[jira] [Commented] (SPARK-32728) Using groupby with rand creates different values when joining table with itself

["Takeshi Yamamuro (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-32728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17188070#comment-17188070 ] 

Takeshi Yamamuro commented on SPARK-32728:
------------------------------------------

+1 on the Sean comment, so I'll close this.

> Using groupby with rand creates different values when joining table with itself
> -------------------------------------------------------------------------------
>
>                 Key: SPARK-32728
>                 URL: https://issues.apache.org/jira/browse/SPARK-32728
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.4.5, 3.0.0
>         Environment: I tested it with Azure Databricks 7.2 (& 6.6) (includes Apache Spark 3.0.0 (& 2.4.5), Scala 2.12 (& 2.11))
> Worker type: Standard_DS3_v2 (2 workers)
>  
>            Reporter: Joachim Bargsten
>            Priority: Minor
>
> When running following query in a python3 notebook on a cluster with *multiple workers (>1)*, the result is not 0.0, even though I would expect it to be.
> {code:java}
> import pyspark.sql.functions as F
> sdf = spark.range(100)
> sdf = (
>     sdf.withColumn("a", F.col("id") + 1)
>     .withColumn("b", F.col("id") + 2)
>     .withColumn("c", F.col("id") + 3)
>     .withColumn("d", F.col("id") + 4)
>     .withColumn("e", F.col("id") + 5)
> )
> sdf = sdf.groupby(["a", "b", "c", "d"]).agg(F.sum("e").alias("e"))
> sdf = sdf.withColumn("x", F.rand() * F.col("e"))
> sdf2 = sdf.join(sdf.withColumnRenamed("x", "xx"), ["a", "b", "c", "d"])
> sdf2 = sdf2.withColumn("delta_x", F.abs(F.col('x') - F.col("xx"))).agg(F.sum("delta_x"))
> sum_delta_x = sdf2.head()[0]
> print(f"{sum_delta_x} should be 0.0")
> assert abs(sum_delta_x) < 0.001
> {code}
> If the groupby statement is commented out, the code is working as expected.



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