Java和scala实现 Spark RDD转换成DataFrame的两种方法小结

一：准备数据源

在项目下新建一个student.txt文件，里面的内容为：

1,zhangsan,20
2,lisi,21
3,wanger,19
4,fangliu,18 

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：

package com.cxd.sql;
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

package com.cxd.sql;
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[0]);
   stu.setSname(parts[1]);
   stu.setSage(Integer.valueOf(parts[2]));
   return stu;
  });

  Dataset<Row> df = spark.createDataFrame(rowRDD, Student.class);
  df.select("sid", "sname", "sage").
  coalesce(1).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[0];
   String sname = parts[1];
   int sage = Integer.parseInt(parts[2]);

   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(1).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(0).trim.toInt,parts(1),parts(2).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(0),parts(1),parts(2)))
 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。

以上这篇Java和scala实现 Spark RDD转换成DataFrame的两种方法小结就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持我们。