sqprk集群上使用自定义udf函数，出现无法序列化的错误

在spark集群上，将读取到的csv文件生成的datafream，需要对其中一列进行转化，内置的udf函数已经不能满足需求

所以需要自定义一个udf，但是在使用的时候报错，如下

Exception in thread "main" org.apache.spark.SparkException: Task not serializable
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:403)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:393)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:162)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2326)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:850)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:849)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:849)
    at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:630)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)

Caused by: java.io.NotSerializableException: org.apache.spark.SparkContext
Serialization stack:
    - object not serializable (class: org.apache.spark.SparkContext, value: org.apache.spark.SparkContext@7334dcbe)
    - field (class: algorithm.config.Base, name: sc, type: class org.apache.spark.SparkContext)
    - object (class algorithm.config.Base, algorithm.config.Base@4a6c33a0)
    - field (class: algorithm.config.Base$$anonfun$save_data_parquet$1, name: $outer, type: class algorithm.config.Base)
    - object (class algorithm.config.Base$$anonfun$save_data_parquet$1, <function1>)
    - field (class: algorithm.config.Base$$anonfun$save_data_parquet$1$$anonfun$2, name: $outer, type: class algorithm.config.Base$$anonfun$save_data_parquet$1)
    - object (class algorithm.config.Base$$anonfun$save_data_parquet$1$$anonfun$2, <function1>)
    - element of array (index: 25)
    - array (class [Ljava.lang.Object;, size 26)

百度了半天，最后总结如下：

当你执行各种转换（map，flatMap，filter等等）的时候，会有以下转换：
1、在driver节点上序列化，
2、上传到合适的集群中的节点，
3、在节点上执行反序列化，
4、最后在节点上执行。

自定义一个udf，并使用这个udf，那么Spark知道不能序列化这个方法，于是试图序列化整个类，因此才能使得这个方法能运行在其他JVM之上，正因为本例没有序列化，所以才出现异常。

解决方法：

1. 在val sc = spark.sparkContext 上面加 @transient