sparksession创建DataFrame方式

spark创建dataFrame方式有很多种

一：通过类反射机制

举两个例子

1.通过List创建dataFrame

  /**
   * Applies a schema to a List of Java Beans.
   *
   * WARNING: Since there is no guaranteed ordering for fields in a Java Bean,
   *          SELECT * queries will return the columns in an undefined order.
   * @since 1.6.0
   */
  def createDataFrame(data: java.util.List[_], beanClass: Class[_]): DataFrame = {
    val attrSeq = getSchema(beanClass)
    val rows = SQLContext.beansToRows(data.asScala.iterator, beanClass, attrSeq)
    Dataset.ofRows(self, LocalRelation(attrSeq, rows.toSeq))
  }

2. 通过JavaRDD创建dataFrame

  /**
   * Applies a schema to an RDD of Java Beans.
   *
   * WARNING: Since there is no guaranteed ordering for fields in a Java Bean,
   * SELECT * queries will return the columns in an undefined order.
   *
   * @since 2.0.0
   */
  def createDataFrame(rdd: JavaRDD[_], beanClass: Class[_]): DataFrame = {
    createDataFrame(rdd.rdd, beanClass)
  }

　　

方式1样例代码：

ArrayList list = new ArrayList();
    GciGri g = new GciGri();
    g.setGci((gci));
    g.setGri((gri));
    list.add(g);

spark.createDataFrame(list, GciGri.class).createOrReplaceTempView("testtesttest");

　　

package cn.com.dtmobile.test;

import java.io.Serializable;

public class GciGri implements Serializable {
	
	private static final long serialVersionUID = 1L;
	
    private int Gci;
    private int Gri;
    
	public int getGci() {
		return Gci;
	}
	public void setGci(int gci) {
		Gci = gci;
	}
	public int getGri() {
		return Gri;
	}
	public void setGri(int gri) {
		Gri = gri;
	}
    
    
}

方式2跟方式1差不多一样，就不贴demo了

 

二：编程指定schema

// Create an RDD
JavaRDD peopleRDD = spark.sparkContext()
  .textFile("examples/src/main/resources/people.txt", 1)
  .toJavaRDD();

// The schema is encoded in a string
String schemaString = "name age";

// Generate the schema based on the string of schema
List fields = new ArrayList<>();
for (String fieldName : schemaString.split(" ")) {
  StructField field = DataTypes.createStructField(fieldName, DataTypes.StringType, true);
  fields.add(field);
}
StructType schema = DataTypes.createStructType(fields);

// Convert records of the RDD (people) to Rows
JavaRDD rowRDD = peopleRDD.map((Function) record -> {
  String[] attributes = record.split(",");
  return RowFactory.create(attributes[0], attributes[1].trim());
});

// Apply the schema to the RDD
Dataset peopleDataFrame = spark.createDataFrame(rowRDD, schema);

// Creates a temporary view using the DataFrame
peopleDataFrame.createOrReplaceTempView("people");

// SQL can be run over a temporary view created using DataFrames
Dataset results = spark.sql("SELECT name FROM people");

 跟上面的基本一样，但是通过传入List生成

　　String schemaString = "AoD,EoD,angle,gain";

　　List fields = new ArrayList<>();
　　for (String fieldName : schemaString.split(",")) {
　　　　StructField field = DataTypes.createStructField(fieldName, DataTypes.DoubleType, true);
　　　　fields.add(field);
　　　　}
　　StructType schema = DataTypes.createStructType(fields);

　　List Horizontal = new ArrayList();

　　Row row = RowFactory.create(60,24,180,1.3520);

　　Horizontal.add(row);

　　spark.createDataFrame(Horizontal, schema).write().mode(SaveMode.Overwrite).saveAsTable("beam_pattern");

 

转载于:https://www.cnblogs.com/dtmobile-ksw/p/11284223.html