一：準備數據源
在項目下新建一個student.txt文件，里面的內容為：
1,zhangsan,20 2,lisi,21 3,wanger,19 4,fangliu,18
二：實現
Java版：
1.首先新建一個student的Bean對象，實現序列化和toString()方法，具體代碼如下：
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.轉換，具體代碼如下
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);//動態轉換 } /** * 通過Java反射轉換 * @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"); } /** * 動態轉換 * @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.SparkSessionimport org.apache.spark.sql.types.StringTypeimport org.apache.spark.sql.types.StructFieldimport org.apache.spark.sql.types.StructTypeimport org.apache.spark.sql.Rowimport org.apache.spark.sql.types.IntegerTypeobject 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) } /** * 通過Java反射轉換 * @param spark */ private def reflectCreate(spark:SparkSession):Unit={ import spark.implicits._ val stuRDD=spark.sparkContext.textFile("student2.txt") //toDF()為隱式轉換 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") //對寫入文件指定列名 stuDf.printSchema() stuDf.createOrReplaceTempView("student") val nameDf=spark.sql("select name from student where age<20") //nameDf.write.text("result") //將查詢結果寫入一個文件 nameDf.show() } /** * 動態轉換 * @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") //將查詢結果寫入一個文件 nameDf.show() }}注：
1.上面代碼全都已經測試通過，測試的環境為spark2.1.0，jdk1.8。
2.此代碼不適用于spark2.0以前的版本。
以上這篇Java和scala實現 Spark RDD轉換成DataFrame的兩種方法小結就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持VeVb武林網。
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