1 package big.data.analyse.udfudaf
 2 
 3 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
 4 import org.apache.spark.sql.{Row, SparkSession}
 5 
 6 /**
 7   * Created by zhen on 2018/11/25.
 8   */
 9 object SparkUdfUdaf {
10   def isAdult(age : Int) ={
11     if(age > 18){
 
12       true
13     }else{
14       false
15     }
16   }
17   def main(args: Array[String]) {
18     val spark = SparkSession
19       .builder()
20       .appName("UdfUdaf")
21       .master("local[2]")
22       .getOrCreate()
23     val userData = Array(
24       "2015,11,www.baidu.com",
25       "2016,14,www.google.com",
 
26       "2017,13,www.apache.com",
27       "2015,21,www.spark.com",
28       "2016,32,www.hadoop.com",
29       "2017,18,www.solr.com",
30       "2017,14,www.hive.com"
31     )
32     val sc = spark.sparkContext
33     val sqlContext = spark.sqlContext
34     val userDataRDD = sc.parallelize(userData) // 轉化為RDD
 

35     val userDataType = userDataRDD.map(line => {
36         val Array(age, id, url) = line.split(",")
37         Row(
38           age, id.toInt, url
39         )
40       })
41     val structTypes = StructType(Array(
42       StructField("age", StringType, true),
43       StructField("id", IntegerType, true),
44       StructField("url", StringType, true)
45     ))
46     // RDD轉化為DataFrame
47     val userDataFrame = sqlContext.createDataFrame(userDataType,structTypes)
48     // 註冊臨時表
49     userDataFrame.createOrReplaceTempView("udf")
50     // 註冊udf(方式一)
51     spark.udf.register("getLength", (str : String) => str.length)
52     // 註冊udf(方式二)
53     spark.udf.register("isAdult", isAdult _)
54     //執行sql
55     val sql = "select * from udf where getLength(udf.url)=13 and isAdult(udf.id)"
56     val result = sqlContext.sql(sql)
57     result.foreach(println(_))
58   }
59 }

結果：

Spark之UDF