Joachim Bargsten created SPARK-32728:
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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.
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