08 学生课程分数的Spark SQL分析(sql语言)
1.生成“表头”
from pyspark.sql.types import IntegerType,StringType,StructField,StructType
from pyspark.sql import Row
fields=[StructField('name',StringType(),True),StructField('course',StringType(),True),StructField('score',IntegerType(),True)]
schema=StructType(fields)
schema
2.生成“表中的记录”
chapter=sc.textFile('file:///home/hadoop/chapter4-data01.txt').map(lambda x:x.split(','))
chapter.take(3)
data=chapter.map(lambda p:Row(p[0],p[1],int(p[2])))
3.把“表头”和“表中的记录”拼装在一起
df_scs=spark.createDataFrame(data,schema)
df_scs.printSchema()
二、用SQL语句完成以下数据分析要求
必须把DataFrame注册为临时表
df_scs.createOrReplaceTempView('scs')
- 每个分数+5分。
- spark.sql("SELECT name,course,score+5 from scs").show()
- 总共有多少学生？
- spark.sql("SELECT count(name) from scs").show()
- 总共开设了哪些课程？
- spark.sql("SELECT distinct(course) from scs").show()
- 每个学生选修了多少门课？
- spark.sql("SELECT name,count(course) from scs group by name").show()
- 每门课程有多少个学生选？
- spark.sql("SELECT count(name),course from scs group by course").show()
- 每门课程大于95分的学生人数？
- spark.sql("SELECT count(name),course from scs where score>95 group by course").show()
- Tom选修了几门课？每门课多少分
- spark.sql("SELECT count(course) from scs where name='Tom'").show()
- spark.sql("SELECT course,score from scs where name='Tom'").show()
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- Tom的成绩按分数大小排序。
- (从大到小 desc 从小到大 asc)
- spark.sql("SELECT course,score from scs where name='Tom' order by score desc").show()
- Tom的平均分。
- spark.sql("SELECT avg(score) from scs where name='Tom'").show()
- 求每门课的平均分，最高分，最低分。
- spark.sql("SELECT course,avg(score),max(score),min(score) from scs group by course").show()
- 求每门课的选修人数及平均分，精确到2位小数。
-
spark.sql("SELECT course,count(course),round(avg(score),2) from scs group by course").show()
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- 每门课的不及格人数，通过率（未成功）
-
spark.sql("SELECT a.nopass,(b.total-a.nopass)/b.total from (SELECT course,distinct(count(*))as nopass from scs where score<60 group by course)as a left join (SELECT course,distinct(count(course))as total from scs group by course)as b on a.course=b.course").show()
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spark.sql("SELECT course, count(name) as n, avg(score) as avg FROM scs group by course").createOrReplaceTempView("a")
spark.sql("SELECT course, count(score) as notPass FROM scs WHERE score<60 group by course").createOrReplaceTempView("b")
spark.sql("SELECT * FROM a left join b on a.course=b.course").show()
spark.sql("SELECT a.course, round(a.avg, 2), b.notPass, round((a.n-b.notPass)/a.n, 2) as passRat FROM a left join b on a.course=b.course").show()
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三、对比分别用RDD操作实现、用DataFrame操作实现和用SQL语句实现的异同。(比较两个以上问题)
准备工作的不同：
RDD:chapters = sc.textFile('file:///home/hadoop/chapter4-data01.txt')
DataFrame:
1.生成“表头”
from pyspark.sql.types import IntegerType,StringType,StructField,StructType
from pyspark.sql import Row
fields=[StructField('name',StringType(),True),StructField('course',StringType(),True),StructField('score',IntegerType(),True)]
schema=StructType(fields)
2.生成“表中的记录”
chapter=sc.textFile('file:///home/hadoop/chapter4-data01.txt').map(lambda x:x.split(','))
data=chapter.map(lambda p:Row(p[0],p[1],int(p[2])))
3.把“表头”和“表中的记录”拼装在一起
df_scs=spark.createDataFrame(data,schema)
SQL:
在DataFrame基础上添加：df_scs.createOrReplaceTempView('scs')
求每门课的选修人数及平均分，精确到2位小数:
RDD:
chapter = chapters.map(lambda x:x.split(',')).map(lambda x:(x[1],x[2]))
chapter = chapter.map(lambda x:(x[0],int(x[1])))
course = chapter.combineByKey(lambda v:(v,1),lambda c,v:(c[0]+v,c[1]+1),lambda c1,c2:(c1[0]+c2[0],c1[1]+c2[1]))
course_rev = course.map(lambda x:(x[0],x[1][1],round(x[1][0]/x[1][1])))
course_rev.take(4)
DataFrame:
df_scs.select(countDistinct('name').alias('学生人数'),countDistinct('cource').alias('课程数'),round(mean('score'),2).alias('所有课的平均分').alias('所有课的平均分')).show()
SQL:
spark.sql("SELECT course,count(course),round(avg(score),2) from scs group by course").show()
Tom的成绩按分数大小排序:
RDD:
chapters.filter(lambda chapter:"Roy" in chapter).map(lambda chapter:chapter.split(',')).sortBy(lambda chapter:(chapter[2])).collect()
DataFrame:
df_scs.filter(df_scs['name']=='Tom').sort(df_scs['score'].desc()).show()
SQL:
spark.sql("SELECT course,score from scs where name='Tom' order by score desc").show()
