练习题

-------------------------------以下使用Structured Streaming：-------------------------------
1、请使用Structured Streaming读取Socket数据，统计出每个单词的个数
2、请使用Structured Streaming读取student_info文件夹写的csv文件，
2.1、统计出文件中的男女生各有多少人
2.2、统计出姓“王”女生的人数
2.3、统计出姓“王”男生和女生的比例
3、请使用Structured Streaming读取department_info文件夹写的csv文件
3.1统计出各个院系的分别多少条信息
4、请使用Structured Streaming读取student_score文件夹写的csv文件
4.1、统计出每个班级的最高分数
4.2、统计出男生最高分
4.3、统计出女生最高分
4.4、分别统计出男生和女生的分数前三名
4.5、统计出分数在500分以上的人数
4.7、统计出分数在300分以下的人中男女各占多少

import org.apache.spark.sql.{DataFrame, SparkSession}/*** 使用Structured Streaming读取Socket数据，把单词和单词的反转组成 json 格式写入到当前目录中的file文件夹中* （abc,cba）*/
object demo01 {def main(args: Array[String]): Unit = {val spark: SparkSession = SparkSession.builder().master("local[*]").appName("demo01").getOrCreate()spark.sparkContext.setLogLevel("WARN")import spark.implicits._//获取每行的数据val lines: DataFrame = spark.readStream.format("socket") // 设置数据源.option("host", "cdh01").option("port", 10000).load//val words: DataFrame = lines.as[String].flatMap(line => {line.split("\\W+").map(word => {// \\W+  空格(word, word.reverse)})}).toDF("原单词", "反转单词")words.writeStream.outputMode("append").format("json") //  // 支持 "orc", "json", "csv".option("path", "./file") // 输出目录.option("checkpointLocation", "./ck1")  // 必须指定 checkpoint 目录.start.awaitTermination()}
}

import org.apache.spark.SparkContext
import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql.types.{LongType, StringType, StructType}
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
/**
2、请使用Structured Streaming读取student_info文件夹写的csv文件，
2.1、统计出文件中的男女生各有多少人
2.2、统计出姓“王”女生的人数
2.3、统计出姓“王”男生和女生的比例*/
object demo02 {def main(args: Array[String]): Unit = {val spark: SparkSession = SparkSession.builder().master("local[*]").appName("ReadFromCSV").getOrCreate()spark.sparkContext.setLogLevel("WARN")val student_info_schema: StructType = new StructType().add("学号","string").add("姓名","string").add("性别","string").add("班级编号","integer").add("入学日期","String")//2.接收数据import spark.implicits._val student_info: DataFrame = spark.readStream.format("csv").option("header","true")//说明csv文件有标题.schema(student_info_schema).load("C:\\Users\\hasee\\Desktop\\4.16\\student_info")//2.1、统计出文件中的男女生各有多少人val result1: Dataset[Row] =student_info.groupBy("性别").count()//2.2、统计出姓“王”男生和女生的各有多少人val result2: Dataset[Row] =student_info.filter($"姓名" .startsWith("王") ).groupBy("性别").count()//输出结果result2.writeStream.format("console").outputMode("complete").trigger(Trigger.ProcessingTime(0))// 触发器 数字表示毫秒值. 0 表示立即处理.start().awaitTermination()}
}

import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
import org.apache.spark.sql.types.StructType
/**
3、请使用Structured Streaming读取department_info文件夹写的csv文件
3.1统计出各个院系的分别多少条信息
*/
object demo03 {def main(args: Array[String]): Unit = {val spark: SparkSession = SparkSession.builder().master("local[*]").appName("ReadFromCSV").getOrCreate()spark.sparkContext.setLogLevel("WARN")//读取数据//设置数据的结构val department_info_schema: StructType = new StructType().add("院系编号", "string").add("院系名称", "string")val department_info: DataFrame = spark.readStream.format("csv").option("header","true").schema(department_info_schema).load("C:\\Users\\hasee\\Desktop\\4.16\\department_info\\")import spark.implicits._//3.1统计出各个院系的分别多少条信息val result: Dataset[Row] = department_info.groupBy("院系名称").count()result.writeStream.format("console").outputMode("complete").trigger(Trigger.ProcessingTime(0))// 触发器 数字表示毫秒值. 0 表示立即处理.start().awaitTermination()}
}

import org.apache.spark.sql._
import org.apache.spark.sql.types.StructType
import org.apache.spark.{SparkConf, SparkContext}
/**
请使用Structured Streaming读取student_score文件夹写的csv文件
**/
object demo04 {//准备临时存储数据的样例类def main(args: Array[String]): Unit = {//1  创建sparksessionval spark: SparkSession = SparkSession.builder().appName("demo04").master("local[*]").getOrCreate()spark.sparkContext.setLogLevel("WARN")val student_scores_schema: StructType = new StructType().add("sno", "string").add("sname", "string").add("sex", "string").add("cno", "string").add("scores", "string")val frame: DataFrame = spark.read.option("header", true).format("csv").schema(student_scores_schema).load("C:\\Users\\hasee\\Desktop\\4.16\\student_score\\")frame.createOrReplaceTempView("student_scores")//4.1、统计出每个班级的最高分数
//    spark.sql("select cno ,max(scores) from student_scores group by cno ").show()//4.2、统计出男生最高分
//  spark.sql("select max(scores) from student_scores where sex='男' ").show()//4.3、统计出女生最高分
//      spark.sql("select max(scores) from student_scores where sex='女' ").show()//4.4、分别统计出男生和女生的分数前三名//SELECT *, Row_Number() OVER (partition by deptid ORDER BY salary desc) rank FROM employee
//    spark.sql("select * from (SELECT *, Row_Number() OVER (partition by sex ORDER BY scores desc) rank FROM student_scores) t1 where t1.rank<=3").show()//4.5、统计出分数在500分以上的人数
//     spark.sql("select count(scores)  from student_scores  where scores>500").show()//4.6、统计出分数在300分以下的人中男女各占多少
//    spark.sql("select sex, count(*)  from student_scores  where scores<300  group by sex ").showspark.stop()}
}

-------------------------------以下使用sparksql：-------------------------------------------
5.请使用Structured Streaming读取class_info文件夹写的csv文件
5.1、统计出哪个院系的专业最多
5.2、统计出计算机学院中有多少专业
5.3、统计出经济管理学院的会计和工商管理的班级数
5.4、分别统计出每个学院的班级最多的专业
5.5、统计出班级编号以2开头的所有的专业名称

import org.apache.spark.SparkContext
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.{DataFrame, SparkSession}object demo05 {def main(args: Array[String]): Unit = {//1  创建sparksessionval spark: SparkSession = SparkSession.builder().appName("demo05").master("local[*]").getOrCreate()spark.sparkContext.setLogLevel("WARN")val class_info_schema: StructType = new StructType().add("classid", "string").add("classname", "string").add("date", "string").add("yname", "string")val frame: DataFrame = spark.read.option("header", true).format("csv").schema(class_info_schema).load("C:\\Users\\hasee\\Desktop\\4.20\\4.16号练习题50道2.0\\class_info\\")frame.createOrReplaceTempView("class_info")spark.udf.register("getMajorCname", (str: String) => {str.substring(0, str.length - 2)})//    spark.sql("select getMajorCname(classname) from class_info ").show(100)//5.1、统计出哪个院系的专业最多
//    spark.sql("select yname,count(distinct(t1.cname)) as count from  (select classid,getMajorCname(classname) as cname,date,yname  from class_info) t1 group by t1.yname").show(100)
//    spark.sql("select * from (select yname,count(distinct(t1.cname)) as count from  (select classid,getMajorCname(classname) as cname,date,yname  from class_info) t1  group by t1.yname) t2 order by count desc limit 1").show()//    spark.sql(" select * from (select yname,count(distinct(t1.cname)) as count from  (select classid,getMajorCname(classname) as cname,date,yname  from class_info) t1 group by t1.yname) t2 order by count desc limit 1 ").show()//5.2、统计出计算机学院中有多少专业
//          spark.sql("select * from (select yname,count(distinct(t1.cname)) as count from  (select classid,getMajorCname(classname) as cname,date,yname  from class_info) t1 group by t1.yname ) t2 where yname='计算机学院' ").show()//5.3、统计出经济管理学院的会计和工商管理的班级数//          spark.sql("select  '会计',count(classname)  from class_info where  yname='经济管理学院' and classname like '会计%' ").show()
//          spark.sql("select  '工商管理',count(classname)  from class_info where  yname='经济管理学院' and classname like '工商管理%' ").show()//5.4、分别统计出每个学院的班级最多的专业//          spark.sql("select yname,count(classid) as count ,cname from (select classid,getMajorCname(classname) as cname,date,yname  from class_info) t1 group by yname,t1.cname ").show()spark.sql("select * from (select t2.*,rank() over( partition by t2.yname order by t2.count desc ) as rank  from (select yname,count(classid) as count ,cname from (select classid,getMajorCname(classname) as cname,date,yname  from class_info) t1 group by yname,t1.cname ) t2) t3 where t3.rank <= 1").show(100)//5.5、统计出班级编号以2开头的所有的专业名称
//    spark.sql("select   getMajorCname(classname)  as zhuanye from class_info where   classid like '02%' group by zhuanye").show()spark.stop()}
}

表（一）Student (学生表)
属性名数据类型可否为空含义
Sno varchar (20) 否学号
Sname varchar (20) 否学生姓名
Ssex varchar (20) 否学生性别
Sbirthday datetime 可学生出生年月
Class varchar (20) 可学生所在班级

表（二）Course（课程表）
属性名数据类型可否为空含义
Cno varchar (20) 否课程号
Cname varchar (20) 否课程名称
Tno varchar (20) 否教工编号

表（三）Score(成绩表)
属性名数据类型可否为空含义
Sno varchar (20) 否学号
Cno varchar (20) 否课程号
Degree Decimal(4,1) 可成绩

表（四）Teacher(教师表)
属性名数据类型可否为空含义
Tno varchar (20) 否教工编号
Tname varchar (20) 否教工姓名
Tsex varchar (20) 否教工性别
Tbirthday datetime 可教工出生年月
Prof varchar (20) 可职称
Depart varchar (20) 否教工所在部门

表1-2数据库中的数据

表（一）Student

Sno  Sname   Ssex    Sbirthday   class
108 丘东  男   1977-09-01  95033
105 匡明  男   1975-10-02  95031
107 王丽  女   1976-01-23  95033
101 李军  男   1976-02-20  95033
109 王芳  女   1975-02-10  95031
103 陆君  男   1974-06-03  95031

表（二）Course

Cno  Cname   Tno
3-105   计算机导论   825
3-245   操作系统    804
6-166   数字电路    856
9-888   高等数学    831

表（三）Score

Sno  Cno Degree
103 3-245   86
105 3-245   75
109 3-245   68
103 3-105   92
105 3-105   88
109 3-105   76
101 3-105   64
107 3-105   91
108 3-105   78
101 6-166   85
107 6-166   79
108 6-166   81

表（四）Teacher

Tno  Tname   Tsex    Tbirthday   Prof    Depart
804 李诚  男   1958-12-02  副教授 计算机系
856 张旭  男   1969-03-12  讲师  电子工程系
825 王萍  女   1972-05-05  助教  计算机系
831 刘冰  女   1977-08-14  助教  电子工程系

sparksql的练习题import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{Dataset, SparkSession}
object Test02 {// 定义样例类封装数据case class Student(Sno:String,Sname:String,Ssex:String,Sbirthday:String,Class:String)case class Course(Cno:String,Cname:String,Tno:String)case class Score(Sno:String,Cno:String,Degree:String)case class Teacher(Tno:String,Tname:String,Tsex:String,Tbirthday:String,Prof:String,Depart:String)def main(args: Array[String]): Unit = {// 1. 创建SparkSessionval spark: SparkSession = SparkSession.builder().master("local[*]").appName("test02").getOrCreate()val sc: SparkContext = spark.sparkContext// 设置日志级别sc.setLogLevel("WARN")// 读取test文件val rdd1: RDD[String] = sc.textFile("F:\\网课\\Spark\\spark练习\\4.16号练习题50道2.0\\student.txt")val rdd2: RDD[String] = sc.textFile("F:\\网课\\Spark\\spark练习\\4.16号练习题50道2.0\\course.txt")val rdd3: RDD[String] = sc.textFile("F:\\网课\\Spark\\spark练习\\4.16号练习题50道2.0\\score.txt")val rdd4: RDD[String] = sc.textFile("F:\\网课\\Spark\\spark练习\\4.16号练习题50道2.0\\teacher.txt")val student: RDD[Student] = rdd1.map(x=>{val str = x.split("\t");Student(str(0),str(1),str(2),str(3),str(4))})val course: RDD[Course] = rdd2.map(x=>{val str = x.split("\t");Course(str(0),str(1),str(2))})val score: RDD[Score] = rdd3.map(x=>{val str = x.split("\t");Score(str(0),str(1),str(2))})val teacher: RDD[Teacher] = rdd4.map(x=>{val str = x.split("\t");Teacher(str(0),str(1),str(2),str(3),str(4),str(5))})// 导入隐式转换import spark.implicits._// 将RDD转换成DFval stuframe = student.toDF()val couframe = course.toDF()val scoframe = score.toDF()val teaframe = teacher.toDF()// 创建临时表stuframe.createOrReplaceTempView("student")couframe.createOrReplaceTempView("course")scoframe.createOrReplaceTempView("score")teaframe.createOrReplaceTempView("teacher")/* 6. 查询Student表中“95031”班或性别为“女”的同学记录*/spark.sql("Select * from student where sno = '95031' or ssex = '女'").show()/* 7. 以Class降序,升序查询Student表的所有记录 */spark.sql("Select * from student order by class desc").show()/* 8. 以Cno升序、Degree降序查询Score表的所有记录。 */spark.sql("Select * from score order by cno,degree desc").show()/* 9. 查询“95031”班的学生。 */spark.sql("Select sname from student where class = '95031'").show()/* 10. 查询Score表中的最高分的学生学号和课程号。（子查询或者排序） */spark.sql("Select sno,cno from score order by degree desc limit 1").show()/* 11. 查询每门课的平均成绩。  */spark.sql("Select first(course.Cname),avg(degree) from score,course where score.Cno = course.Cno  group by score.Cno").show()/* 12. 查询Score表中至少有5名学生选修的并以3开头的课程的平均分数。  */spark.sql("Select first(cno),avg(Degree) from score where cno like '3%' group by cno having count(cno) >= 5").show()/* 13.查询分数大于70，小于90的Sno列  */spark.sql("Select sno from score where Degree > 70 and Degree < 90 ").show()/* 14.查询所有学生的Sname、Cno和Degree列。 */spark.sql("select sname,cno,degree from score,student where score.sno = student.sno").show()/* 15.查询所有学生的Sno、Cname和Degree列。 */spark.sql("select sno,cname,degree from score,course where score.cno=course.cno").show()/* 16.查询所有学生的Sname、Cname和Degree列。 */spark.sql("""|select sname,cname,degree from score,course,student|where score.cno = course.cno and student.sno = score.sno""".stripMargin).show()/* 17.查询“95033”班学生的平均分。*/spark.sql("select first(class),avg(Degree) from student,score where student.sno = score.sno and class = '95033'").show()/* 18.查询所有选修“计算机导论”课程的“女”同学的成绩表。 */spark.sql("""|select sname,cname,ssex,Degree from student,course,score|where student.sno = score.sno and course.cno=score.cno|and ssex = "女" and cname = "计算机导论"""".stripMargin).show()/* 19.查询选修“3-105”课程的成绩高于“109”号同学成绩的所有同学的记录。*/spark.sql("""|select * from score where cno = "3-105" and degree > (select degree from score where sno = "109" and cno = "3-105" )|""".stripMargin).show()/* 20.查询score中选学多门课程的同学中分数为非最高分成绩的记录。  */spark.sql("select * from score a where sno in (select sno from score group by sno having count(*)>1) and degree<(select max(degree) from score b where a.cno=b.cno )").show()/* 21.查询成绩高于学号为“109”、课程号为“3-105”的成绩的所有记录。 */spark.sql("select * from score where degree > (select degree from score where sno = '109' and cno = '3-105')").show()/* 22. 查询和学号为105的同学同年出生的所有学生的Sno、Sname和Sbirthday列。*/spark.udf.register("sub",(str:String,num1:Int,num2:Int)=>str.substring(num1,num2))spark.sql("select sno,sname,sbirthday from student where sub(Sbirthday,0,5) = (select sub(Sbirthday,0,5) from student where sno = '105') ").show()/* 23. 查询“张旭“教师任课的学生成绩 */spark.sql("""|select  sno,degree from score,course,teacher|where tname = "张旭" and teacher.tno = course.tno|and course.Cno = score.Cno""".stripMargin).show()/* 24. 查询选修某课程的同学人数多于4人的教师姓名。 */spark.sql("""|select first(tname) from teacher,score,course|where teacher.tno = course.tno|and course.Cno = score.Cno|group by score.Cno having count(score.Cno) > 4""".stripMargin).show()/* 25. 查询95033班和95031班全体学生的记录。 */spark.sql("select * from student where class= '95033' or class = '95031'").show()/* 26. 查询存在有85分以上成绩的课程Cno.  */spark.sql("select distinct cno from score where degree >= 85").show()/* 27. 查询出“计算机系“教师所教课程的成绩表。 */spark.sql("""|select  score.*  from score,Course,teacher|where  teacher.tno = course.tno|and course.Cno = score.Cno|and teacher.Depart = "计算机系"""".stripMargin).show()/* 28. 查询“计算机系”与“电子工程系“不同职称的教师的Tname和Prof */spark.sql("""|select tname,prof from teacher where depart = '计算机系' and prof not in|(select prof from teacher where depart = '电子工程系')""".stripMargin).show()/* 29. 查询选修编号为“3-105“课程且成绩至少高于选修编号为“3-245”的同学的Cno、Sno和Degree,并按Degree从高到低次序排序。 */spark.sql("""|select s1.cno,s1.sno,s1.degree from score s1,score s2|where s1.cno = "3-105" and s2.cno = "3-245"|and s1.Sno = s2.Sno|and s1.degree > s2. degree|order by s1.degree desc""".stripMargin).show()/* 30. 查询选修编号为“3-105”且成绩高于选修编号为“3-245”课程的同学的Cno、Sno和Degree. */spark.sql("""|select cno,sno,degree from score|where cno = "3-105" and degree > (select max(degree) from score where cno = "3-245")""".stripMargin).show()/* 31. 查询所有教师和同学的name、sex和birthday. */spark.sql("""|select distinct Sname as name,Ssex as sex,Sbirthday as birthday from student|union|select distinct Tname as name,Tsex as sex,Tbirthday as birthday from Teacher|""".stripMargin).show()/* 32. 查询所有“女”教师和“女”同学的name、sex和birthday. */spark.sql("""|select distinct Sname as name,Ssex as sex,Sbirthday as birthday from student where Ssex = "女"|union|select distinct Tname as name,Tsex as sex,Tbirthday as birthday from Teacher where Tsex = "女"|""".stripMargin).show()/* 33. 查询成绩比该课程平均成绩低的同学的成绩表。 */spark.sql("""|select * from score s1|where degree < (select avg(degree) from score s2 where s1.Cno = s2.Cno)""".stripMargin).show()/* 34. 查询所有任课教师的Tname和Depart. */spark.sql("""|select tname,depart from teacher|where tno in (select tno from course where cno in (select distinct cno from score) )""".stripMargin).show()/* 35. 查询所有未讲课的教师的Tname和Depart.  */spark.sql("""|select tname,depart from teacher|where tno not in (select tno from course where cno in (select distinct cno from score) )""".stripMargin).show()/* 36. 查询至少有2名男生的班号。 */spark.sql("""|select class from student|where ssex = "男"|group by class having count(class) >= 2""".stripMargin).show()/* 37. 查询Student表中不姓“王”的同学记录。 */spark.sql("""|select * from student|where sname not like "王%"""".stripMargin).show()/* 38. 查询Student表中每个学生的姓名和年龄。将函数运用到spark sql中去计算，可以直接拿String的类型计算不需要再转换成数值型 默认是会转换成Double类型计算浮点型转整型 */spark.sql("""|select Sname,year(current_date)-year(Sbirthday) from student""".stripMargin).show()/* 39. 查询Student表中最大和最小的Sbirthday日期值。 时间格式最大值,最小值*/spark.sql("""|select max(Sbirthday),min(Sbirthday) from student""".stripMargin).show()/* 40. 以班号和年龄从大到小的顺序查询Student表中的全部记录。 查询结果排序*/spark.sql("""|select * from student order by class desc,year(current_date)-year(Sbirthday) desc""".stripMargin).show()/* 41. 查询“男”教师及其所上的课程。 */spark.sql("""|select cname from course,teacher|where teacher.Tno = course.Tno and Tsex = "男"""".stripMargin).show()/* 42. 查询最高分同学的Sno、Cno和Degree列。  */spark.sql("""|select * from score|where degree = (select max(degree) from score)""".stripMargin).show()/* 43. 查询和“李军”同性别的所有同学的Sname.*/spark.sql("""|select sname from student|where ssex = (select ssex from student where sname = "李军") and sname != "李军"""".stripMargin).show()/* 44. 查询和“李军”同性别并同班的同学Sname. */spark.sql("""|select distinct s1.sname from student s1,student s2|where s1.ssex = s2.ssex and s1.class = s2.class|and s1.sname != "李军"""".stripMargin).show()/* 45. 查询所有选修“计算机导论”课程的“男”同学的成绩表。 */spark.sql("""|select degree from score,student,course|where score.cno = course.cno|and score.sno = student.sno|and student.ssex = "男"|and course.cname = "计算机导论"""".stripMargin).show()/* 46. 查询Student表中的所有记录的Sname、Ssex和Class列。 */spark.sql("""|select sname,ssex,class from student""".stripMargin).show()/* 47. 查询教师所有的单位即不重复的Depart列。*/spark.sql("""|select distinct depart from teacher""".stripMargin).show()/* 48. 查询Student表的所有记录 */spark.sql("""|select * from student""".stripMargin).show()/* 49. 查询Score表中成绩在60到80之间的所有记录。 */spark.sql("""|select * from score where degree > 60 and degree < 80""".stripMargin).show()/* 50. 查询Score表中成绩为85，86或88的记录。 */spark.sql("""|select * from score where Degree in (85,86,88)""".stripMargin).show()}
}

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