Details
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Bug
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Status: Resolved
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Major
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Resolution: Incomplete
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2.3.2, 2.4.0
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None
Description
The following code gives unexpected output due to the filter not getting pushed down in catalyst optimizer.
df = spark.createDataFrame([['a','123','12.3','n'],['a','123','12.3','y'],['a','123','12.4','y']],['x','y','z','y_n']) df.show(5) df.filter("y_n='y'").select('x','y','z').distinct().show() df.select('x','y','z').distinct().filter("y_n='y'").show()
| x | y | z | y_n |
| a | 123 | 12.3 | n |
| a | 123 | 12.3 | y |
| a | 123 | 12.4 | y |
| x | y | z |
| a | 123 | 12.3 |
| a | 123 | 12.4 |
| x | y | z |
| a | 123 | 12.4 |
Ideally, the second statement should result in an error since the column used in the filter is not present in the preceding select statement. But the catalyst optimizer is using first() on column y_n and then applying the filter.
Even if the filter was pushed down, the result would have been accurate.
df = spark.createDataFrame([['a','123','12.3','n'],['a','123','12.3','y'],['a','123','12.4','y']],['x','y','z','y_n']) df.filter("y_n='y'").select('x','y','z').distinct().explain(True) df.select('x','y','z').distinct().filter("y_n='y'").explain(True)
== Parsed Logical Plan ==
Deduplicate x#74, y#75, z#76
+- AnalysisBarrier
+- Project x#74, y#75, z#76
+- Filter (y_n#77 = y)
+- LogicalRDD x#74, y#75, z#76, y_n#77, false
== Analyzed Logical Plan ==
x: string, y: string, z: string
Deduplicate x#74, y#75, z#76
+- Project x#74, y#75, z#76
+- Filter (y_n#77 = y)
+- LogicalRDD x#74, y#75, z#76, y_n#77, false
== Optimized Logical Plan ==
Aggregate x#74, y#75, z#76, x#74, y#75, z#76
+- Project x#74, y#75, z#76
+- Filter (isnotnull(y_n#77) && (y_n#77 = y))
+- LogicalRDD x#74, y#75, z#76, y_n#77, false
== Physical Plan ==
*(2) HashAggregate(keys=x#74, y#75, z#76, functions=[], output=x#74, y#75, z#76)
+- Exchange hashpartitioning(x#74, y#75, z#76, 10)
+- *(1) HashAggregate(keys=x#74, y#75, z#76, functions=[], output=x#74, y#75, z#76)
+- *(1) Project x#74, y#75, z#76
+- *(1) Filter (isnotnull(y_n#77) && (y_n#77 = y))
+- Scan ExistingRDDx#74,y#75,z#76,y_n#77
-------------------------------------------------------------------------------------------------------------------
== Parsed Logical Plan ==
'Filter ('y_n = y)
+- AnalysisBarrier
+- Deduplicate x#74, y#75, z#76
+- Project x#74, y#75, z#76
+- LogicalRDD x#74, y#75, z#76, y_n#77, false
== Analyzed Logical Plan ==
x: string, y: string, z: string
Project x#74, y#75, z#76
+- Filter (y_n#77 = y)
+- Deduplicate x#74, y#75, z#76
+- Project x#74, y#75, z#76, y_n#77
+- LogicalRDD x#74, y#75, z#76, y_n#77, false
== Optimized Logical Plan ==
Project x#74, y#75, z#76
+- Filter (isnotnull(y_n#77) && (y_n#77 = y))
+- Aggregate x#74, y#75, z#76, x#74, y#75, z#76, first(y_n#77, false) AS y_n#77
+- LogicalRDD x#74, y#75, z#76, y_n#77, false
== Physical Plan ==
*(3) Project x#74, y#75, z#76
+- *(3) Filter (isnotnull(y_n#77) && (y_n#77 = y))
+- SortAggregate(key=x#74, y#75, z#76, functions=first(y_n#77, false), output=x#74, y#75, z#76, y_n#77)
+- *(2) Sort x#74 ASC NULLS FIRST, y#75 ASC NULLS FIRST, z#76 ASC NULLS FIRST, false, 0
+- Exchange hashpartitioning(x#74, y#75, z#76, 10)
+- SortAggregate(key=x#74, y#75, z#76, functions=partial_first(y_n#77, false), output=x#74, y#75, z#76, first#95, valueSet#96)
+- *(1) Sort x#74 ASC NULLS FIRST, y#75 ASC NULLS FIRST, z#76 ASC NULLS FIRST, false, 0
+- Scan ExistingRDDx#74,y#75,z#76,y_n#77
The second query. ie "df.select('x','y','z').distinct().filter("y_n='y'").explain(True)" should result in error rather than giving wrong output.
Attachments
Issue Links
- relates to
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- Resolved
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SPARK-25420 Dataset.count() every time is different.
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- Resolved
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