[BUG] test_cast_neg_to_decimal_err failing in databricks

[abellina]

This test failed in my PR when I triggered the databricks CI: #5531

10:54:26  _________________________ test_cast_neg_to_decimal_err _________________________
10:54:26  [gw0] linux -- Python 3.8.13 /databricks/conda/envs/cudf-udf/bin/python
10:54:26  
10:54:26      def test_cast_neg_to_decimal_err():
10:54:26          # -12 cannot be represented as decimal(7,7)
10:54:26          data_gen = _decimal_gen_7_7
10:54:26          exception_content = "Decimal(compact,-120000000,20,0}) cannot be represented as Decimal(7, 7)"
10:54:26          exception_str = "java.lang.ArithmeticException: " + exception_content if is_before_spark_330() else \
10:54:26              "org.apache.spark.SparkArithmeticException: " + exception_content
10:54:26      
10:54:26  >       assert_gpu_and_cpu_error(
10:54:26              lambda spark : unary_op_df(spark, data_gen).selectExpr(
10:54:26                  'cast(-12 as {})'.format(to_cast_string(data_gen.data_type))).collect(),
10:54:26              ansi_enabled_conf,
10:54:26              exception_str)
10:54:26  
10:54:26  ../../src/main/python/arithmetic_ops_test.py:304: 
10:54:26  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
10:54:26  ../../src/main/python/asserts.py:572: in assert_gpu_and_cpu_error
10:54:26      assert_py4j_exception(lambda: with_cpu_session(df_fun, conf), error_message)
10:54:26  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
10:54:26  
10:54:26  func = <function assert_gpu_and_cpu_error.<locals>.<lambda> at 0x7f28ab314dc0>
10:54:26  error_message = 'java.lang.ArithmeticException: Decimal(compact,-120000000,20,0}) cannot be represented as Decimal(7, 7)'
10:54:26  
10:54:26      def assert_py4j_exception(func, error_message):
10:54:26          """
10:54:26          Assert that a specific Java exception is thrown
10:54:26          :param func: a function to be verified
10:54:26          :param error_message: a string such as the one produce by java.lang.Exception.toString
10:54:26          :return: Assertion failure if no exception matching error_message has occurred.
10:54:26          """
10:54:26          with pytest.raises(Py4JJavaError) as py4jError:
10:54:26              func()
10:54:26  >       assert error_message in str(py4jError.value.java_exception)
10:54:26  E       AssertionError
10:54:26  
10:54:26  ../../src/main/python/asserts.py:561: AssertionError
10:54:26  ----------------------------- Captured stderr call -----------------------------
10:54:26  22/05/20 14:33:20 WARN SQLExecution: Error executing delta metering
10:54:26  org.apache.spark.SparkArithmeticException: Decimal(compact,-120000000,20,0}) cannot be represented as Decimal(7, 7). If necessary set spark.sql.ansi.enabled to false to bypass this error.
10:54:26  	at org.apache.spark.sql.errors.QueryExecutionErrors$.cannotChangeDecimalPrecisionError(QueryExecutionErrors.scala:110)
10:54:26  	at org.apache.spark.sql.catalyst.expressions.CastBase.changePrecision(Cast.scala:816)
10:54:26  	at org.apache.spark.sql.catalyst.expressions.CastBase.$anonfun$castToDecimal$12(Cast.scala:849)
10:54:26  	at org.apache.spark.sql.catalyst.expressions.CastBase.nullSafeEval(Cast.scala:995)
10:54:26  	at org.apache.spark.sql.catalyst.expressions.UnaryExpression.eval(Expression.scala:625)
10:54:26  	at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:103)
10:54:26  	at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:69)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:575)
10:54:26  	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:167)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:575)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$3(TreeNode.scala:580)
10:54:26  	at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1226)
10:54:26  	at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1225)
10:54:26  	at org.apache.spark.sql.catalyst.expressions.UnaryExpression.mapChildren(Expression.scala:607)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:580)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$transformExpressionsDownWithPruning$1(QueryPlan.scala:161)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$1(QueryPlan.scala:202)
10:54:26  	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:167)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:202)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.recursiveTransform$1(QueryPlan.scala:213)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$3(QueryPlan.scala:218)
10:54:26  	at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
10:54:26  	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
10:54:26  	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
10:54:26  	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
10:54:26  	at scala.collection.TraversableLike.map(TraversableLike.scala:286)
10:54:26  	at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
10:54:26  	at scala.collection.AbstractTraversable.map(Traversable.scala:108)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.recursiveTransform$1(QueryPlan.scala:218)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$4(QueryPlan.scala:223)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:418)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:223)
10:54:26  	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDownWithPruning(QueryPlan.scala:161)
10:54:26  	at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1.applyOrElse(expressions.scala:69)
10:54:26  	at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1.applyOrElse(expressions.scala:67)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:575)
10:54:26  	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:167)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:575)
10:54:26  	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
10:54:26  	at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:268)
10:54:26  	at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:264)
10:54:26  	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
10:54:26  	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
10:54:26  	at org.apache.spark.sql.catalyst.trees.TreeNode.transformWithPruning(TreeNode.scala:541)
10:54:26  	at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$.apply(expressions.scala:67)
10:54:26  	at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$.apply(expressions.scala:49)
10:54:26  	at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$3(RuleExecutor.scala:216)
10:54:26  	at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
10:54:26  	at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:216)
10:54:26  	at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
10:54:26  	at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
10:54:26  	at scala.collection.immutable.List.foldLeft(List.scala:91)
10:54:26  	at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:213)
10:54:26  	at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:205)
10:54:26  	at scala.collection.immutable.List.foreach(List.scala:431)
10:54:26  	at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:205)
10:54:26  	at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:184)
10:54:26  	at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:126)
10:54:26  	at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:184)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.$anonfun$optimizedPlan$1(QueryExecution.scala:196)
10:54:26  	at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80)
10:54:26  	at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:151)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:265)
10:54:26  	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:958)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:265)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:192)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:188)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.assertOptimized(QueryExecution.scala:206)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:225)
10:54:26  	at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:222)
10:54:26  	at com.databricks.sql.transaction.tahoe.metering.DeltaMetering$.reportUsage(DeltaMetering.scala:136)
10:54:26  	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$8(SQLExecution.scala:277)
10:54:26  	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:360)
10:54:26  	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$1(SQLExecution.scala:160)
10:54:26  	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:958)
10:54:26  	at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:115)
10:54:26  	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:310)
10:54:26  	at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3928)
10:54:26  	at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3716)
10:54:26  	at sun.reflect.GeneratedMethodAccessor128.invoke(Unknown Source)
10:54:26  	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
10:54:26  	at java.lang.reflect.Method.invoke(Method.java:498)
10:54:26  	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
10:54:26  	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
10:54:26  	at py4j.Gateway.invoke(Gateway.java:295)
10:54:26  	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
10:54:26  	at py4j.commands.CallCommand.execute(CallCommand.java:79)
10:54:26  	at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:195)
10:54:26  	at py4j.ClientServerConnection.run(ClientServerConnection.java:115)
10:54:26  	at java.lang.Thread.run(Thread.java:748)

[tgravescs]

this test was added in #5483 and likely wasn't tested on databricks