Scala 和 Spark UDF 函数 [英] Scala and Spark UDF function

问题描述

我制作了一个简单的 UDF 来转换或从 spark 中临时表中的时间字段中提取一些值.我注册了该函数，但是当我使用 sql 调用该函数时，它会抛出 NullPointerException.下面是我的函数和执行过程.我正在使用齐柏林飞艇.奇怪的是，这是昨天工作，但今天早上停止工作.

I made a simple UDF to convert or extract some values from a time field in a temptabl in spark. I register the function but when I call the function using sql it throws a NullPointerException. Below is my function and process of executing it. I am using Zeppelin. Strangly this was working yesterday but it stopped working this morning.

功能

def convert( time:String ) : String = {
  val sdf = new java.text.SimpleDateFormat("HH:mm")
  val time1 = sdf.parse(time)
  return sdf.format(time1)
}

注册函数

sqlContext.udf.register("convert",convert _)

在没有 SQL 的情况下测试函数 -- 这有效

Test the function without SQL -- This works

convert(12:12:12) -> returns 12:12

在 Zeppelin 中使用 SQL 测试函数失败.

Test the function with SQL in Zeppelin this FAILS.

%sql
select convert(time) from temptable limit 10

temptable的结构

Structure of temptable

root
 |-- date: string (nullable = true)
 |-- time: string (nullable = true)
 |-- serverip: string (nullable = true)
 |-- request: string (nullable = true)
 |-- resource: string (nullable = true)
 |-- protocol: integer (nullable = true)
 |-- sourceip: string (nullable = true)

我得到的堆栈跟踪的一部分.

Part of the stacktrace that I am getting.

java.lang.NullPointerException
    at org.apache.hadoop.hive.ql.exec.FunctionRegistry.getFunctionInfo(FunctionRegistry.java:643)
    at org.apache.hadoop.hive.ql.exec.FunctionRegistry.getFunctionInfo(FunctionRegistry.java:652)
    at org.apache.spark.sql.hive.HiveFunctionRegistry.lookupFunction(hiveUdfs.scala:54)
    at org.apache.spark.sql.hive.HiveContext$$anon$3.org$apache$spark$sql$catalyst$analysis$OverrideFunctionRegistry$$super$lookupFunction(HiveContext.scala:376)
    at org.apache.spark.sql.catalyst.analysis.OverrideFunctionRegistry$$anonfun$lookupFunction$2.apply(FunctionRegistry.scala:44)
    at org.apache.spark.sql.catalyst.analysis.OverrideFunctionRegistry$$anonfun$lookupFunction$2.apply(FunctionRegistry.scala:44)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.sql.catalyst.analysis.OverrideFunctionRegistry$class.lookupFunction(FunctionRegistry.scala:44)

推荐答案

使用udf而不是直接定义一个函数

Use udf instead of define a function directly

import org.apache.spark.sql.functions._

val convert = udf[String, String](time => {
        val sdf = new java.text.SimpleDateFormat("HH:mm")
        val time1 = sdf.parse(time)
        sdf.format(time1)
    }
)

udf 的输入参数是列(或列).返回类型是列.

A udf's input parameter is Column(or Columns). And the return type is Column.

case class UserDefinedFunction protected[sql] (
    f: AnyRef,
    dataType: DataType,
    inputTypes: Option[Seq[DataType]]) {

  def apply(exprs: Column*): Column = {
    Column(ScalaUDF(f, dataType, exprs.map(_.expr), inputTypes.getOrElse(Nil)))
  }
}

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