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

["Joachim Bargsten (Jira)" <ji...@apache.org>]

Joachim Bargsten created SPARK-32728:
----------------------------------------

             Summary: 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: 3.0.0, 2.4.5
         Environment: I tested it with  environment,Azure Databricks 7.2 (includes Apache Spark 3.0.0, Scala 2.12)

Worker type: Standard_DS3_v2 (2 workers)

 
            Reporter: Joachim Bargsten


When running following query 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.



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org