refactor for SPARK-34079-multi-column-scalar-subquery #4
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peter-toth
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Jan 17, 2023
### What changes were proposed in this pull request? This PR introduces sasl retry count in RetryingBlockTransferor. ### Why are the changes needed? Previously a boolean variable, saslTimeoutSeen, was used. However, the boolean variable wouldn't cover the following scenario: 1. SaslTimeoutException 2. IOException 3. SaslTimeoutException 4. IOException Even though IOException at #2 is retried (resulting in increment of retryCount), the retryCount would be cleared at step #4. Since the intention of saslTimeoutSeen is to undo the increment due to retrying SaslTimeoutException, we should keep a counter for SaslTimeoutException retries and subtract the value of this counter from retryCount. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? New test is added, courtesy of Mridul. Closes apache#39611 from tedyu/sasl-cnt. Authored-by: Ted Yu <[email protected]> Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
peter-toth
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Apr 11, 2023
…edExpression() ### What changes were proposed in this pull request? In `EquivalentExpressions.addExpr()`, add a guard `supportedExpression()` to make it consistent with `addExprTree()` and `getExprState()`. ### Why are the changes needed? This fixes a regression caused by apache#39010 which added the `supportedExpression()` to `addExprTree()` and `getExprState()` but not `addExpr()`. One example of a use case affected by the inconsistency is the `PhysicalAggregation` pattern in physical planning. There, it calls `addExpr()` to deduplicate the aggregate expressions, and then calls `getExprState()` to deduplicate the result expressions. Guarding inconsistently will cause the aggregate and result expressions go out of sync, eventually resulting in query execution error (or whole-stage codegen error). ### Does this PR introduce _any_ user-facing change? This fixes a regression affecting Spark 3.3.2+, where it may manifest as an error running aggregate operators with higher-order functions. Example running the SQL command: ```sql select max(transform(array(id), x -> x)), max(transform(array(id), x -> x)) from range(2) ``` example error message before the fix: ``` java.lang.IllegalStateException: Couldn't find max(transform(array(id#0L), lambdafunction(lambda x#2L, lambda x#2L, false)))#4 in [max(transform(array(id#0L), lambdafunction(lambda x#1L, lambda x#1L, false)))#3] ``` after the fix this error is gone. ### How was this patch tested? Added new test cases to `SubexpressionEliminationSuite` for the immediate issue, and to `DataFrameAggregateSuite` for an example of user-visible symptom. Closes apache#40473 from rednaxelafx/spark-42851. Authored-by: Kris Mok <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
peter-toth
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Aug 22, 2023
…edExpression() ### What changes were proposed in this pull request? In `EquivalentExpressions.addExpr()`, add a guard `supportedExpression()` to make it consistent with `addExprTree()` and `getExprState()`. ### Why are the changes needed? This fixes a regression caused by apache#39010 which added the `supportedExpression()` to `addExprTree()` and `getExprState()` but not `addExpr()`. One example of a use case affected by the inconsistency is the `PhysicalAggregation` pattern in physical planning. There, it calls `addExpr()` to deduplicate the aggregate expressions, and then calls `getExprState()` to deduplicate the result expressions. Guarding inconsistently will cause the aggregate and result expressions go out of sync, eventually resulting in query execution error (or whole-stage codegen error). ### Does this PR introduce _any_ user-facing change? This fixes a regression affecting Spark 3.3.2+, where it may manifest as an error running aggregate operators with higher-order functions. Example running the SQL command: ```sql select max(transform(array(id), x -> x)), max(transform(array(id), x -> x)) from range(2) ``` example error message before the fix: ``` java.lang.IllegalStateException: Couldn't find max(transform(array(id#0L), lambdafunction(lambda x#2L, lambda x#2L, false)))#4 in [max(transform(array(id#0L), lambdafunction(lambda x#1L, lambda x#1L, false)))#3] ``` after the fix this error is gone. ### How was this patch tested? Added new test cases to `SubexpressionEliminationSuite` for the immediate issue, and to `DataFrameAggregateSuite` for an example of user-visible symptom. Closes apache#40473 from rednaxelafx/spark-42851. Authored-by: Kris Mok <[email protected]> Signed-off-by: Wenchen Fan <[email protected]> (cherry picked from commit ef0a76e) Signed-off-by: Wenchen Fan <[email protected]>
peter-toth
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Aug 22, 2023
### What changes were proposed in this pull request? This PR introduces sasl retry count in RetryingBlockTransferor. ### Why are the changes needed? Previously a boolean variable, saslTimeoutSeen, was used. However, the boolean variable wouldn't cover the following scenario: 1. SaslTimeoutException 2. IOException 3. SaslTimeoutException 4. IOException Even though IOException at #2 is retried (resulting in increment of retryCount), the retryCount would be cleared at step #4. Since the intention of saslTimeoutSeen is to undo the increment due to retrying SaslTimeoutException, we should keep a counter for SaslTimeoutException retries and subtract the value of this counter from retryCount. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? New test is added, courtesy of Mridul. Closes apache#39611 from tedyu/sasl-cnt. Authored-by: Ted Yu <yuzhihonggmail.com> Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com> Closes apache#39709 from akpatnam25/SPARK-42090-backport-3.3. Authored-by: Ted Yu <[email protected]> Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
peter-toth
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Apr 23, 2024
### What changes were proposed in this pull request? In the `Window` node, both `partitionSpec` and `orderSpec` must be orderable, but the current type check only verifies `orderSpec` is orderable. This can cause an error in later optimizing phases. Given a query: ``` with t as (select id, map(id, id) as m from range(0, 10)) select rank() over (partition by m order by id) from t ``` Before the PR, it fails with an `INTERNAL_ERROR`: ``` org.apache.spark.SparkException: [INTERNAL_ERROR] grouping/join/window partition keys cannot be map type. SQLSTATE: XX000 at org.apache.spark.SparkException$.internalError(SparkException.scala:92) at org.apache.spark.SparkException$.internalError(SparkException.scala:96) at org.apache.spark.sql.catalyst.optimizer.NormalizeFloatingNumbers$.needNormalize(NormalizeFloatingNumbers.scala:103) at org.apache.spark.sql.catalyst.optimizer.NormalizeFloatingNumbers$.org$apache$spark$sql$catalyst$optimizer$NormalizeFloatingNumbers$$needNormalize(NormalizeFloatingNumbers.scala:94) ... ``` After the PR, it fails with a `EXPRESSION_TYPE_IS_NOT_ORDERABLE`, which is expected: ``` org.apache.spark.sql.catalyst.ExtendedAnalysisException: [EXPRESSION_TYPE_IS_NOT_ORDERABLE] Column expression "m" cannot be sorted because its type "MAP<BIGINT, BIGINT>" is not orderable. SQLSTATE: 42822; line 2 pos 53; Project [RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4] +- Project [id#1L, m#0, RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4, RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4] +- Window [rank(id#1L) windowspecdefinition(m#0, id#1L ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS RANK() OVER (PARTITION BY m ORDER BY id ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)#4], [m#0], [id#1L ASC NULLS FIRST] +- Project [id#1L, m#0] +- SubqueryAlias t +- SubqueryAlias t +- Project [id#1L, map(id#1L, id#1L) AS m#0] +- Range (0, 10, step=1, splits=None) at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:52) ... ``` ### How was this patch tested? Unit test. Closes apache#45730 from chenhao-db/SPARK-47572. Authored-by: Chenhao Li <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
peter-toth
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Jun 28, 2024
… throw internal error
### What changes were proposed in this pull request?
This PR fixes the error messages and classes when Python UDFs are used in higher order functions.
### Why are the changes needed?
To show the proper user-facing exceptions with error classes.
### Does this PR introduce _any_ user-facing change?
Yes, previously it threw internal error such as:
```python
from pyspark.sql.functions import transform, udf, col, array
spark.range(1).select(transform(array("id"), lambda x: udf(lambda y: y)(x))).collect()
```
Before:
```
py4j.protocol.Py4JJavaError: An error occurred while calling o74.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 15 in stage 0.0 failed 1 times, most recent failure: Lost task 15.0 in stage 0.0 (TID 15) (ip-192-168-123-103.ap-northeast-2.compute.internal executor driver): org.apache.spark.SparkException: [INTERNAL_ERROR] Cannot evaluate expression: <lambda>(lambda x_0#3L)#2 SQLSTATE: XX000
at org.apache.spark.SparkException$.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 <[email protected]>
Signed-off-by: Kent Yao <[email protected]>
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