SQLContext、HiveContext自定義函式註冊

weixin_34119545發表於2017-12-25
SQLContextHive函式

本文簡單介紹兩種往SQLContext、HiveContext中註冊自定義函式方法。

下邊以sqlContext為例，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(("張三", 25), ("李四", 30),("趙六", 27))).toDF("name", "age")
df: org.apache.spark.sql.DataFrame = [name: string, age: int]
scala> df.registerTempTable("emp")
1）外部定義函式：
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|
+----+---+----------------+
|  張三| 25|              35|
|  李四| 30|              30|
|  趙六| 27|              33|
+----+---+----------------+
2）匿名函式：
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|
+----+---+----------------+
|  張三| 25|              35|
|  李四| 30|              30|
|  趙六| 27|              33|
+----+---+----------------+

 

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