Java和scala实现 Spark RDD转换成DataFrame的两种方法小结
作者：黑白调92
今天小编就为大家分享一篇Java和scala实现 Spark RDD转换成DataFrame的两种方法小结，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
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脚本之家。