[SPARK-44695][PYTHON] Improve error message for DataFrame.toDF#42369
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itholic wants to merge 5 commits intoapache:masterfrom
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[SPARK-44695][PYTHON] Improve error message for DataFrame.toDF#42369itholic wants to merge 5 commits intoapache:masterfrom
DataFrame.toDF#42369itholic wants to merge 5 commits intoapache:masterfrom
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HyukjinKwon
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Aug 8, 2023
HyukjinKwon
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Aug 8, 2023
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Merged to master and branch-3.5. |
HyukjinKwon
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Aug 9, 2023
### What changes were proposed in this pull request?
This PR proposes to improve error message for `DataFrame.toDF`
### Why are the changes needed?
The current error message is not helpful to solve the problem.
### Does this PR introduce _any_ user-facing change?
Displaying more clear error message than before.
**Before**
```python
>>> df = spark.createDataFrame([("John", 30), ("Alice", 25), ("Bob", 28)])
>>> cols = ['A', None]
>>> df.toDF(*cols)
Traceback (most recent call last):
...
py4j.protocol.Py4JJavaError: An error occurred while calling o54.toDF.
: org.apache.spark.SparkException: [INTERNAL_ERROR] The Spark SQL phase analysis failed with an internal error. You hit a bug in Spark or the Spark plugins you use. Please, report this bug to the corresponding communities or vendors, and provide the full stack trace.
at org.apache.spark.SparkException$.internalError(SparkException.scala:98)
at org.apache.spark.sql.execution.QueryExecution$.toInternalError(QueryExecution.scala:519)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:531)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:202)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:858)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:201)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:76)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:74)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:66)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:92)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:858)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:90)
at org.apache.spark.sql.Dataset.withPlan(Dataset.scala:4318)
at org.apache.spark.sql.Dataset.select(Dataset.scala:1541)
at org.apache.spark.sql.Dataset.toDF(Dataset.scala:539)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.catalyst.analysis.ColumnResolutionHelper.$anonfun$resolveLateralColumnAlias$2(ColumnResolutionHelper.scala:308)
at scala.collection.immutable.List.map(List.scala:297)
at org.apache.spark.sql.catalyst.analysis.ColumnResolutionHelper.resolveLateralColumnAlias(ColumnResolutionHelper.scala:305)
at org.apache.spark.sql.catalyst.analysis.ColumnResolutionHelper.resolveLateralColumnAlias$(ColumnResolutionHelper.scala:260)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.resolveLateralColumnAlias(Analyzer.scala:1462)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$16.applyOrElse(Analyzer.scala:1602)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$16.applyOrElse(Analyzer.scala:1487)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$3(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:76)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:32)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUp(AnalysisHelper.scala:111)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUp$(AnalysisHelper.scala:110)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUp(LogicalPlan.scala:32)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:1487)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:1462)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:222)
at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
at scala.collection.immutable.List.foldLeft(List.scala:91)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:219)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:211)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:211)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:228)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:224)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:173)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:224)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:188)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:209)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:208)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:76)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:202)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:529)
... 24 more
```
**After**
```python
>>> df = spark.createDataFrame([("John", 30), ("Alice", 25), ("Bob", 28)])
>>> cols = ['A', None]
>>> df.toDF(*cols)
Traceback (most recent call last):
...
raise PySparkTypeError(
pyspark.errors.exceptions.base.PySparkTypeError: [NOT_LIST_OF_STR] Argument `cols` should be a list[str], got NoneType.
```
### How was this patch tested?
Add UT.
Closes #42369 from itholic/improve_error_toDF.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit 66d8e6a)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
valentinp17
pushed a commit
to valentinp17/spark
that referenced
this pull request
Aug 24, 2023
### What changes were proposed in this pull request?
This PR proposes to improve error message for `DataFrame.toDF`
### Why are the changes needed?
The current error message is not helpful to solve the problem.
### Does this PR introduce _any_ user-facing change?
Displaying more clear error message than before.
**Before**
```python
>>> df = spark.createDataFrame([("John", 30), ("Alice", 25), ("Bob", 28)])
>>> cols = ['A', None]
>>> df.toDF(*cols)
Traceback (most recent call last):
...
py4j.protocol.Py4JJavaError: An error occurred while calling o54.toDF.
: org.apache.spark.SparkException: [INTERNAL_ERROR] The Spark SQL phase analysis failed with an internal error. You hit a bug in Spark or the Spark plugins you use. Please, report this bug to the corresponding communities or vendors, and provide the full stack trace.
at org.apache.spark.SparkException$.internalError(SparkException.scala:98)
at org.apache.spark.sql.execution.QueryExecution$.toInternalError(QueryExecution.scala:519)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:531)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:202)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:858)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:201)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:76)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:74)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:66)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:92)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:858)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:90)
at org.apache.spark.sql.Dataset.withPlan(Dataset.scala:4318)
at org.apache.spark.sql.Dataset.select(Dataset.scala:1541)
at org.apache.spark.sql.Dataset.toDF(Dataset.scala:539)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.catalyst.analysis.ColumnResolutionHelper.$anonfun$resolveLateralColumnAlias$2(ColumnResolutionHelper.scala:308)
at scala.collection.immutable.List.map(List.scala:297)
at org.apache.spark.sql.catalyst.analysis.ColumnResolutionHelper.resolveLateralColumnAlias(ColumnResolutionHelper.scala:305)
at org.apache.spark.sql.catalyst.analysis.ColumnResolutionHelper.resolveLateralColumnAlias$(ColumnResolutionHelper.scala:260)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.resolveLateralColumnAlias(Analyzer.scala:1462)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$16.applyOrElse(Analyzer.scala:1602)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$16.applyOrElse(Analyzer.scala:1487)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$3(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:76)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:32)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUp(AnalysisHelper.scala:111)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUp$(AnalysisHelper.scala:110)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUp(LogicalPlan.scala:32)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:1487)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:1462)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:222)
at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
at scala.collection.immutable.List.foldLeft(List.scala:91)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:219)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:211)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:211)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:228)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:224)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:173)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:224)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:188)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:209)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:208)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:76)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:202)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:529)
... 24 more
```
**After**
```python
>>> df = spark.createDataFrame([("John", 30), ("Alice", 25), ("Bob", 28)])
>>> cols = ['A', None]
>>> df.toDF(*cols)
Traceback (most recent call last):
...
raise PySparkTypeError(
pyspark.errors.exceptions.base.PySparkTypeError: [NOT_LIST_OF_STR] Argument `cols` should be a list[str], got NoneType.
```
### How was this patch tested?
Add UT.
Closes apache#42369 from itholic/improve_error_toDF.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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What changes were proposed in this pull request?
This PR proposes to improve error message for
DataFrame.toDFWhy are the changes needed?
The current error message is not helpful to solve the problem.
Does this PR introduce any user-facing change?
Displaying more clear error message than before.
Before
After
How was this patch tested?
Add UT.