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

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,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。