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https://issues.apache.org/jira/browse/SPARK-40181?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-40181:
---------------------------------
Component/s: SQL
> DataFrame.intersect and .intersectAll are inconsistently dropping rows
> ----------------------------------------------------------------------
>
> Key: SPARK-40181
> URL: https://issues.apache.org/jira/browse/SPARK-40181
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 3.0.1
> Reporter: Luke
> Priority: Major
>
> I don't have a minimal reproducible example for this, but the place where it
> shows up in our workflow is very simple.
> The data in "COLUMN" are a few hundred million distinct strings (gets
> deduplicated in the plan also) and it is being compared against itself using
> intersect.
> The code that is failing is essentially:
> {quote}values = [...] # python list containing many unique strings, none of
> which are None
> df = spark.createDataFrame(
> spark.sparkContext.parallelize(
> [(value,) for value in values], numSlices=2 + len(values) // 10000
> ),
> schema=StructType([StructField("COLUMN", StringType())]),
> )
> df = df.distinct()
> assert df.count() == df.intersect(df).count()
> assert df.count() == df.intersectAll(df).count()
> {quote}
> The issue is that both of the above asserts sometimes pass, and sometimes
> fail (technically we haven't seen intersectAll pass yet, but we have only
> tried a few times). One thing which is striking is that if you call
> df.intersect(df).count() multiple times, the returned count is not always the
> same. Sometimes it is exactly df.count(), sometimes it is ~1% lower, but how
> much lower exactly seems random.
> In particular, we have called df.intersect(df).count() twice in a row, and
> got two different counts, which is very surprising given that df should be
> deterministic, and suggests maybe there is some kind of
> concurrency/inconsistent hashing issue?
> One other thing which is possibly noteworthy is that using df.join(df,
> df.columns, how="inner") does seem to reliably have the desired behavior (not
> dropping any rows).
> Here is the resulting plan from df.intersect(df)
> {quote}== Parsed Logical Plan ==
> 'Intersect false
> :- Deduplicate [COLUMN#144487]
> : +- LogicalRDD [COLUMN#144487], false
> +- Deduplicate [COLUMN#144487]
> +- LogicalRDD [COLUMN#144487], false
> == Analyzed Logical Plan ==
> COLUMN: string
> Intersect false
> :- Deduplicate [COLUMN#144487]
> : +- LogicalRDD [COLUMN#144487], false
> +- Deduplicate [COLUMN#144523]
> +- LogicalRDD [COLUMN#144523], false
> == Optimized Logical Plan ==
> Aggregate [COLUMN#144487], [COLUMN#144487]
> +- Join LeftSemi, (COLUMN#144487 <=> COLUMN#144523)
> :- LogicalRDD [COLUMN#144487], false
> +- Aggregate [COLUMN#144523], [COLUMN#144523]
> +- LogicalRDD [COLUMN#144523], false
> == Physical Plan ==
> *(7) HashAggregate(keys=[COLUMN#144487], functions=[], output=[COLUMN#144487])
> +- Exchange hashpartitioning(COLUMN#144487, 200), true, [id=#22790]
> +- *(6) HashAggregate(keys=[COLUMN#144487], functions=[],
> output=[COLUMN#144487])
> +- *(6) SortMergeJoin [coalesce(COLUMN#144487, ),
> isnull(COLUMN#144487)], [coalesce(COLUMN#144523, ), isnull(COLUMN#144523)],
> LeftSemi
> :- *(2) Sort [coalesce(COLUMN#144487, ) ASC NULLS FIRST,
> isnull(COLUMN#144487) ASC NULLS FIRST], false, 0
> : +- Exchange hashpartitioning(coalesce(COLUMN#144487, ),
> isnull(COLUMN#144487), 200), true, [id=#22772]
> : +- *(1) Scan ExistingRDD[COLUMN#144487]
> +- *(5) Sort [coalesce(COLUMN#144523, ) ASC NULLS FIRST,
> isnull(COLUMN#144523) ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(coalesce(COLUMN#144523, ),
> isnull(COLUMN#144523), 200), true, [id=#22782]
> +- *(4) HashAggregate(keys=[COLUMN#144523], functions=[],
> output=[COLUMN#144523])
> +- Exchange hashpartitioning(COLUMN#144523, 200), true,
> [id=#22778]
> +- *(3) HashAggregate(keys=[COLUMN#144523],
> functions=[], output=[COLUMN#144523])
> +- *(3) Scan ExistingRDD[COLUMN#144523]
> {quote}
> and for df.intersectAll(df)
> {quote}== Parsed Logical Plan ==
> 'IntersectAll true
> :- Deduplicate [COLUMN#144487]
> : +- LogicalRDD [COLUMN#144487], false
> +- Deduplicate [COLUMN#144487]
> +- LogicalRDD [COLUMN#144487], false
> == Analyzed Logical Plan ==
> COLUMN: string
> IntersectAll true
> :- Deduplicate [COLUMN#144487]
> : +- LogicalRDD [COLUMN#144487], false
> +- Deduplicate [COLUMN#144533]
> +- LogicalRDD [COLUMN#144533], false
> == Optimized Logical Plan ==
> Project [COLUMN#144487]
> +- Generate replicaterows(min_count#144566L, COLUMN#144487), [1], false,
> [COLUMN#144487]
> +- Project [COLUMN#144487, if ((vcol1_count#144563L >
> vcol2_count#144565L)) vcol2_count#144565L else vcol1_count#144563L AS
> min_count#144566L]
> +- Filter ((vcol1_count#144563L >= 1) AND (vcol2_count#144565L >= 1))
> +- Aggregate [COLUMN#144487], [count(vcol1#144558) AS
> vcol1_count#144563L, count(vcol2#144561) AS vcol2_count#144565L,
> COLUMN#144487]
> +- Union
> :- Aggregate [COLUMN#144487], [true AS vcol1#144558, null AS
> vcol2#144561, COLUMN#144487]
> : +- LogicalRDD [COLUMN#144487], false
> +- Aggregate [COLUMN#144533], [null AS vcol1#144559, true AS
> vcol2#144560, COLUMN#144533]
> +- LogicalRDD [COLUMN#144533], false
> == Physical Plan ==
> *(7) Project [COLUMN#144487]
> +- Generate replicaterows(min_count#144566L, COLUMN#144487), [COLUMN#144487],
> false, [COLUMN#144487]
> +- *(6) Project [COLUMN#144487, if ((vcol1_count#144563L >
> vcol2_count#144565L)) vcol2_count#144565L else vcol1_count#144563L AS
> min_count#144566L]
> +- *(6) Filter ((vcol1_count#144563L >= 1) AND (vcol2_count#144565L >=
> 1))
> +- *(6) HashAggregate(keys=[COLUMN#144487],
> functions=[count(vcol1#144558), count(vcol2#144561)],
> output=[vcol1_count#144563L, vcol2_count#144565L, COLUMN#144487])
> +- Exchange hashpartitioning(COLUMN#144487, 200), true,
> [id=#23310]
> +- *(5) HashAggregate(keys=[COLUMN#144487],
> functions=[partial_count(vcol1#144558), partial_count(vcol2#144561)],
> output=[COLUMN#144487, count#144569L, count#144570L])
> +- Union
> :- *(2) HashAggregate(keys=[COLUMN#144487],
> functions=[], output=[vcol1#144558, vcol2#144561, COLUMN#144487])
> : +- Exchange hashpartitioning(COLUMN#144487, 200),
> true, [id=#23267]
> : +- *(1) HashAggregate(keys=[COLUMN#144487],
> functions=[], output=[COLUMN#144487])
> : +- *(1) Scan ExistingRDD[COLUMN#144487]
> +- *(4) HashAggregate(keys=[COLUMN#144533],
> functions=[], output=[vcol1#144559, vcol2#144560, COLUMN#144533])
> +- ReusedExchange [COLUMN#144533], Exchange
> hashpartitioning(COLUMN#144487, 200), true, [id=#23267]
> {quote}
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