Spark SQL程序实现RDD转换DataFrame（一）

通过反射推断Schema

在Spark SQL中有两种方式可以在DataFrame和RDD进行转换

利用反射机制，推导包含某种类型的RDD，通过反射将其转换为指定类型的DataFrame，适用于提前知道RDD的schema。

通过编程接口与RDD进行交互获取schema，并动态创建DataFrame，在运行时决定列及其类型。

1、创建maven工程添加依赖

<properties>
<scala.version>2.11.8</scala.version>
<hadoop.version>2.7.4</hadoop.version>
<spark.version>2.0.2</spark.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.38</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.0.2</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.11</artifactId>
<version>2.0.2</version>
</dependency>
</dependencies>

<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-dependencyfile</arg>
&
4000
lt;arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass></mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>

2、代码实现

Scala支持使用case class类型导入RDD转换为DataFrame，通过case class创建schema，case class的参数名称会被反射读取并成为表的列名。这种RDD可以高效的转换为DataFrame并注册为表。

package cn.cheng.sql
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, SparkSession}

/**
* RDD转化成DataFrame:利用反射机制
*/
//todo:定义一个样例类Person
case class Person(id:Int,name:String,age:Int) extends Serializable

object InferringSchema {

def main(args: Array[String]): Unit = {
//todo：1、构建sparkSession 指定appName和master的地址
val spark: SparkSession = SparkSession.builder()
.appName("InferringSchema")
.master("local[2]").getOrCreate()
//todo:2、从sparkSession获取sparkContext对象
val sc: SparkContext = spark.sparkContext
sc.setLogLevel("WARN")//设置日志输出级别
//todo:3、加载数据
val dataRDD: RDD[String] = sc.textFile("D:\\person.txt")
//todo:4、切分每一行记录
val lineArrayRDD: RDD[Array[String]] = dataRDD.map(_.split(" "))
//todo:5、将RDD与Person类关联
val personRDD: RDD[Person] = lineArrayRDD.map(x=>Person(x(0).toInt,x(1),x(2).toInt))
//todo:6、创建dataFrame,需要导入隐式转换
import spark.implicits._
val personDF: DataFrame = personRDD.toDF()

//todo-------------------DSL语法操作 start--------------
//1、显示DataFrame的数据，默认显示20行
personDF.show()
//2、显示DataFrame的schema信息
personDF.printSchema()
//3、显示DataFrame记录数
println(personDF.count())
//4、显示DataFrame的所有字段
personDF.columns.foreach(println)
//5、取出DataFrame的第一行记录
println(personDF.head())
//6、显示DataFrame中name字段的所有值
personDF.select("name").show()
//7、过滤出DataFrame中年龄大于30的记录
personDF.filter($"age" > 30).show()
//8、统计DataFrame中年龄大于30的人数
println(personDF.filter($"age">30).count())
//9、统计DataFrame中按照年龄进行分组，求每个组的人数
personDF.groupBy("age").count().show()
//todo-------------------DSL语法操作 end-------------

//todo--------------------SQL操作风格 start-----------
//todo:将DataFrame注册成表
personDF.createOrReplaceTempView("t_person")
//todo:传入sql语句，进行操作

spark.sql("select * from t_person").show()

spark.sql("select * from t_person where name='zhangsan'").show()

spark.sql("select * from t_person order by age desc").show()
//todo--------------------SQL操作风格 end-------------

sc.stop()
}
}

喜欢就点赞评论+关注吧



感谢阅读，希望能帮助到大家，谢谢大家的支持！