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…ays are referenced from nested structs
### What changes were proposed in this pull request?
This PR fixes an issue in `arrays_zip` function where a field index was used as a column name in the resulting schema which was a regression from Spark 3.1. With this change, the original behaviour is restored: a corresponding struct field name will be used instead of a field index.
Example:
```sql
with q as (
select
named_struct(
'my_array', array(1, 2, 3),
'my_array2', array(4, 5, 6)
) as my_struct
)
select
arrays_zip(my_struct.my_array, my_struct.my_array2)
from
q
```
would return schema:
```
root
|-- arrays_zip(my_struct.my_array, my_struct.my_array2): array (nullable = false)
| |-- element: struct (containsNull = false)
| | |-- 0: integer (nullable = true)
| | |-- 1: integer (nullable = true)
```
which is somewhat inaccurate. PR adds handling of `GetStructField` expression to return the struct field names like this:
```
root
|-- arrays_zip(my_struct.my_array, my_struct.my_array2): array (nullable = false)
| |-- element: struct (containsNull = false)
| | |-- my_array: integer (nullable = true)
| | |-- my_array2: integer (nullable = true)
```
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
Yes, `arrays_zip` function returns struct field names now as in Spark 3.1 instead of field indices.
Some users might have worked around this issue so this patch would affect them by bringing back the original behaviour.
### How was this patch tested?
Existing unit tests. I also added a test case that reproduces the problem.
Closes #37833 from sadikovi/SPARK-40292.
Authored-by: Ivan Sadikov <[email protected]>
Signed-off-by: Max Gekk <[email protected]>
pull bot
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Feb 24, 2024
…n properly
### What changes were proposed in this pull request?
Make `ResolveRelations` handle plan id properly
### Why are the changes needed?
bug fix for Spark Connect, it won't affect classic Spark SQL
before this PR:
```
from pyspark.sql import functions as sf
spark.range(10).withColumn("value_1", sf.lit(1)).write.saveAsTable("test_table_1")
spark.range(10).withColumnRenamed("id", "index").withColumn("value_2", sf.lit(2)).write.saveAsTable("test_table_2")
df1 = spark.read.table("test_table_1")
df2 = spark.read.table("test_table_2")
df3 = spark.read.table("test_table_1")
join1 = df1.join(df2, on=df1.id==df2.index).select(df2.index, df2.value_2)
join2 = df3.join(join1, how="left", on=join1.index==df3.id)
join2.schema
```
fails with
```
AnalysisException: [CANNOT_RESOLVE_DATAFRAME_COLUMN] Cannot resolve dataframe column "id". It's probably because of illegal references like `df1.select(df2.col("a"))`. SQLSTATE: 42704
```
That is due to existing plan caching in `ResolveRelations` doesn't work with Spark Connect
```
=== Applying Rule org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations ===
'[#12]Join LeftOuter, '`==`('index, 'id) '[#12]Join LeftOuter, '`==`('index, 'id)
!:- '[#9]UnresolvedRelation [test_table_1], [], false :- '[#9]SubqueryAlias spark_catalog.default.test_table_1
!+- '[#11]Project ['index, 'value_2] : +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false
! +- '[#10]Join Inner, '`==`('id, 'index) +- '[#11]Project ['index, 'value_2]
! :- '[#7]UnresolvedRelation [test_table_1], [], false +- '[#10]Join Inner, '`==`('id, 'index)
! +- '[#8]UnresolvedRelation [test_table_2], [], false :- '[#9]SubqueryAlias spark_catalog.default.test_table_1
! : +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false
! +- '[#8]SubqueryAlias spark_catalog.default.test_table_2
! +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_2`, [], false
Can not resolve 'id with plan 7
```
`[#7]UnresolvedRelation [test_table_1], [], false` was wrongly resolved to the cached one
```
:- '[#9]SubqueryAlias spark_catalog.default.test_table_1
+- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false
```
### Does this PR introduce _any_ user-facing change?
yes, bug fix
### How was this patch tested?
added ut
### Was this patch authored or co-authored using generative AI tooling?
ci
Closes apache#45214 from zhengruifeng/connect_fix_read_join.
Authored-by: Ruifeng Zheng <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>
wangyum
pushed a commit
that referenced
this pull request
Jun 26, 2024
…plan properly ### What changes were proposed in this pull request? Make `ResolveRelations` handle plan id properly cherry-pick bugfix apache#45214 to 3.5 ### Why are the changes needed? bug fix for Spark Connect, it won't affect classic Spark SQL before this PR: ``` from pyspark.sql import functions as sf spark.range(10).withColumn("value_1", sf.lit(1)).write.saveAsTable("test_table_1") spark.range(10).withColumnRenamed("id", "index").withColumn("value_2", sf.lit(2)).write.saveAsTable("test_table_2") df1 = spark.read.table("test_table_1") df2 = spark.read.table("test_table_2") df3 = spark.read.table("test_table_1") join1 = df1.join(df2, on=df1.id==df2.index).select(df2.index, df2.value_2) join2 = df3.join(join1, how="left", on=join1.index==df3.id) join2.schema ``` fails with ``` AnalysisException: [CANNOT_RESOLVE_DATAFRAME_COLUMN] Cannot resolve dataframe column "id". It's probably because of illegal references like `df1.select(df2.col("a"))`. SQLSTATE: 42704 ``` That is due to existing plan caching in `ResolveRelations` doesn't work with Spark Connect ``` === Applying Rule org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations === '[#12]Join LeftOuter, '`==`('index, 'id) '[#12]Join LeftOuter, '`==`('index, 'id) !:- '[#9]UnresolvedRelation [test_table_1], [], false :- '[#9]SubqueryAlias spark_catalog.default.test_table_1 !+- '[#11]Project ['index, 'value_2] : +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false ! +- '[#10]Join Inner, '`==`('id, 'index) +- '[#11]Project ['index, 'value_2] ! :- '[#7]UnresolvedRelation [test_table_1], [], false +- '[#10]Join Inner, '`==`('id, 'index) ! +- '[#8]UnresolvedRelation [test_table_2], [], false :- '[#9]SubqueryAlias spark_catalog.default.test_table_1 ! : +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false ! +- '[#8]SubqueryAlias spark_catalog.default.test_table_2 ! +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_2`, [], false Can not resolve 'id with plan 7 ``` `[#7]UnresolvedRelation [test_table_1], [], false` was wrongly resolved to the cached one ``` :- '[#9]SubqueryAlias spark_catalog.default.test_table_1 +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false ``` ### Does this PR introduce _any_ user-facing change? yes, bug fix ### How was this patch tested? added ut ### Was this patch authored or co-authored using generative AI tooling? ci Closes apache#46291 from zhengruifeng/connect_fix_read_join_35. Authored-by: Ruifeng Zheng <[email protected]> Signed-off-by: Ruifeng Zheng <[email protected]>
wangyum
pushed a commit
that referenced
this pull request
Nov 20, 2024
…plan properly ### What changes were proposed in this pull request? Make `ResolveRelations` handle plan id properly cherry-pick bugfix apache#45214 to 3.4 ### Why are the changes needed? bug fix for Spark Connect, it won't affect classic Spark SQL before this PR: ``` from pyspark.sql import functions as sf spark.range(10).withColumn("value_1", sf.lit(1)).write.saveAsTable("test_table_1") spark.range(10).withColumnRenamed("id", "index").withColumn("value_2", sf.lit(2)).write.saveAsTable("test_table_2") df1 = spark.read.table("test_table_1") df2 = spark.read.table("test_table_2") df3 = spark.read.table("test_table_1") join1 = df1.join(df2, on=df1.id==df2.index).select(df2.index, df2.value_2) join2 = df3.join(join1, how="left", on=join1.index==df3.id) join2.schema ``` fails with ``` AnalysisException: [CANNOT_RESOLVE_DATAFRAME_COLUMN] Cannot resolve dataframe column "id". It's probably because of illegal references like `df1.select(df2.col("a"))`. SQLSTATE: 42704 ``` That is due to existing plan caching in `ResolveRelations` doesn't work with Spark Connect ``` === Applying Rule org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations === '[#12]Join LeftOuter, '`==`('index, 'id) '[#12]Join LeftOuter, '`==`('index, 'id) !:- '[#9]UnresolvedRelation [test_table_1], [], false :- '[#9]SubqueryAlias spark_catalog.default.test_table_1 !+- '[#11]Project ['index, 'value_2] : +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false ! +- '[#10]Join Inner, '`==`('id, 'index) +- '[#11]Project ['index, 'value_2] ! :- '[#7]UnresolvedRelation [test_table_1], [], false +- '[#10]Join Inner, '`==`('id, 'index) ! +- '[#8]UnresolvedRelation [test_table_2], [], false :- '[#9]SubqueryAlias spark_catalog.default.test_table_1 ! : +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false ! +- '[#8]SubqueryAlias spark_catalog.default.test_table_2 ! +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_2`, [], false Can not resolve 'id with plan 7 ``` `[#7]UnresolvedRelation [test_table_1], [], false` was wrongly resolved to the cached one ``` :- '[#9]SubqueryAlias spark_catalog.default.test_table_1 +- 'UnresolvedCatalogRelation `spark_catalog`.`default`.`test_table_1`, [], false ``` ### Does this PR introduce _any_ user-facing change? yes, bug fix ### How was this patch tested? added ut ### Was this patch authored or co-authored using generative AI tooling? ci Closes apache#46290 from zhengruifeng/connect_fix_read_join_34. Authored-by: Ruifeng Zheng <[email protected]> Signed-off-by: Ruifeng Zheng <[email protected]>
pull bot
pushed a commit
that referenced
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Aug 19, 2025
…onicalized expressions
### What changes were proposed in this pull request?
Make PullOutNonDeterministic use canonicalized expressions to dedup group and aggregate expressions. This affects pyspark udfs in particular. Example:
```
from pyspark.sql.functions import col, avg, udf
pythonUDF = udf(lambda x: x).asNondeterministic()
spark.range(10)\
.selectExpr("id", "id % 3 as value")\
.groupBy(pythonUDF(col("value")))\
.agg(avg("id"), pythonUDF(col("value")))\
.explain(extended=True)
```
Currently results in a plan like this:
```
Aggregate [_nondeterministic#15](#15), [_nondeterministic#15 AS dummyNondeterministicUDF(value)#12, avg(id#0L) AS avg(id)#13, dummyNondeterministicUDF(value#6L)#8 AS dummyNondeterministicUDF(value)#14](#15%20AS%20dummyNondeterministicUDF(value)#12,%20avg(id#0L)%20AS%20avg(id)#13,%20dummyNondeterministicUDF(value#6L)#8%20AS%20dummyNondeterministicUDF(value)#14)
+- Project [id#0L, value#6L, dummyNondeterministicUDF(value#6L)#7 AS _nondeterministic#15](#0L,%20value#6L,%20dummyNondeterministicUDF(value#6L)#7%20AS%20_nondeterministic#15)
+- Project [id#0L, (id#0L % cast(3 as bigint)) AS value#6L](#0L,%20(id#0L%20%%20cast(3%20as%20bigint))%20AS%20value#6L)
+- Range (0, 10, step=1, splits=Some(2))
```
and then it throws:
```
[[MISSING_AGGREGATION] The non-aggregating expression "value" is based on columns which are not participating in the GROUP BY clause. Add the columns or the expression to the GROUP BY, aggregate the expression, or use "any_value(value)" if you do not care which of the values within a group is returned. SQLSTATE: 42803
```
- how canonicalized fixes this:
- nondeterministic PythonUDF expressions always have distinct resultIds per udf
- The fix is to canonicalize the expressions when matching. Canonicalized means that we're setting the resultIds to -1, allowing us to dedup the PythonUDF expressions.
- for deterministic UDFs, this rule does not apply and "Post Analysis" batch extracts and deduplicates the expressions, as expected
### Why are the changes needed?
- the output of the query with the fix applied still makes sense - the nondeterministic UDF is invoked only once, in the project.
### Does this PR introduce _any_ user-facing change?
Yes, it's additive, it enables queries to run that previously threw errors.
### How was this patch tested?
- added unit test
### Was this patch authored or co-authored using generative AI tooling?
No
Closes apache#52061 from benrobby/adhoc-fix-pull-out-nondeterministic.
Authored-by: Ben Hurdelhey <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
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