【Spark】这一篇或许能让你大概了解如何通过JavaAPI实现DataFrame的相关操作
2024-09-07 07:55:00
文章目录
需求概述
将RDD转换得到DataFrame，主要有两种方法：利用反射机制 和 通过编程结构与RDD进行交互。
步骤
一、创建Maven工程并导包
<properties>
<scala.version>2.11.8</scala.version>
<spark.version>2.2.0</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.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.7.5</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.0</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>UTF-8</encoding>
<!-- <verbal>true</verbal>-->
</configuration>
</plugin>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.0</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-dependencyfile</arg>
<arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.1.1</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>
二、选用第一种方法：利用反射机制配合样例类构建DataFrame
开发代码
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
// 定义person case class
case class Person(id: Int,name: String,age: Int)
object SparkDF {
def main(args: Array[String]): Unit = {
// 获取SparkSession
val sparkSession: SparkSession = SparkSession.builder().appName("SparkDF").master("local[2]").config("spark.driver.host", "localhost").getOrCreate()
// 获取SparkContext
val sparkContext: SparkContext = sparkSession.sparkContext
// 过滤筛选日志
sparkContext.setLogLevel("WARN")
//读取文件
val fileRDD: RDD[String] = sparkContext.textFile("/Users/zhaozhuang/Desktop/4、Spark/Spark第三天/Spark第三天教案/资料/person.txt")
//按照分隔符切割数据
val splitRDD: RDD[Array[String]] = fileRDD.map(x => x.split(" "))
//关联person case class
val personRDD: RDD[Person] = splitRDD.map(x => Person(x(0).toInt, x(1), x(2).toInt))
//准备把personRDD转换成DataFrame
//首先要导包
import sparkSession.implicits._
val personDF: DataFrame = personRDD.toDF()
//todo 至此已经成功通过反射机制构建DataFrame
/**
* 接下来可以使用DFL语法和SQL语法验证是否构建成功
*/
println("*******************************************DSL语法测试开始********************************************")
//查看全表数据 show()默认展示前20条数据
personDF.show()
//查看指定字段数据
personDF.select($"name",$"age").show()
//查看schema
personDF.printSchema()
//过滤筛选数据
personDF.filter($"age" > 25).show()
println("*******************************************DSL语法测试结束********************************************")
println("*******************************************SQL语法测试开始********************************************")
// 将DataFrame注册为一张table，有三种方法
//第一种 已过时
val person1: Unit = personDF.registerTempTable("person1")
//第二种
val person2: Unit = personDF.createTempView("person2")
//第三种 （推荐）
val person3: Unit = personDF.createOrReplaceTempView("person3")
//打印三钟表的数据
sparkSession.sql("select * from person1").show()
sparkSession.sql("select * from person2").show()
sparkSession.sql("select * from person3").show()
//实现left join操作
sparkSession.sql("select * from person1 p1 left join person2 p2 on p1.id = p2.id").show()
println("*******************************************SQL语法测试结束********************************************")
sparkContext.stop()
sparkSession.close()
}
}
控制台结果
*******************************************DSL语法测试开始********************************************
+---+--------+---+
| id| name|age|
+---+--------+---+
| 1|zhangsan|108|
| 2| lisi| 28|
| 3| wangwu| 58|
| 4| zhaoliu| 24|
| 5| zhuqi| 35|
| 6| qiuba| 28|
+---+--------+---+
+--------+---+
| name|age|
+--------+---+
|zhangsan|108|
| lisi| 28|
| wangwu| 58|
| zhaoliu| 24|
| zhuqi| 35|
| qiuba| 28|
+--------+---+
root
|-- id: integer (nullable = false)
|-- name: string (nullable = true)
|-- age: integer (nullable = false)
+---+--------+---+
| id| name|age|
+---+--------+---+
| 1|zhangsan|108|
| 2| lisi| 28|
| 3| wangwu| 58|
| 5| zhuqi| 35|
| 6| qiuba| 28|
+---+--------+---+
*******************************************DSL语法测试结束********************************************
*******************************************SQL语法测试开始********************************************
+---+--------+---+
| id| name|age|
+---+--------+---+
| 1|zhangsan|108|
| 2| lisi| 28|
| 3| wangwu| 58|
| 4| zhaoliu| 24|
| 5| zhuqi| 35|
| 6| qiuba| 28|
+---+--------+---+
+---+--------+---+
| id| name|age|
+---+--------+---+
| 1|zhangsan|108|
| 2| lisi| 28|
| 3| wangwu| 58|
| 4| zhaoliu| 24|
| 5| zhuqi| 35|
| 6| qiuba| 28|
+---+--------+---+
+---+--------+---+
| id| name|age|
+---+--------+---+
| 1|zhangsan|108|
| 2| lisi| 28|
| 3| wangwu| 58|
| 4| zhaoliu| 24|
| 5| zhuqi| 35|
| 6| qiuba| 28|
+---+--------+---+
+---+--------+---+---+--------+---+
| id| name|age| id| name|age|
+---+--------+---+---+--------+---+
| 1|zhangsan|108| 1|zhangsan|108|
| 6| qiuba| 28| 6| qiuba| 28|
| 3| wangwu| 58| 3| wangwu| 58|
| 5| zhuqi| 35| 5| zhuqi| 35|
| 4| zhaoliu| 24| 4| zhaoliu| 24|
| 2| lisi| 28| 2| lisi| 28|
+---+--------+---+---+--------+---+
*******************************************SQL语法测试结束********************************************
Process finished with exit code 0
选用第二种方法：通过StrucType配合Row构建DataFrame
开发代码
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
object SparkDF2 {
def main(args: Array[String]): Unit = {
//获取SparkSession
val sparkSession: SparkSession = SparkSession.builder().appName("saprkDF2").master("local[2]").config("spark.driver.host", "localhost").getOrCreate()
//获取SparkContext
val sparkContext: SparkContext = sparkSession.sparkContext
//设置日志筛选
sparkContext.setLogLevel("WARN")
//读取文件
val fileRDD: RDD[String] = sparkContext.textFile("/Users/zhaozhuang/Desktop/4、Spark/Spark第三天/Spark第三天教案/资料/person.txt")
//对数据进行切割
val arrayRDD: RDD[Array[String]] = fileRDD.map(x => x.split(" "))
//将arrayRDD转换为row
val rowRDD: RDD[Row] = arrayRDD.map(x => Row(x(0).toInt, x(1), x(2).toInt))
//获取StructType对象
val structType = new StructType().add("id",IntegerType).add("name",StringType).add("age",IntegerType)
//需要两个参数 RDD[Row] 和 StructType
val personDF: DataFrame = sparkSession.createDataFrame(rowRDD, structType)
//接下来DSL和SQL的操作和之前相同
personDF.printSchema()
sparkContext.stop()
sparkSession.close()
}
}
控制台结果
root
|-- id: integer (nullable = true)
|-- name: string (nullable = true)
|-- age: integer (nullable = true)
Process finished with exit code 0
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