[
https://issues.apache.org/jira/browse/SPARK-41049?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Guy Boo updated SPARK-41049:
----------------------------
Description:
h2. Expectation
For a given row, Nondeterministic expressions are expected to have stable
values.
{code:scala}
import org.apache.spark.sql.functions._
val df = sparkContext.parallelize(1 to 5).toDF("x")
val v1 = rand().*(lit(10000)).cast(IntegerType)
df.select(v1, v1).collect{code}
Returns a set like this:
|8777|8777|
|1357|1357|
|3435|3435|
|9204|9204|
|3870|3870|
where both columns always have the same value, but what that value is changes
from row to row. This is different from the following:
{code:scala}
df.select(rand(), rand()).collect{code}
In this case, because the rand() calls are distinct, the values in both columns
should be different.
h2. Problem
This expectation does not appear to be stable in the event that any subsequent
expression is a CodegenFallback. This program:
{code:scala}
import org.apache.spark.sql._
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._
val sparkSession = SparkSession.builder().getOrCreate()
val df = sparkSession.sparkContext.parallelize(1 to 5).toDF("x")
val v1 = rand().*(lit(10000)).cast(IntegerType)
val v2 = to_csv(struct(v1.as("a"))) // to_csv is CodegenFallback
df.select(v1, v1, v2, v2).collect {code}
produces output like this:
|8159|8159|8159|{color:#ff0000}2028{color}|
|8320|8320|8320|{color:#ff0000}1640{color}|
|7937|7937|7937|{color:#ff0000}769{color}|
|436|436|436|{color:#ff0000}8924{color}|
|8924|8924|2827|{color:#ff0000}2731{color}|
Not sure why the first call via the CodegenFallback path should be correct
while subsequent calls aren't.
h2. Workaround
If the Nondeterministic expression is moved to a separate, earlier select()
call, and the CodegenFallback instead only refers to a column reference, then
the problem seems to go away. But I don't know if this workaround is expected
to be reliable if optimization is ever able to restructure adjacent select()s.
was:
h2. Expectation
For a given row, Nondeterministic expressions are expected to have stable
values.
{code:scala}
import org.apache.spark.sql.functions._
val df = sparkContext.parallelize(1 to 5).toDF("x")
val v1 = rand().*(lit(10000)).cast(IntegerType)
df.select(v1, v1).collect{code}
Returns a set like this:
|8777|8777|
|1357|1357|
|3435|3435|
|9204|9204|
|3870|3870|
where both columns always have the same value, but what that value is changes
from row to row. This is different from the following:
{code:scala}
df.select(rand(), rand()).collect{code}
In this case, because the rand() calls are distinct, the values in both columns
should be different.
h2. Problem
This expectation does not appear to be stable in the event that any subsequent
expression is a CodegenFallback. This program:
{code:scala}
import org.apache.spark.sql._
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._
val sparkSession = SparkSession.builder().getOrCreate()
val df = sparkSession.sparkContext.parallelize(1 to 5).toDF("x")
val v1 = rand().*(lit(10000)).cast(IntegerType)
val v2 = to_csv(struct(v1.as("a"))) // to_csv is CodegenFallback
df.select(v1, v1, v2, v2).collect {code}
produces output like this:
|8159|8159|8159|{color:#ff0000}2028{color}|
|8320|8320|8320|{color:#ff0000}1640{color}|
|7937|7937|7937|{color:#ff0000}769{color}|
|436|436|436|{color:#ff0000}8924{color}|
|8924|8924|2827|{color:#ff0000}2731{color}|
Not sure why the first call via the CodegenFallback path should be correct
while subsequent calls aren't.
> Nondeterministic expressions have unstable values if they are children of
> CodegenFallback expressions
> -----------------------------------------------------------------------------------------------------
>
> Key: SPARK-41049
> URL: https://issues.apache.org/jira/browse/SPARK-41049
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.1.2
> Reporter: Guy Boo
> Priority: Major
>
> h2. Expectation
> For a given row, Nondeterministic expressions are expected to have stable
> values.
> {code:scala}
> import org.apache.spark.sql.functions._
> val df = sparkContext.parallelize(1 to 5).toDF("x")
> val v1 = rand().*(lit(10000)).cast(IntegerType)
> df.select(v1, v1).collect{code}
> Returns a set like this:
> |8777|8777|
> |1357|1357|
> |3435|3435|
> |9204|9204|
> |3870|3870|
> where both columns always have the same value, but what that value is changes
> from row to row. This is different from the following:
> {code:scala}
> df.select(rand(), rand()).collect{code}
> In this case, because the rand() calls are distinct, the values in both
> columns should be different.
> h2. Problem
> This expectation does not appear to be stable in the event that any
> subsequent expression is a CodegenFallback. This program:
> {code:scala}
> import org.apache.spark.sql._
> import org.apache.spark.sql.types._
> import org.apache.spark.sql.functions._
> val sparkSession = SparkSession.builder().getOrCreate()
> val df = sparkSession.sparkContext.parallelize(1 to 5).toDF("x")
> val v1 = rand().*(lit(10000)).cast(IntegerType)
> val v2 = to_csv(struct(v1.as("a"))) // to_csv is CodegenFallback
> df.select(v1, v1, v2, v2).collect {code}
> produces output like this:
> |8159|8159|8159|{color:#ff0000}2028{color}|
> |8320|8320|8320|{color:#ff0000}1640{color}|
> |7937|7937|7937|{color:#ff0000}769{color}|
> |436|436|436|{color:#ff0000}8924{color}|
> |8924|8924|2827|{color:#ff0000}2731{color}|
> Not sure why the first call via the CodegenFallback path should be correct
> while subsequent calls aren't.
> h2. Workaround
> If the Nondeterministic expression is moved to a separate, earlier select()
> call, and the CodegenFallback instead only refers to a column reference, then
> the problem seems to go away. But I don't know if this workaround is expected
> to be reliable if optimization is ever able to restructure adjacent select()s.
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