Supporting TTL with Multiple State Variables, using ForkJoinPool instead of single thread#2
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… throw internal error
### What changes were proposed in this pull request?
This PR fixes the error messages and classes when Python UDFs are used in higher order functions.
### Why are the changes needed?
To show the proper user-facing exceptions with error classes.
### Does this PR introduce _any_ user-facing change?
Yes, previously it threw internal error such as:
```python
from pyspark.sql.functions import transform, udf, col, array
spark.range(1).select(transform(array("id"), lambda x: udf(lambda y: y)(x))).collect()
```
Before:
```
py4j.protocol.Py4JJavaError: An error occurred while calling o74.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 15 in stage 0.0 failed 1 times, most recent failure: Lost task 15.0 in stage 0.0 (TID 15) (ip-192-168-123-103.ap-northeast-2.compute.internal executor driver): org.apache.spark.SparkException: [INTERNAL_ERROR] Cannot evaluate expression: <lambda>(lambda x_0#3L)#2 SQLSTATE: XX000
at org.apache.spark.SparkException$.internalError(SparkException.scala:92)
at org.apache.spark.SparkException$.internalError(SparkException.scala:96)
```
After:
```
pyspark.errors.exceptions.captured.AnalysisException: [INVALID_LAMBDA_FUNCTION_CALL.UNEVALUABLE] Invalid lambda function call. Python UDFs should be used in a lambda function at a higher order function. However, "<lambda>(lambda x_0#3L)" was a Python UDF. SQLSTATE: 42K0D;
Project [transform(array(id#0L), lambdafunction(<lambda>(lambda x_0#3L)#2, lambda x_0#3L, false)) AS transform(array(id), lambdafunction(<lambda>(lambda x_0#3L), namedlambdavariable()))#4]
+- Range (0, 1, step=1, splits=Some(16))
```
### How was this patch tested?
Unittest was added
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes apache#47079 from HyukjinKwon/SPARK-48706.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Kent Yao <yao@apache.org>
ericm-db
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Dec 12, 2024
### What changes were proposed in this pull request? Fix self-join after `applyInArrow`, the same issue of `applyInPandas` was fixed in apache#31429 ### Why are the changes needed? bug fix before: ``` In [1]: import pyarrow as pa In [2]: df = spark.createDataFrame([(1, 1)], ("k", "v")) In [3]: def arrow_func(key, table): ...: return pa.Table.from_pydict({"x": [2], "y": [2]}) ...: In [4]: df2 = df.groupby("k").applyInArrow(arrow_func, schema="x long, y long") In [5]: df2.show() 24/12/04 17:47:43 WARN CheckAllocator: More than one DefaultAllocationManager on classpath. Choosing first found +---+---+ | x| y| +---+---+ | 2| 2| +---+---+ In [6]: df2.join(df2) ... Failure when resolving conflicting references in Join: 'Join Inner :- FlatMapGroupsInArrow [k#0L], arrow_func(k#0L, v#1L)#2, [x#3L, y#4L] : +- Project [k#0L, k#0L, v#1L] : +- LogicalRDD [k#0L, v#1L], false +- FlatMapGroupsInArrow [k#12L], arrow_func(k#12L, v#13L)#2, [x#3L, y#4L] +- Project [k#12L, k#12L, v#13L] +- LogicalRDD [k#12L, v#13L], false Conflicting attributes: "x", "y". SQLSTATE: XX000 at org.apache.spark.SparkException$.internalError(SparkException.scala:92) at org.apache.spark.SparkException$.internalError(SparkException.scala:79) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.$anonfun$checkAnalysis0$2(CheckAnalysis.scala:798) ``` after: ``` In [6]: df2.join(df2) Out[6]: DataFrame[x: bigint, y: bigint, x: bigint, y: bigint] In [7]: df2.join(df2).show() +---+---+---+---+ | x| y| x| y| +---+---+---+---+ | 2| 2| 2| 2| +---+---+---+---+ ``` ### Does this PR introduce _any_ user-facing change? bug fix ### How was this patch tested? added tests ### Was this patch authored or co-authored using generative AI tooling? no Closes apache#49056 from zhengruifeng/fix_arrow_join. Authored-by: Ruifeng Zheng <ruifengz@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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What changes were proposed in this pull request?
Why are the changes needed?
Does this PR introduce any user-facing change?
How was this patch tested?
Was this patch authored or co-authored using generative AI tooling?