Spark中RDD转换成DataFrame的两种方式（分别用Java和Scala实现）

一：准备数据源

    在项目下新建一个student.txt文件，里面的内容为：

,zhangsan,
,lisi,
,wanger,
,fangliu,

二：实现

Java版：

1.首先新建一个student的Bean对象，实现序列化和toString()方法，具体代码如下：

import java.io.Serializable;  

@SuppressWarnings("serial")
public class Student implements Serializable {  

    String sid;
    String sname;
    int sage;
    public String getSid() {
        return sid;
    }
    public void setSid(String sid) {
        this.sid = sid;
    }
    public String getSname() {
        return sname;
    }
    public void setSname(String sname) {
        this.sname = sname;
    }
    public int getSage() {
        return sage;
    }
    public void setSage(int sage) {
        this.sage = sage;
    }
    @Override
    public String toString() {
        return "Student [sid=" + sid + ", sname=" + sname + ", sage=" + sage + "]";
    }
}  

2.转换，具体代码如下

import java.util.ArrayList;  

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SaveMode;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;  

public class TxtToParquetDemo {  

    public static void main(String[] args) {  

        SparkConf conf = new SparkConf().setAppName("TxtToParquet").setMaster("local");
        SparkSession spark = SparkSession.builder().config(conf).getOrCreate();  

        reflectTransform(spark);//Java反射
        dynamicTransform(spark);//动态转换
    }  

    /**
     * 通过Java反射转换
     * @param spark
     */
    private static void reflectTransform(SparkSession spark)
    {
        JavaRDD<String> source = spark.read().textFile("stuInfo.txt").javaRDD();  

        JavaRDD<Student> rowRDD = source.map(line -> {
            String parts[] = line.split(",");  

            Student stu = new Student();
            stu.setSid(parts[]);
            stu.setSname(parts[]);
            stu.setSage(Integer.valueOf(parts[]));
            return stu;
        });  

        Dataset<Row> df = spark.createDataFrame(rowRDD, Student.class);
        df.select("sid", "sname", "sage").
        coalesce().write().mode(SaveMode.Append).parquet("parquet.res");
    }
    /**
     * 动态转换
     * @param spark
     */
    private static void dynamicTransform(SparkSession spark)
    {
        JavaRDD<String> source = spark.read().textFile("stuInfo.txt").javaRDD();  

        JavaRDD<Row> rowRDD = source.map( line -> {
            String[] parts = line.split(",");
            String sid = parts[];
            String sname = parts[];
            int sage = Integer.parseInt(parts[]);  

            return RowFactory.create(
                    sid,
                    sname,
                    sage
                    );
        });  

        ArrayList<StructField> fields = new ArrayList<StructField>();
        StructField field = null;
        field = DataTypes.createStructField("sid", DataTypes.StringType, true);
        fields.add(field);
        field = DataTypes.createStructField("sname", DataTypes.StringType, true);
        fields.add(field);
        field = DataTypes.createStructField("sage", DataTypes.IntegerType, true);
        fields.add(field);  

        StructType schema = DataTypes.createStructType(fields);  

        Dataset<Row> df = spark.createDataFrame(rowRDD, schema);
        df.coalesce().write().mode(SaveMode.Append).parquet("parquet.res1");
    }
}  

scala版本：

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.IntegerType  

object RDD2Dataset {  

  case class Student(id:Int,name:String,age:Int)
  def main(args:Array[String])
  {  

    val spark=SparkSession.builder().master("local").appName("RDD2Dataset").getOrCreate()
    import spark.implicits._
    reflectCreate(spark)
    dynamicCreate(spark)
  }  

 /**
     * 通过Java反射转换
     * @param spark
     */
  private def reflectCreate(spark:SparkSession):Unit={
    import spark.implicits._
    val stuRDD=spark.sparkContext.textFile("student2.txt")
    //toDF()为隐式转换
    val stuDf=stuRDD.map(_.split(",")).map(parts⇒Student(parts().trim.toInt,parts(),parts().trim.toInt)).toDF()
    //stuDf.select("id","name","age").write.text("result") //对写入文件指定列名
    stuDf.printSchema()
    stuDf.createOrReplaceTempView("student")
    val nameDf=spark.sql("select name from student where age<20")
    //nameDf.write.text("result") //将查询结果写入一个文件
    nameDf.show()
  }  

  /**
     * 动态转换
     * @param spark
     */
  private def dynamicCreate(spark:SparkSession):Unit={
    val stuRDD=spark.sparkContext.textFile("student.txt")
    import spark.implicits._
    val schemaString="id,name,age"
    val fields=schemaString.split(",").map(fieldName => StructField(fieldName, StringType, nullable = true))
    val schema=StructType(fields)
    val rowRDD=stuRDD.map(_.split(",")).map(parts⇒Row(parts(),parts(),parts()))
    val stuDf=spark.createDataFrame(rowRDD, schema)
        stuDf.printSchema()
    val tmpView=stuDf.createOrReplaceTempView("student")
    val nameDf=spark.sql("select name from student where age<20")
    //nameDf.write.text("result") //将查询结果写入一个文件
    nameDf.show()
  }
}  

注：1.上面代码全都已经测试通过，测试的环境为spark2.1.0，jdk1.8。

2.此代码不适用于spark2.0以前的版本。