一、前言

Spark的发展史可以简单概括为三个阶段，分别为：RDD、DataFrame 和DataSet。在Spark 2.0之前，使用Spark必须先创建SparkConf和SparkContext，不过在Spark 2.0中只要创建一个SparkSession就可以了，SparkConf、SparkContext和SQLContext都已经被封装在SparkSession当中，它是Spark的一个全新切入点，大大降低了Spark的学习难度。

二、创建SparkSession

创建SparkSession的方式非常简单，如下：

//创建SparkSession
val spark = SparkSession.builder().master("local[*]").appName("dataset").enableHiveSupport()  //支持hive，如果代码中用不到hive的话，可以省略这一条.getOrCreate()

三、DataSet/DataFrame的创建

1、序列创建 DataSet

//1、产生序列dataset
val numDS = spark.range(5, 100, 5)
numDS.orderBy(desc("id")).show(5)  //降序排序，显示5个
numDS.describe().show()  //打印numDS的摘要

结果如下所示：

+---+
| id|
+---+
| 95|
| 90|
| 85|
| 80|
| 75|
+---+
only showing top 5 rows+-------+------------------+
|summary|                id|
+-------+------------------+
|  count|                19|
|   mean|              50.0|
| stddev|28.136571693556885|
|    min|                 5|
|    max|                95|
+-------+------------------+

2、集合创建 DataSet

首先创建几个可能用到的样例类：

//样例类
case class Person(name: String, age: Int, height: Int)
case class People(age: Int, names: String)
case class Score(name: String, grade: Int)

然后定义隐式转换：

import spark.implicits._

最后，定义集合，创建 DataSet

//2、集合转成dataset
val seq1 = Seq(Person("xzw", 24, 183), Person("yxy", 24, 178), Person("lzq", 25, 168))
val ds1 = spark.createDataset(seq1)
ds1.show()

结果如下所示：

+----+---+------+
|name|age|height|
+----+---+------+
| xzw| 24|   183|
| yxy| 24|   178|
| lzq| 25|   168|
+----+---+------+

3、RDD 转成 DataFrame

//3、RDD转成DataFrame
val array1 = Array((33, 24, 183), (33, 24, 178), (33, 25, 168))
val rdd1 = spark.sparkContext.parallelize(array1, 3).map(f => Row(f._1, f._2, f._3))
val schema = StructType(StructField("a", IntegerType, false) ::StructField("b", IntegerType, true) :: Nil
)
val rddToDataFrame = spark.createDataFrame(rdd1, schema)
rddToDataFrame.show(false)

结果如下所示：

+---+---+
|a  |b  |
+---+---+
|33 |24 |
|33 |24 |
|33 |25 |
+---+---+

4、读取文件

//4、读取文件，这里以csv文件为例
val ds2 = spark.read.csv("C://Users//Machenike//Desktop//xzw//test.csv")
ds2.show()

结果如下所示：

+---+---+----+
|_c0|_c1| _c2|
+---+---+----+
|xzw| 24| 183|
|yxy| 24| 178|
|lzq| 25| 168|
+---+---+----+

5、读取文件，并配置详细参数

//5、读取文件，并配置详细参数
val ds3 = spark.read.options(Map(("delimiter", ","), ("header", "false"))).csv("C://Users//Machenike//Desktop//xzw//test.csv")
ds3.show()

结果如下图所示：

+---+---+----+
|_c0|_c1| _c2|
+---+---+----+
|xzw| 24| 183|
|yxy| 24| 178|
|lzq| 25| 168|
+---+---+----+

四、DataSet 基础函数

//1、DataSet存储类型
val seq1 = Seq(Person("xzw", 24, 183), Person("yxy", 24, 178), Person("lzq", 25, 168))
val ds1 = spark.createDataset(seq1)
ds1.show()
ds1.checkpoint()
ds1.cache()
ds1.persist()
ds1.count()
ds1.unpersist(true)//2、DataSet结构属性
ds1.columns
ds1.dtypes
ds1.explain()//3、DataSet rdd数据互换
val rdd1 = ds1.rdd
val ds2 = rdd1.toDS()
ds2.show()
val df2 = rdd1.toDF()
df2.show()//4、保存文件
df2.select("name", "age", "height").write.format("csv").save("./save")

五、DataSet 的 Actions 操作

六、DataSet 的转化操作

package sparkmlimport org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._//样例类
case class Person(name: String, age: Int, height: Int)
case class People(age: Int, names: String)
case class Score(name: String, grade: Int)object WordCount2 {def main(args: Array[String]): Unit = {//设置日志输出格式Logger.getLogger("org").setLevel(Level.WARN)//创建SparkSessionval spark = SparkSession.builder().master("local[*]").appName("dataset").getOrCreate()import spark.implicits._//seq创建datasetval seq1 = Seq(Person("leo", 29, 170), Person("jack", 21, 170), Person("xzw", 21, 183))val ds1 = spark.createDataset(seq1)//1、map操作，flatmap操作ds1.map{x => (x.age + 1, x.name)}.show()ds1.flatMap{x =>val a = x.ageval s = x.name.split("").map{x => (a, x)}s}.show()//2、filter操作，where操作ds1.filter("age >= 25 and height >= 170").show()ds1.filter($"age" >= 25 && $"height" >= 170).show()ds1.filter{x => x.age >= 25 && x.height >= 170}.show()ds1.where("age >= 25 and height >= 170").show()ds1.where($"age" >= 25 && $"height" >= 170).show()//3、去重操作ds1.distinct().show()ds1.dropDuplicates("age").show()ds1.dropDuplicates("age", "height").show()ds1.dropDuplicates(Seq("age", "height")).show()ds1.dropDuplicates(Array("age", "height")).show()//4、加法减法操作val seq2 = Seq(Person("leo", 18, 183), Person("jack", 18, 175), Person("xzw", 22, 183), Person("lzq", 23, 175))val ds2 = spark.createDataset(seq2)val seq3 = Seq(Person("leo", 19, 183), Person("jack", 18, 175), Person("xzw", 22, 170), Person("lzq", 23, 175))val ds3 = spark.createDataset(seq3)ds3.union(ds2).show()  //并集ds3.except(ds2).show()  // 差集ds3.intersect(ds2).show()  //交集//5、select操作ds2.select("name", "age").show()ds2.select(expr("height + 1").as[Int].as("height")).show()//6、排序操作ds2.sort("age").show()  //默认升序排序ds2.sort($"age".desc, $"height".desc).show()ds2.orderBy("age").show()  //默认升序排序ds2.orderBy($"age".desc, $"height".desc).show()//7、分割抽样操作val ds4 = ds3.union(ds2)val rands = ds4.randomSplit(Array(0.3, 0.7))println(rands(0).count())println(rands(1).count())rands(0).show()rands(1).show()val ds5 = ds4.sample(true, 0.5)println(ds5.count())ds5.show()//8、列操作val ds6 = ds4.drop("height")println(ds6.columns)ds6.show()val ds7 = ds4.withColumn("add", $"age" + 2)println(ds7.columns)ds7.show()val ds8 = ds7.withColumnRenamed("add", "age_new")println(ds8.columns)ds8.show()ds4.withColumn("add_col", lit(1)).show()//9、join操作val seq4 = Seq(Score("leo", 85), Score("jack", 63), Score("wjl", 70), Score("zyn", 90))val ds9 = spark.createDataset(seq4)val ds10 = ds2.join(ds9, Seq("name"), "inner")ds10.show()val ds11 = ds2.join(ds9, Seq("name"), "left")ds11.show()//10、分组聚合操作val ds12 = ds4.groupBy("height").agg(avg("age").as("avg_age"))ds12.show()}
}

七、DataSet 的内置函数

八、例子：WordCount

package sparkmlimport org.apache.spark.sql.SparkSessionobject WordCount {def main(args: Array[String]): Unit = {//创建SparkSessionval spark = SparkSession.builder().appName("Dataset").master("local[*]").getOrCreate()import spark.implicits._val data = spark.read.textFile("C://xzw//wordcount").flatMap(_.split(" ")).map(_.toLowerCase()).filter($"value"=!="," && $"value"=!="." && $"value"=!="not")data.groupBy($"value").count().sort($"count".desc).show(50)}}