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https://issues.apache.org/jira/browse/SPARK-44378?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Priyanka Raju updated SPARK-44378:
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Attachment: Screenshot 2023-07-11 at 9.36.19 AM.png
> Jobs that have join & have .rdd calls get executed 2x when AQE is enabled.
> --------------------------------------------------------------------------
>
> Key: SPARK-44378
> URL: https://issues.apache.org/jira/browse/SPARK-44378
> Project: Spark
> Issue Type: Question
> Components: Spark Submit
> Affects Versions: 3.1.2
> Reporter: Priyanka Raju
> Priority: Major
> Labels: aqe
> Attachments: Screenshot 2023-07-11 at 9.36.14 AM.png, Screenshot
> 2023-07-11 at 9.36.19 AM.png
>
>
> We have a few spark scala jobs that are currently running in production. Most
> jobs typically use Dataset, Dataframes. There is a small code in our custom
> library code, that makes rdd calls example to check if the dataframe is
> empty: df.rdd.getNumPartitions == 0
> When I enable aqe for these jobs, this .rdd is converted into a separate job
> of it's own and the entire dag is executed 2x, taking 2x more time. This does
> not happen when AQE is disabled. Why does this happen and what is the best
> way to fix the issue?
>
> Sample code to reproduce the issue:
>
>
> {code:java}
> import org.apache.spark.sql._
> case class Record(
> id: Int,
> name: String
> )
>
> val partCount = 4
> val input1 = (0 until 100).map(part => Record(part, "a"))
>
> val input2 = (100 until 110).map(part => Record(part, "c"))
>
> implicit val enc: Encoder[Record] = Encoders.product[Record]
>
> val ds1 = spark.createDataset(
> spark.sparkContext
> .parallelize(input1, partCount)
> )
>
> va
> l ds2 = spark.createDataset(
> spark.sparkContext
> .parallelize(input2, partCount)
> )
>
> val ds3 = ds1.join(ds2, Seq("id"))
> val l = ds3.count()
>
> val incomingPartitions = ds3.rdd.getNumPartitions
> log.info(s"Num partitions ${incomingPartitions}")
> {code}
>
> Spark UI for the same job with AQE, !Screenshot 2023-07-11 at 9.36.14 AM.png!
>
>
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