That is unfortunately the way how Scala compiler captures (and defines) closures. Nothing is really final in the JVM. You can always use reflection or unsafe to modify the value of fields.
On Mon, Aug 8, 2016 at 8:16 PM, Simon Scott <simon.sc...@viavisolutions.com> wrote: > But does the “notSer” object have to be serialized? > > > > The object is immutable by the definition of A, so the only thing that > needs to be serialized is the (immutable) Int value? And Ints are > serializable? > > > > Just thinking out loud > > > > Simon Scott > > > > Research Developer @ viavisolutions.com > > > > *From:* Hao Ren [mailto:inv...@gmail.com] > *Sent:* 08 August 2016 09:03 > *To:* Muthu Jayakumar <bablo...@gmail.com> > *Cc:* user <user@spark.apache.org>; dev <d...@spark.apache.org> > *Subject:* Re: [SPARK-2.0][SQL] UDF containing non-serializable object > does not work as expected > > > > Yes, it is. > > You can define a udf like that. > > Basically, it's a udf Int => Int which is a closure contains a non > serializable object. > > The latter should cause Task not serializable exception. > > > > Hao > > > > On Mon, Aug 8, 2016 at 5:08 AM, Muthu Jayakumar <bablo...@gmail.com> > wrote: > > Hello Hao Ren, > > > > Doesn't the code... > > > > val add = udf { > > (a: Int) => a + notSer.value > > } > > Mean UDF function that Int => Int ? > > > > Thanks, > > Muthu > > > > On Sun, Aug 7, 2016 at 2:31 PM, Hao Ren <inv...@gmail.com> wrote: > > I am playing with spark 2.0 > > What I tried to test is: > > > > Create a UDF in which there is a non serializable object. > > What I expected is when this UDF is called during materializing the > dataFrame where the UDF is used in "select", an task non serializable > exception should be thrown. > > It depends also which "action" is called on that dataframe. > > > > Here is the code for reproducing the pb: > > > > ============ > > object DataFrameSerDeTest extends App { > > > > class A(val value: Int) // It is not serializable > > > > def run() = { > > val spark = SparkSession > > .builder() > > .appName("DataFrameSerDeTest") > > .master("local[*]") > > .getOrCreate() > > > > import org.apache.spark.sql.functions.udf > > import spark.sqlContext.implicits._ > > > > val notSer = new A(2) > > val add = udf { > > (a: Int) => a + notSer.value > > } > > val df = spark.createDataFrame(Seq( > > (1, 2), > > (2, 2), > > (3, 2), > > (4, 2) > > )).toDF("key", "value") > > .select($"key", add($"value").as("added")) > > > > df.show() // *It should not work because the udf contains a > non-serializable object, but it works* > > > > df.filter($"key" === 2).show() // *It does not work as expected > (org.apache.spark.SparkException: Task not serializable)* > > } > > > > run() > > } > > ============ > > > > Also, I tried collect(), count(), first(), limit(). All of them worked > without non-serializable exceptions. > > It seems only filter() throws the exception. (feature or bug ?) > > > > Any ideas ? Or I just messed things up ? > > Any help is highly appreciated. > > > > -- > > Hao Ren > > > > Data Engineer @ leboncoin > > > > Paris, France > > > > > > > > -- > > Hao Ren > > > > Data Engineer @ leboncoin > > > > Paris, France >
