Spark-Sql之DataFrame实战详解

1、DataFrame简介：

在Spark中，DataFrame是一种以RDD为基础的分布式数据据集，类似于传统数据库听二维表格，DataFrame带有Schema元信息，即DataFrame所表示的二维表数据集的每一列都带有名称和类型。

类似这样的

root
 |-- age: long (nullable = true)
 |-- id: long (nullable = true)
 |-- name: string (nullable = true)  

2、准备测试结构化数据集

people.json

{"id":1, "name":"Ganymede", "age":32}
{"id":2, "name":"Lilei", "age":19}
{"id":3, "name":"Lily", "age":25}
{"id":4, "name":"Hanmeimei", "age":25}
{"id":5, "name":"Lucy", "age":37}
{"id":6, "name":"Tom", "age":27}  

3、通过编程方式理解DataFrame

1)  通过DataFrame的API来操作数据

import org.apache.spark.sql.SQLContext
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.log4j.Level
import org.apache.log4j.Logger  

object DataFrameTest {
  def main(args: Array[String]): Unit = {
    //日志显示级别
    Logger.getLogger("org.apache.spark").setLevel(Level.ERROR)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.ERROR)  

    //初始化
    val conf = new SparkConf().setAppName("DataFrameTest")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val df = sqlContext.read.json("people.json")  

    //查看df中的数据
    df.show()
    //查看Schema
    df.printSchema()
    //查看某个字段
    df.select("name").show()
    //查看多个字段，plus为加上某值
    df.select(df.col("name"), df.col("age").plus(1)).show()
    //过滤某个字段的值
    df.filter(df.col("age").gt(25)).show()
    //count group 某个字段的值
    df.groupBy("age").count().show()  

    //foreach 处理各字段返回值
    df.select(df.col("id"), df.col("name"), df.col("age")).foreach { x =>
      {
        //通过下标获取数据
        println("col1: " + x.get(0) + ", col2: " + "name: " + x.get(2) + ", col3: " + x.get(2))
      }
    }  

    //foreachPartition 处理各字段返回值，生产中常用的方式
    df.select(df.col("id"), df.col("name"), df.col("age")).foreachPartition { iterator =>
      iterator.foreach(x => {
        //通过字段名获取数据
        println("id: " + x.getAs("id") + ", age: " + "name: " + x.getAs("name") + ", age: " + x.getAs("age"))  

      })
    }  

  }
}  

 

2）通过注册表，操作sql的方式来操作数据

1. import org.apache.spark.sql.SQLContext
   import org.apache.spark.SparkConf
   import org.apache.spark.SparkContext
   import org.apache.log4j.Level
   import org.apache.log4j.Logger  
   
   /**
    * @author Administrator
    */
   object DataFrameTest2 {
     def main(args: Array[String]): Unit = {
       Logger.getLogger("org.apache.spark").setLevel(Level.ERROR);
       Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.ERROR);  
   
       val conf = new SparkConf().setAppName("DataFrameTest2")
       val sc = new SparkContext(conf)
       val sqlContext = new SQLContext(sc)
       val df = sqlContext.read.json("people.json")  
   
       df.registerTempTable("people")  
   
       df.show();
       df.printSchema();  
   
       //查看某个字段
       sqlContext.sql("select name from people ").show()
       //查看多个字段
       sqlContext.sql("select name,age+1 from people ").show()
       //过滤某个字段的值
       sqlContext.sql("select age from people where age>=25").show()
       //count group 某个字段的值
       sqlContext.sql("select age,count(*) cnt from people group by age").show()  
   
       //foreach 处理各字段返回值
       sqlContext.sql("select id,name,age  from people ").foreach { x =>
         {
           //通过下标获取数据
           println("col1: " + x.get(0) + ", col2: " + "name: " + x.get(2) + ", col3: " + x.get(2))
         }
       }  
   
       //foreachPartition 处理各字段返回值，生产中常用的方式
       sqlContext.sql("select id,name,age  from people ").foreachPartition { iterator =>
         iterator.foreach(x => {
           //通过字段名获取数据
           println("id: " + x.getAs("id") + ", age: " + "name: " + x.getAs("name") + ", age: " + x.getAs("age"))  
   
         })
       }  
   
     }
   }  

两种方式运行结果是一样的，第一种适合程序员，第二种适合熟悉sql的人员。

4、对于非结构化的数据

people.txt

1. 1,Ganymede,32
   2, Lilei, 19
   3, Lily, 25
   4, Hanmeimei, 25
   5, Lucy, 37
   6, wcc, 4  

1)  通过字段反射来映射注册临时表

     import org.apache.spark.sql.SQLContext  

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.Row  

/**
 * @author Administrator
 */
object DataFrameTest3 {
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.ERROR);
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.ERROR);  

    val conf = new SparkConf().setAppName("DataFrameTest3")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val people = sc.textFile("people.txt")  

    val peopleRowRDD = people.map { x => x.split(",") }.map { data =>
      {
        val id = data(0).trim().toInt
        val name = data(1).trim()
        val age = data(2).trim().toInt
        Row(id, name, age)
      }
    }  

    val structType = StructType(Array(
      StructField("id", IntegerType, true),
      StructField("name", StringType, true),
      StructField("age", IntegerType, true)));  

    val df = sqlContext.createDataFrame(peopleRowRDD, structType);  

    df.registerTempTable("people")  

    df.show()
    df.printSchema()  

  }
}  

2)   通过case class反射来映射注册临时表

import org.apache.spark.sql.SQLContext
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.log4j.Level
import org.apache.log4j.Logger
import org.apache.spark.sql.types.IntegerType
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.types.StructField
import org.apache.spark.sql.Row  

/**
 * @author Administrator
 */
object DataFrameTest4 {
  case class People(id: Int, name: String, age: Int)
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.ERROR);
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.ERROR);  

    val conf = new SparkConf().setAppName("DataFrameTest4")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val people = sc.textFile("people.txt")  

    val peopleRDD = people.map { x => x.split(",") }.map { data =>
      {
        People(data(0).trim().toInt, data(1).trim(), data(2).trim().toInt)
      }
    }  

    //这里需要隐式转换一把
    import sqlContext.implicits._
    val df = peopleRDD.toDF()
    df.registerTempTable("people")  

    df.show()
    df.printSchema()  

  }
}  

5、总结：

Spark SQL是Spark中的一个模块，主要用于进行结构化数据的处理。它提供的最核心的编程抽象，就是DataFrame。同时Spark SQL还可以作为分布式的SQL查询引擎。Spark SQL最重要的功能之一，就是从Hive中查询数据。

DataFrame，可以理解为是，以列的形式组织的，分布式的数据集合。它其实和关系型数据库中的表非常类似，但是底层做了很多的优化。DataFrame可以通过很多来源进行构建，包括：结构化的数据文件，Hive中的表，外部的关系型数据库，以及RDD。