留表结构，列，权限，索引，视图，关系等等，相当于清零数据，是一种数据库定义语言(DDL Data Definition Language)，执行后不能撤销。
3.DELETE TABLE 删除（符合某些条件的）数据，是一种数据操纵语言(DML Data Manipulation Language)，执行后可以撤销。
运行速度一般DROP最快，DELETE最慢，但是DELETE最安全。）
truncate exam_record
2.表与索引操作
表的创建、修改与删除
SQL118 创建一张新表
（1. create table 表名（… , … , …）default charset=UTF8 ）
create table user_info_vip(
id int(11) primary key auto_increment comment "自增ID",
uid int(11) unique not null comment "用户ID",
nick_name varchar(64) comment "昵称",
achievement int(11) default 0 comment "成就值",
level int(11) comment "用户等级",
job varchar(32) comment "职业方向",
register_time datetime default CURRENT_TIMESTAMP comment "注册时间"
) default CHARSET=UTF8;
SQL119 修改表
（1.alter table 表名 add/change/modify …；
2.add 增加列的名称 数据类型；
3.change 原列名 修改列名 修改数据类型；
4. modify 修改列名称 数据类型 默认值；）
ALTER TABLE user_info ADD school varchar(15) AFTER level;
ALTER TABLE user_info CHANGE job profession VARCHAR(10);
alter table user_info modify achievement int(11) DEFAULT 0 ;
SQL120 删除表
（1.删除表：DROP TABLE [IF EXISTS] 表名1 [, 表名2]）
DROP TABLE IF EXISTS
exam_record_2011,
exam_record_2012,
exam_record_2013,
exam_record_2014;
索引的创建、删除
（* 1.索引使用时满足最左前缀匹配原则，即对于组合索引(col1, col2)，在不考虑引擎优化时，条件必须是col1在前col2在后，或者只使用col1，索引才会生效；
2.索引不包含有NULL值的列
3.一个查询只使用一次索引，where中如果使用了索引，order by就不会使用
like做字段比较时只有前缀确定时才会使用索引
4.在列上进行运算后不会使用索引，如year(start_time)<2020不会使用start_time上的索引*）
SQL121 创建索引
（1. create index 索引名 ON 表名 – 创建索引；
2. alter table 表名 add [index] 索引名 --alter方式创建索引 ;
3. unique – 唯一索引 ， fulltext – 全文索引 ）
create index idx_duration on examination_info(duration);
create unique index uniq_idx_exam_id on examination_info(exam_id);
create fulltext index full_idx_tag on examination_info(tag);
SQL122 删除索引
（1.drop方式删除索引：DROP INDEX <索引名> ON <表名> ；
2.alter方式删除索引：ALTER TABLE <表名> DROP INDEX <索引名> ）
drop index uniq_idx_exam_id on examination_info;
drop index full_idx_tag on examination_info;
3.聚和分组查询
聚和函数
SQL123 SQL类别高难度试卷得分的截断平均值
select tag,difficulty,
round((sum(score)-max(score)-min(score))/(count(score)-2),1) as clip_avg_score
from exam_record
join examination_info using(exam_id)
where tag='SQL' and difficulty='hard' and score is not null
group by tag,difficulty
SQL124 统计作答次数
select
count(start_time) as total_pv,
count(submit_time) as complete_pv,
count(distinct if(score is not null,exam_id,null)) as complete_exam_cnt
from exam_record
SQL125 得分不小于平均分的最低分
select min(score) as min_score_over_avg
from exam_record
join examination_info using(exam_id)
where tag='SQL' and score>= (select avg(score)
from exam_record
join examination_info using(exam_id)
where tag='SQL')
分组查询
SQL126 平均活跃天数和月活人数
（在MySQL中， count(distinct A,B)是可以的， count(A,B) 报错 ； ）
select
date_format(start_time,'%Y%m') as month,
round(count(distinct uid,date_format(start_time,'%Y%m%d') )/count(distinct uid),2)as avg_active_days,
count(distinct uid) as mau
from exam_record
where year(start_time)=2021 and submit_time is not null
group by month
SQL127 月总刷题数和日均刷题数
（1.第三行需要加avg,因为按照月份进行分组之后day(LAST_DAY())是一组数，而不是一个数，所以需要用avg将他转换成一个具体的数；
2.last_day（）可以用来返回参数日期的最后一天 ；）
select DATE_FORMAT(submit_time,'%Y%m') submit_month,
count(question_id) month_q_cnt,
round(count(question_id)/avg(day(last_day(submit_time))),3) avg_day_q_cnt
from practice_record
where DATE_FORMAT(submit_time,'%Y')='2021'
group by submit_month
union ALL
SELECT '2021汇总' as submit_month,
count(question_id) month_q_cnt,
round(count(submit_time)/31,3) avg_day_q_cnt
from practice_record
where DATE_FORMAT(submit_time,'%Y')='2021'
order by submit_month
SQL128 未完成试卷数大于一的有效用户
（1. 对于每条作答tag，用 : 连接日期和tag：concat_ws(‘:’, date(start_time), tag) ；
2. group_concat中默认用 逗号 作分隔符，如有需要，可以用 seperator修改分隔符， ；
3. 对于一个人（组内）的多条作答，用 ; 连接去重后的作答记录：group_concat(distinct concat_ws(‘:’, date(start_time), tag) SEPARATOR ‘;’) ）
select uid,
sum(if(submit_time is null,1,0)) as incomplete_cnt,
sum(if(submit_time is null,0,1)) as complete_cnt,
group_concat(distinct concat_ws(':', date(start_time), tag) SEPARATOR ';') as detail
from examination_info
right join exam_record using(exam_id)
where year(start_time)=2021
group by uid
having (incomplete_cnt between 2 and 4) and complete_cnt>=1
order by incomplete_cnt desc
4.多表查询
嵌套子查询
SQL129 月均完成试卷数不小于3的用户爱作答的类别
select tag,count(start_time) as tag_cnt
from examination_info
join exam_record using(exam_id)
where uid in (
select uid
from exam_record
group by uid,month(submit_time)
having count(score)>=3
)
group by tag
order by tag_cnt desc
SQL130 试卷发布当天作答人数和平均分
select exam_id,
count(distinct uid) as uv,
round(avg(score),1) as avg_score
from exam_record
where uid in (
select uid
from user_info
where `level`>5 )
and
(exam_id, DATE(start_time)) IN (
SELECT exam_id, DATE(release_time)
FROM examination_info WHERE tag = "SQL")
group by exam_id
order by uv desc,avg_score
SQL131 作答试卷得分大于过80的人的用户等级分布
select level,
count(uid) as level_cnt
from user_info
where uid in (
select uid
from exam_record
where score>80 and exam_id in(
select exam_id
from examination_info
where tag='SQL'
)
)
group by level
order by level_cnt desc
合并查询
SQL132 每个题目和每份试卷被作答的人数和次数
（1.left（tid, 1）表示从tid列中取出从左数的第一个字符（即8001中的8,9001中的9），并进行降序排序 ； ）
select exam_id as tid,
count(distinct uid) as uv,
count(start_time) as pv
from exam_record
group by exam_id
union all
select question_id as tid,
count(distinct uid) as uv,
count(submit_time) as pv
from practice_record
group by question_id
order by LEFT(tid,1) desc,uv desc, pv desc
SQL133 分别满足两个活动的人
select uid,
'activity1' as activity
from exam_record
join examination_info using(exam_id)
where year(start_time)=2021 and score>=85
group by uid
union all
select uid,
'activity2' as activity
from exam_record
join examination_info using(exam_id)
where year(start_time)=2021
and difficulty='hard'
and score>80
and timestampdiff(second,start_time,submit_time)<= duration*30
order by uid
连接查询
SQL134 满足条件的用户的试卷完成数和题目练习数
（1.question_cnt这里用到if函数 if(question_cnt is null, 0, question_cnt) 是因为：题目中提到 “1003完成了2次试卷9001、9002，未练习题目（因此计数为0）” ，所以答案要null用0表示；
2.本题注重结构性思维，分块求出exam_cnt,question_cnt, 设置条件，并将这几块段落连接）
select
uid,
exam_cnt,
if(question_cnt is null, 0, question_cnt)
from
(select
uid,
count(submit_time) as exam_cnt
from exam_record
where YEAR(submit_time) = 2021
group by uid) t
left join
(select
uid,
count(submit_time) as question_cnt
from practice_record
where YEAR(submit_time) = 2021
group by uid) t2 using(uid)
where uid in
(
select
uid
from exam_record
join examination_info using(exam_id)
join user_info using(uid)
where tag = 'SQL' and difficulty = 'hard' and `level` = 7
group by uid
having avg(score) >= 80
)
order by exam_cnt asc, question_cnt desc
SQL135 每6/7级用户活跃情况
select u_i.uid as uid,
count(distinct act_month) as act_month_total,
count(distinct case
when year(act_time) = 2021
then act_day
end) as act_days_2021,
count(distinct case
when year(act_time) = 2021
and tag = 'exam'
then act_day
end) as act_days_2021_exam,
count(distinct case
when year(act_time) = 2021
and tag = 'question'
then act_day
end) as act_days_2021_question
from user_info u_i
left join (select uid,
start_time as act_time,
date_format(start_time, '%Y%m') as act_month,
date_format(start_time, '%Y%m%d') as act_day,
'exam' as tag
from exam_record
union all
select uid,
submit_time as act_time,
date_format(submit_time, '%Y%m') as act_month,
date_format(submit_time, '%Y%m%d') as act_day,
'question' as tag
from practice_record
) exam_and_practice
on exam_and_practice.uid = u_i.uid
where u_i.level >= 6
group by uid
order by act_month_total desc, act_days_2021 desc
5.窗口函数
专用窗口函数
SQL136 每类试卷得分前3名
（1. rank() over() 1 2 2 4 4 6 (计数排名，跳过相同的几个，没有3没有5；)
2. row_number() over() 1 2 3 4 5 6 (赋予唯一排名)；
3. dense_rank() over() 1 2 2 3 3 4 (不跳过排名，可以理解为对类别进行计数)； ）
select tag as tid,uid,ranking
from (select
tag,
uid,
dense_rank()over (partition by tag order by max(score) desc,min(score) desc,uid desc) as ranking
from examination_info
left join exam_record using(exam_id)
group by tag,uid) as a
where ranking<=3
SQL137 第二块/慢用时之差大于试卷时长一半的试卷
（1. nth_value（字段名，n）over（partition by…order by…）是窗口函数，表示允许从有序行集中的第N行获取值）
select exam_id,duration,release_time
from(
select exam_id,duration, release_time,
nth_value(timestampdiff(minute,start_time,submit_time),2)
over(partition by exam_id order by timestampdiff(minute,start_time,submit_time) desc) as max_time,
nth_value(timestampdiff(minute,start_time,submit_time),2)
over(partition by exam_id order by timestampdiff(minute,start_time,submit_time) asc) as min_time
from exam_record
join examination_info using(exam_id)
) as a
where max_time-min_time>duration*0.5
group by exam_id
order by exam_id desc
SQL138 连续两次作答试卷的最大时间窗
（lead的窗口函数用法：
- lead(字段名，n) over () :取值向后偏移n行（空间的理解就是直接将一列数据往前推n个位置，后面的位置就空出来了，具体配合图片理解）;
- lag(字段名，n) over () :取值向前偏移n行（空间的理解就是直接将一列数据往前后n个位置，前面的位置就空出来了，具体配合图片理解）;
- lag(字段名，n，x) over () :取值向前偏移n行，并将空值填充为数字x（空间的理解就是直接将一列数据往前后n个位置，前面的空出来的位置用X填充上，具体配合图片理解*）
时间函数： - datediff(时间1，时间2)：计算两个日期之间间隔的天数，单位为日
- timestampdiff(时间单位，开始时间，结束时间):两个日期的时间差，返回的时间差形式由时间单位决定(日，周，月，年)
- date_add(日期，INTERVAL n 时间单位) ：返回加上n个时间单位后的日期
- date_sub(日期，INTERVAL n 时间单位 )：返回减去n个时间单位后的日期
- date_format(时间，‘%Y-%m-%d’)：强制转换时间为所需要的格式 ）
- datediff 与 timestampdiff 的区别：
如9月5号12点，到9月6号的11点，中间隔了23个小时，timestampdiff会从秒分时天的角度来计算，返回的结果是0天，而datediff只会根据9月5号和9月6号的天的时间计算，返回的结果是1天。
SELECT uid, days_window, round(days_window*exam_cnt/diff_days, 2) as avg_exam_cnt
FROM (
SELECT uid,
count(start_time) as exam_cnt, -- 此人作答的总试卷数
DATEDIFF(max(start_time), min(start_time))+1 as diff_days, -- 最早一次作答和最晚一次作答的相差天数
max(DATEDIFF(next_start_time, start_time))+1 as days_window -- 两次作答的最大时间窗
FROM (
SELECT uid, exam_id, start_time,
lead(start_time) over(partition by uid ORDER BY start_time) as next_start_time -- 将连续的下次作答时间拼上
FROM exam_record
WHERE year(start_time)=2021
) as t_exam_record_lead
GROUP BY uid
) as t_exam_record_stat
WHERE diff_days>1
ORDER BY days_window DESC, avg_exam_cnt DESC
SQL139 近三个月未完成试卷数为0的用户完成情况
（1.为什么不能在子查询里用where score is not null筛选空的记录
在子查询中先where筛选会将用户的某条未完成记录去掉，而当月它有完成记录，也有未完成记录时，在外面的查询中会统一他认为它没有未完成记录。所以会多出结果（题目要求是‘近三个有试卷作答记录的月份中没有试卷是未完成状态的用户’）；）
select uid, count(start_time) as exam_complete_cnt
from(
select *,
dense_rank() over(partition by uid order by date_format(start_time,'%Y%m') desc) as rk
from exam_record
) as a
where rk<=3
group by uid
having count(score)=count(uid)
order by exam_complete_cnt desc, uid desc
SQL140 未完成率较高的50%用户近三个月答卷情况
（具体可参考：https://blog.nowcoder.net/n/29bdcc42caf1495a83bb91222977f029?f=comment）
select uid,
date_format(start_time,'%Y%m') as start_month,
count(start_time) as total_cnt,
count(submit_time) as complete_cnt
from (
select *,
dense_rank()over(partition by uid order by date_format(start_time,'%Y%m') desc) as rk1
from exam_record ) a
right join (
select *
from (
select uid,
percent_rank()over(order by count(submit_time)/count(start_time)) as rk2
FROM exam_record
where exam_id in (
select exam_id
from examination_info
where tag='SQL')
group by uid) b
where rk2<=0.5
and uid in (
select uid
from user_info
where `level` in (6,7))) c
using (uid)
where rk1<=3
group by uid,start_month
order by uid,start_month
SQL141 试卷完成数同比2020你那的增长率及排名变化
（1. SQL一般默认都是unsigned, 是不能出现负数的,一旦出现负数就可能报错，可用cast(字段 as signed)即可
2. rank1和rank2属于排序, 应该是不会出现负数的, 但是计算排名升降的时候,会有出现降排名, 也就是负数, 而数据库默认是unsigned的所以需要转化一下. 而growth_rate在用concat拼接之后已经是字符串格式了, 所以不需要转换）
SELECT #第三步：取出所有需要的字段，并计算排名变化（2021-2020年，无需取绝对值）
tag,
exam_cnt_20,
exam_cnt_21,
growth_rate,
exam_cnt_rank_20,
exam_cnt_rank_21,
CAST(exam_cnt_rank_21 as SIGNED) - CAST(exam_cnt_rank_20 AS SIGNED) rank_delta
FROM (
SELECT #第二步：计算growth_rate，并生成各tag完成数排名
tag,
exam_cnt_20,
exam_cnt_21,
CONCAT(ROUND((exam_cnt_21 - exam_cnt_20)* 100 / exam_cnt_20 , 1), '%') growth_rate,
RANK() OVER (ORDER BY exam_cnt_20 DESC) exam_cnt_rank_20,
RANK() OVER (ORDER BY exam_cnt_21 DESC) exam_cnt_rank_21
FROM (
SELECT #第一步：找到所有tag在2020，2021上半年的完成数
tag,
SUM(IF(DATE_FORMAT(submit_time, '%Y-%m') BETWEEN '2020-01' AND '2020-06', 1, 0)) exam_cnt_20,
SUM(IF(DATE_FORMAT(submit_time, '%Y-%m') BETWEEN '2021-01' AND '2021-06', 1, 0)) exam_cnt_21
FROM exam_record
LEFT JOIN examination_info ei USING(exam_id)
GROUP BY 1) t1
) t2
WHERE exam_cnt_20 != 0 AND exam_cnt_21 != 0 # 第四步：筛选出2020和2021年均有完成记录的tag，并按题目要求排序
ORDER BY 4 desc, 6 desc
方法二
select tag,
exam_cnt_20,
exam_cnt_21,
concat(round((exam_cnt_21-exam_cnt_20)*100/exam_cnt_20,1),'%') as growth_rate,
exam_cnt_rank_20,
exam_cnt_rank_21,
cast(exam_cnt_rank_21 as signed) - CAST(exam_cnt_rank_20 as signed) as rank_delta
from (
select
exam_id,
count(submit_time) as exam_cnt_20,
rank()over(order by count(submit_time) desc) exam_cnt_rank_20
from exam_record
where year(submit_time)=2020 and month(submit_time) <=6
group by exam_id ) as a
join
(select exam_id,
count(submit_time) as exam_cnt_21,
rank()over(order by count(submit_time) desc) exam_cnt_rank_21
from exam_record
where year(submit_time)=2021 and month(submit_time) <=6
group by exam_id ) as b
using(exam_id)
join examination_info using(exam_id)
order by growth_rate desc, exam_cnt_rank_21 desc
聚和窗口函数
SQL142 对试卷得分做min-max归一化
select uid,exam_id,
round(avg(max_min),0) as avg_new_score
from (
select uid,exam_id,
if(min_score=max_score,score,(score-min_score)*100/(max_score-min_score)) as max_min
from (
select uid,exam_id,score,
min(score)over(partition by exam_id) as min_score,
max(score)over(partition by exam_id) as max_score
from exam_record
join examination_info using(exam_id)
where difficulty='hard' and score is not null
) a
) b
group by exam_id,uid
order by exam_id, avg_new_score desc
SQL143 每份试卷每月作答数和截止当月的作答总数
（聚合窗口函数中，over()的括号中有order by 时，即为计算到当前时间为止的累计数量）
select
exam_id,
date_format(start_time,'%Y%m') as start_month,
count(start_time) as month_cnt,
sum(count(start_time))over(partition by exam_id order by date_format(start_time,'%Y%m')) as cum_exam_cnt
from exam_record
group by exam_id,start_month
SQL144 每月及截止当月的答题情况
（第12行if语句是用来确定是否为新用户，如果这个用户做题时间是最小做题时间，那么这个用户就是新用户。另外，如果不用partition by的话，只能找到那一位最早做题的用户，不能找到新用户。）
select
start_month,
count(distinct uid) as mau,
sum(new_user) as month_add_uv,
max(sum(new_user))over(order by start_month) as max_month_add_uv,
sum(sum(new_user))over(order by start_month) as cum_sum_uv
from (
select *,
date_format(start_time,'%Y%m') as start_month,
if(start_time=min(start_time)over(partition by uid),1,0) as new_user
from exam_record
) a
group by start_month
order by start_month
6.其他常用操作
空值处理
SQL145 统计有未完成状态的试卷的未完成数和未完成率
（题目要求要有未完成状态的试卷，所以要在最后加上having，否则会输出9002这些没有未完成状态的试卷）
select
exam_id,
sum(if (score is null,1,0)) as incomplete_cnt,
round(sum(if (score is null,1,0))/count(start_time),3) as complete_rate
from exam_record
group by exam_id
having incomplete_cnt <>0
SQL146 0级用户高难度试卷的平均用时和平均得分
select
uid,
round(avg(if(score is null,0,score)),0) as avg_score,
round(avg(if(submit_time is null,duration,timestampdiff(minute,start_time,submit_time))),1) as avg_time_took
from exam_record
join user_info using(uid)
join examination_info using(exam_id)
where `level`=0 and difficulty='hard'
group by uid
高级条件语句
SQL147 筛选限定昵称成就值活跃日期的用户
select
uid,
nick_name,
achievement
from user_info
where nick_name like '牛客%号'
and achievement between 1200 and 2500
and uid in (
select uid
from (
select uid,submit_time
from exam_record
union all
select uid,submit_time
from practice_record
) a
group by uid
having date_format(max(submit_time),'%Y%m')=202109
)
SQL148 筛选昵称规则和试卷规则的作答记录
select
uid,
exam_id,
round(avg(score) ,0) as avg_score
from exam_record
where uid in (
select uid
from user_info
where nick_name regexp "^牛客[0-9]+号$|^[0-9]+$"and submit_time is not null )
and exam_id in (
select exam_id
from examination_info
where tag regexp"^[Cc]" and submit_time is not null )
group by uid,exam_id
order by uid,avg_score
方法二
select
select uid,exam_id,round(sum(score)/count(exam_id),0) as avg_score
from exam_record
left join user_info using(uid)
left join examination_info using(exam_id)
where nick_name regexp '^牛客[0-9]+号$|^[0-9]+$'
and tag regexp '^[Cc]'
and submit_time is not NULL
group by uid,exam_id
order by 1,3
SQL149 根据指定记录是否存在输出不同的情况
（1. 题目中提到当有任意一个0级用户未完成试卷数大于2时，输出每个0级用户的试卷未完成数和未完成率，所以第一个where exists 用来判断是否存在有0级用户未完成试卷数大于2，如果不存在，输出false，如果存在，后面还要加一个 level=0过滤得到所有0级用户
2.题目中提到输出所有0级用户的信息，而不是所有有作答记录的0级用户的信息，所以a表中要用到right join，如果不用right join，就会只显示三个有答题记录的用户的信息，其他没有答题记录的0级用户就不显示）
with a as(
select uid,level,
count(start_time)-count(submit_time) as incomplete_cnt,
round(ifnull(1-count(submit_time)/count(start_time),0),3) as incomplete_rate,
count(start_time) as total_cnt
from exam_record
right join user_info using(uid)
group by uid)
select uid,incomplete_cnt,incomplete_rate
from a
where exists ( select incomplete_cnt,level from a where level=0 and incomplete_cnt>2)
and level=0
union all
select uid,incomplete_cnt,incomplete_rate
from a
where not exists ( select incomplete_cnt,level from a where level=0 and incomplete_cnt>2 )
and total_cnt>0
order by incomplete_rate
SQL150 各用户等级的不同得分表现占比
（* 1. count()在统计结果的时候，不会忽略列值为NULL；
count(列名)只包括列名那一列，在统计结果的时候，会忽略列值为空。
2. 本题中，如果用count（列名）则会报错only full group by，可能是因为在第二行用到了count这一含有partition by level的字段，与后面的group by level 相冲突，使这一行不依赖于后面group by的分组，不符合SQL中的语句规则。此时使用count（），不指代某一列，则可以解决这个问题）
select level,score_grade,
round(count(uid)/level_cnt,3) as ratio
from (
select level,score,uid,exam_id,
case when score>=90 then'优'
when score>=75 then '良'
when score>=60 then '中'
else'差' end as score_grade,
count(*)over(partition by level) as level_cnt
from exam_record
join user_info using(uid)
where score is not null
) as a
group by level,score_grade
order by level desc,ratio desc
限量查询
SQL151 注册时间最早的三个人
select uid,nick_name,register_time
from user_info
order by register_time
limit 3
SQL152 注册当天就完成了试卷的名单第三页
（limit x,y 表示从第x行开始取，取y行。在实际操作中，第一行是0，而不是1。举个例子：limit 1,1 表示的是从第二行开始取，取一行；limit 3 表示 取前三行）
select uid,
level,
register_time,
max(score) as max_score
from exam_record
join examination_info using(exam_id)
join user_info using(uid)
where job='算法' and tag='算法'
and timestampdiff(day,register_time,submit_time)=0
group by uid,exam_id
order by max_score desc
limit 6,3
文本转换函数
SQL153 修复串列了的记录
（1. substring_index（字符串, ‘分隔符’ , n），其中n表示搜索分隔符的次数，若为正，返回分隔符左侧的数字，若为负，返回分隔符右侧的数字）
select
exam_id,
substring_index(tag,',',1) as tag,
substring_index((substring_index(tag,',',2)),',',-1) as difficulty,
substring_index(tag,',',-1) as duratiom
from examination_info
where difficulty=''
SQL154 对过长的昵称截取处理
（ 1. char_length（）可以返回字符串的长度
2. substring（字符串，起始位置，提取字符数）length若为正，从开头提取，若为负，从末尾提取 ）
select
uid,
if( char_length(nick_name)>13, concat(substring(nick_name,1,10),'...') , nick_name)
from user_info
where char_length(nick_name)>10
SQL155 大小写混乱时的筛选统计
（题目：试卷的类别tag可能出现大小写混乱的情况，请先筛选出试卷作答数小于3的类别tag，统计将其转换为大写后对应的原本试卷作答数。
如果转换后tag并没有发生变化，不输出该条结果。
1.筛选出试卷作答数小于3的类别tag：a.answer_cnt<3 ；
2.统计将其转换为大写后对应的原本试卷作答数：upper(a.tag)=b.tag；
3.如果转换后tag并没有发生变化，不输出该条结果：a.tag != b.tag；
4.本题难点在于两个表的连接。
最后一行的 on upper(a.tag)=b.tag ， 使b表变成了只有tag为大写的表，因此，b表中的tag全是大写的，b表中的amswer_cnt也是只有大写tag对应的数量 ；
如果把第一行select后的a.tag换成b.tag 则会输出大写的tag 而不是小写的tag；
如果把第一行select后的b.answer_cnt换成a.answer_cnt 则会输出小写tag对应的数量，而不是大写的tag对应的数量；
为了便于理解，可参照下图思考：
）
select a.tag, b.answer_cnt
from (
select tag,count(start_time) as answer_cnt
from exam_record
join examination_info using(exam_id)
group by tag ) as a
join(
select tag,count(start_time) as answer_cnt
from exam_record
join examination_info using(exam_id)
group by tag ) as b
on upper(a.tag)=b.tag and a.answer_cnt<3 and a.tag != b.tag