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Posted to issues@spark.apache.org by "Guy Boo (Jira)" <ji...@apache.org> on 2022/11/08 11:31:00 UTC
[jira] [Updated] (SPARK-41049) Nondeterministic expressions have unstable values if they are children of CodegenFallback expressions
[ 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.
was:
h2. Expectation
For a given row, Nondeterministic expressions should 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))
df.select(v1, v1).collect{code}
Should return a set where both columns always have the same value, but what that value is changes from row to row. This is true for composed expressions as well:
{code:scala}
df.select(v1.cast(IntegerType), v1.cast(IntegerType)).collect
{code}
should still have the same value in both columns. This is different from the following:
{code:scala}
df.select(rand(), rand()).collect{code}
Should always have different values in each column, because the two rand() calls refer to different invocations.
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.
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