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在Spark-1.3新加的最重要的新特性之一DataFrame的引入，很类似在R语言中的DataFrame的操作，使得Spark-Sql更稳定高效。
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"))
})
}
}
}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
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()
}
}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。
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原文地址：http://blog.csdn.net/kwu_ganymede/article/details/51337952
