实验5 Spark SQL编程初级实践

1.Spark SQL基本操作

将下列 json 数据复制到你的 ubuntu 系统/usr/local/spark 下，并保存命名为 employee.json

答案：

scala> import org.apache.spark.sql.SparkSession scala> val spark=SparkSession.builder().getOrCreate() scala> import spark.implicits._ scala> val df = spark.read.json("file:///usr/local/spark/employee.json")

（1）查询 DataFrame 的所有数据：scala> df.show()

（2）查询所有数据，并去除重复的数据：scala> df.distinct().show()

（3）查询所有数据，打印时去除 id 字段：scala> df.drop("id").show()

（4）筛选 age>20 的记录：scala> df.filter(df("age") > 30 ).show()

（5）将数据按 name 分组：scala> df.groupBy("name").count().show()

（6）将数据按 name 升序排列：scala> df.sort(df("name").asc).show()

（7）取出前 3 行数据：scala> df.take(3) 或 scala> df.head(3)

（8）查询所有记录的 name 列，并为其取别名为 username ：scala> df.select(df("name").as("username")).show()

（9）查询年龄 age 的平均值：scala> df.agg("age"->"avg")

（10）查询年龄 age 的最小值：scala> df.agg("age"->"min")

2.编程实现将RDD转换成DataFrame

答案：假设当前目录为/usr/local/spark/mycode/rddtodf，在当前目录下新建一个目录 mkdir -p src/main/scala ，然后在目录/usr/local/spark/mycode/rddtodf/src/main/scala 下新建一个 rddtodf.scala，复制下面代码

import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder 
import org.apache.spark.sql.Encoder 
import spark.implicits._ object RDDtoDF {    
         def main(args: Array[String]) { 
             case class Employee(id:Long,name: String, age: Long) 
             val employeeDF = 
         spark.sparkContext.textFile("file:///usr/local/spark/employee.txt").map(_.split(",")).map(at tributes => Employee(attributes(0).trim.toInt,attributes(1), attributes(2).trim.toInt)).toDF()      
employeeDF.createOrReplaceTempView("employee") 
         val employeeRDD = spark.sql("select id,name,age from employee") employeeRDD.map(t => "id:"+t(0)+","+"name:"+t(1)+","+"age:"+t(2)).show()     } }

在目录/usr/local/spark/mycode/rddtodf 目录下新建 simple.sbt，复制下面代码：

name := "Simple Project" 
version := "1.0"
scalaVersion := "2.11.8" 
libraryDependencies += "org.apache.spark" % "spark-core" % "2.1.0"

在目录/usr/local/spark/mycode/rddtodf 下执行下面命令打包程序：/usr/local/sbt/sbt package

最后在目录/usr/local/spark/mycode/rddtodf 下执行下面命令提交程序：/usr/local/spark/bin/spark-submit --class " RDDtoDF " /usr/local/spark/mycode/rddtodf/target/scala-2.11/simple-project_2.11-1.0.jar

3.编程实现利用DataFrame读写mysql数据库

答案：

（1）mysql> create database sparktest;

mysql> use sparktest;

mysql> create table employee (id int(4), name char(20), gender char(4), age int(4));

mysql> insert into employee values(1,'Alice','F',22);

mysql> insert into employee values(2,'John','M',25);

（2）假设当前目录为/usr/local/spark/mycode/testmysql，在当前目录下新建一个目录 mkdir -p src/main/scala ，然后在目录 /usr/local/spark/mycode/testmysql/src/main/scala 下新建一个 testmysql.scala，复制下面代码；

import java.util.Properties 
import org.apache.spark.sql.types._ 
import org.apache.spark.sql.Row 
object TestMySQL {     
        def main(args: Array[String]) { 
             val employeeRDD = spark.sparkContext.parallelize(Array("3 Mary F 26","4 Tom M 23")).map(_.split(" "))
        val schema = StructType(List(StructField("id", IntegerType, true),StructField("name", StringType, true),StructField("gender", StringType, true),StructField("age", IntegerType, true)))
        val rowRDD = employeeRDD.map(p => Row(p(0).toInt,p(1).trim, p(2).trim,p(3).toInt)) 
        val employeeDF = spark.createDataFrame(rowRDD, schema) val prop = new Properties() 
        prop.put("user", "root")  
        prop.put("password", "hadoop")                  prop.put("driver","com.mysql.jdbc.Driver")    
 employeeDF.write.mode("append").jdbc("jdbc:mysql://localhost:3306/sparktest", sparktest.employee", prop) 
       val jdbcDF = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/sparktest").option("driver","com.mysql.jdbc.Driver").optio n("dbtable","employee").option("user","root").option("password", "hadoop").load() jdbcDF.agg("age" -> "max", "age" -> "sum")    
     } 
 }

在目录/usr/local/spark/mycode/testmysql 目录下新建 simple.sbt，复制下面代码：

name := "Simple Project" 
version := "1.0" 
scalaVersion := "2.11.8" 
libraryDependencies += "org.apache.spark" % "spark-core" % "2.1.0"

在目录/usr/local/spark/mycode/testmysql 下执行下面命令打包程序：/usr/local/sbt/sbt package

最后在目录/usr/local/spark/mycode/testmysql 下执行下面命令提交程序：

/usr/local/spark/bin/spark-submit --class " TestMySQL " /usr/local/spark/mycode/testmysql/target/scala-2.11/simple-project_2.11-1.0.jar

实验5 Spark SQL编程初级实践

猜你喜欢