RE: [SPARK-2.0][SQL] UDF containing non-serializable object does not work as expected

[Simon Scott]

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 <u...@spark.apache.org>; dev <dev@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<mailto: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<mailto: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