本文简单介绍两种往SQLContext、HiveContext中注册自定义函数方法。
下边以sqlContext为例，在spark-shell下操作示例：
scala> sc
res5: org.apache.spark.SparkContext = org.apache.spark.SparkContext@35d4035f
scala> sqlContext
res7: org.apache.spark.sql.SQLContext = org.apache.spark.sql.hive.HiveContext@171b0d3
scala> val df = sc.parallelize(Seq(("张三", 25), ("李四", 30),("赵六", 27))).toDF("name", "age")
df: org.apache.spark.sql.DataFrame = [name: string, age: int]
scala> df.registerTempTable("emp")
1）外部定义函数：
scala> def remainWorkYears(age: Int) : Int = {
| 60 - age
| }
remainWorkYears: (age: Int)Int
scala> sqlContext.udf.register("remainWorkYears", remainWorkYears _)
res1: org.apache.spark.sql.UserDefinedFunction = UserDefinedFunction(<function1>,IntegerType,List())
scala> sqlContext.sql("select e.*, remainWorkYears(e.age) as remainedWorkYear from emp e").show
hiveContext.sql("select e.*, remainWorkYears(e.age) as remainedWorkYear from emp e").show
+----+---+----------------+
|name|age|remainedWorkYear|
+----+---+----------------+
| 张三| 25| 35|
| 李四| 30| 30|
| 赵六| 27| 33|
+----+---+----------------+
2）匿名函数：
scala> sqlContext.udf.register("remainWorkYears_anoymous", (age: Int) => {
| 60 - age
| })
res3: org.apache.spark.sql.UserDefinedFunction = UserDefinedFunction(<function1>,IntegerType,List())
scala> sqlContext.sql("select e.*, remainWorkYears_anoymous(e.age) as remainedWorkYear from emp e").show
+----+---+----------------+
|name|age|remainedWorkYear|
+----+---+----------------+
| 张三| 25| 35|
| 李四| 30| 30|
| 赵六| 27| 33|
+----+---+----------------+