Spark2.2.0实战：RDD转DataFrame两种方式（上）

Spark SQL支持两种不一样的方法将现有的RDDs转换为数据集。java

    第一个方法：使用反射来推断包含特定对象类型的RDD的模式。这种基于反射的方法使代码更加简洁，而且当您在编写Spark应用程序时已经了解了模式时，它能够很好地工做。sql

第一种方法代码实例java版本实现：apache

    数据准备studentDatatxt
api

1001,20,zhangsan1002,17,lisi1003,24,wangwu1004,16,zhaogang

    本地模式代码实现：ide

package com.unicom.ljs.spark220.study;
import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaRDD;import org.apache.spark.api.java.JavaSparkContext;import org.apache.spark.api.java.function.Function;import org.apache.spark.api.java.function.VoidFunction;import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Row;import org.apache.spark.sql.SQLContext;
/** * @author: Created By lujisen * @company ChinaUnicom Software JiNan * @date: 2020-01-20 08:58 * @version: v1.0 * @description: com.unicom.ljs.spark220.study */public class RDD2DataFrameReflect {    public static void main(String[] args) {        SparkConf sparkConf = new SparkConf().setMaster("local[*]").setAppName("RDD2DataFrameReflect");        JavaSparkContext sc = new JavaSparkContext(sparkConf);        SQLContext sqlContext=new SQLContext(sc);
        JavaRDD<String> lines = sc.textFile("C:\\Users\\Administrator\\Desktop\\studentData.txt");        JavaRDD<Student2> studentRDD = lines.map(new Function<String, Student2>() {            @Override            public Student2 call(String line) throws Exception {                String[] split = line.split(",");                Student2 student=new Student2();                student.setId(Integer.valueOf(split[0]));                student.setAge(Integer.valueOf(split[1]));                student.setName(split[2]);                return student;            }        });        //使用反射方式将RDD转换成dataFrame        //将Student.calss传递进去，其实就是利用反射的方式来建立DataFrame        Dataset<Row> dataFrame = sqlContext.createDataFrame(studentRDD, Student2.class);        //拿到DataFrame以后将其注册为临时表，而后针对其中的数据执行SQL语句        dataFrame.registerTempTable("studentTable");
        //针对student临时表，执行sql语句查询年龄小于18岁的学生，        /*DataFrame rowDF */        Dataset<Row> dataset = sqlContext.sql("select * from  studentTable where age < 18");        JavaRDD<Row> rowJavaRDD = dataset.toJavaRDD();        JavaRDD<Student2> ageRDD = rowJavaRDD.map(new Function<Row, Student2>() {            @Override            public Student2 call(Row row) throws Exception {                Student2 student = new Student2();                student.setId(row.getInt(0));                student.setAge(row.getInt(1));                student.setName(row.getString(2));
                return student;            }        });        ageRDD.foreach(new VoidFunction<Student2>() {            @Override            public void call(Student2 student) throws Exception {                System.out.println(student.toString());            }        });    }}

Student2类：this

package com.unicom.ljs.spark220.study;
import java.io.Serializable;
/** * @author: Created By lujisen * @company ChinaUnicom Software JiNan * @date: 2020-01-20 08:57 * @version: v1.0 * @description: com.unicom.ljs.spark220.study */public class Student2 implements Serializable {    int  id;    int  age;    String name;
    public int getId() {        return id;    }
    public void setId(int id) {        this.id = id;    }
    public int getAge() {        return age;    }
    public void setAge(int age) {        this.age = age;    }
    public String getName() {        return name;    }
    public void setName(String name) {        this.name = name;    }
    @Override    public String toString() {        return "Student2{" +                "id=" + id +                ", age=" + age +                ", name='" + name + '\'' +                '}';    }}

pom.xml关键依赖：
spa

2.2.02.11.8

<dependency>    <groupId>org.apache.spark</groupId>    <artifactId>spark-sql_2.11</artifactId>    <version>${spark.version}</version></dependency><dependency>    <groupId>org.apache.spark</groupId>    <artifactId>spark-core_2.11</artifactId>    <version>${spark.version}</version></dependency>