SparkSQL-DataFrams和RDD的相互转换

Spark SQL支持两种RDDs转换为DataFrames的方式：

• 使用反射获取RDD内的Schema
  • 当已知类的Schema的时候，使用这种基于反射的方法会让代码更加简洁而且效果也很好。
• 通过编程接口指定Schema
  • 通过Spark SQL的接口创建RDD的Schema，这种方式会让代码比较冗长。
  • 这种方法的好处是，在运行时才知道数据的列以及列的类型的情况下，可以动态生成Schema。

一、使用反射获取Schema

Spark SQL支持将JavaBean的RDD自动转换成DataFrame。通过反射获取Bean的基本信息，依据Bean的信息定义Schema。目前Spark SQL不支持嵌套的JavaBeans和复杂数据类型（如：List、Array）。创建一个实现Serializable接口包含所有属性getters和setters的类来创建一个JavaBean。通过调用createDataFrame并提供JavaBean的Class object，指定一个Schema给一个RDD。示例如下：

public static class Person implements Serializable {
  private String name;
  private int age;

  public String getName() {
    return name;
  }

  public void setName(String name) {
    this.name = name;
  }

  public int getAge() {
    return age;
  }

  public void setAge(int age) {
    this.age = age;
  }
}

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;

// Create an RDD of Person objects from a text file
JavaRDD peopleRDD = spark.read()
  .textFile("examples/src/main/resources/people.txt")
  .javaRDD()
  .map(line -> {
    String[] parts = line.split(",");
    Person person = new Person();
    person.setName(parts[0]);
    person.setAge(Integer.parseInt(parts[1].trim()));
    return person;
  });

// Apply a schema to an RDD of JavaBeans to get a DataFrame
Dataset peopleDF = spark.createDataFrame(peopleRDD, Person.class);
// Register the DataFrame as a temporary view
peopleDF.createOrReplaceTempView("people");

// SQL statements can be run by using the sql methods provided by spark
Dataset teenagersDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");

// The columns of a row in the result can be accessed by field index
Encoder stringEncoder = Encoders.STRING();
Dataset teenagerNamesByIndexDF = teenagersDF.map(
    (MapFunction) row -> "Name: " + row.getString(0),
    stringEncoder);
teenagerNamesByIndexDF.show();
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+

// or by field name
Dataset teenagerNamesByFieldDF = teenagersDF.map(
    (MapFunction) row -> "Name: " + row.getAs("name"),
    stringEncoder);
teenagerNamesByFieldDF.show();
// +------------+
// |       value|
// +------------+
// |Name: Justin|
// +------------+

二、通过编程接口指定Schema

当JavaBean不能被预先定义的时候，编程创建DataFrame分为三步：

• 从原来的RDD创建一个Row格式的RDD
• 创建与RDD中Rows结构匹配的StructType，通过该StructType创建表示RDD的Schema
• 通过SQLContext提供的createDataFrame方法创建DataFrame，方法参数为RDD的Schema

示例如下：

import java.util.ArrayList;
import java.util.List;

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

// 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");

// The results of SQL queries are DataFrames and support all the normal RDD operations
// The columns of a row in the result can be accessed by field index or by field name
Dataset namesDS = results.map(
    (MapFunction) row -> "Name: " + row.getString(0),
    Encoders.STRING());
namesDS.show();
// +-------------+
// |        value|
// +-------------+
// |Name: Michael|
// |   Name: Andy|
// | Name: Justin|
// +-------------+