cloud-fan commented on code in PR #43438:
URL: https://github.com/apache/spark/pull/43438#discussion_r1366941333
##########
core/src/test/scala/org/apache/spark/ui/UIUtilsSuite.scala:
##########
@@ -192,7 +192,7 @@ class UIUtilsSuite extends SparkFunSuite {
// scalastyle:off line.size.limit
test("SPARK-44367: Extract errorClass from errorMsg with errorMessageCell") {
- val e1 = "Job aborted due to stage failure: Task 0 in stage 1.0 failed 1
times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1) (10.221.98.22
executor driver): org.apache.spark.SparkArithmeticException: [DIVIDE_BY_ZERO]
Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL
instead. If necessary set \"spark.sql.ansi.enabled\" to \"false\" to bypass
this error.\n== SQL(line 1, position 8) ==\nselect a/b from src\n
^^^\n\n\tat
org.apache.spark.sql.errors.QueryExecutionErrors$.divideByZeroError(QueryExecutionErrors.scala:226)\n\tat
org.apache.spark.sql.errors.QueryExecutionErrors.divideByZeroError(QueryExecutionErrors.scala)\n\tat
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(generated.java:54)\n\tat
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat
org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvalua
tor$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:43)\n\tat
org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)\n\tat
org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:890)\n\tat
org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:890)\n\tat
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)\n\tat
org.apache.spark.rdd.RDD.iterator(RDD.scala:328)\n\tat
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93)\n\tat
org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)\n\tat
org.apache.spark.scheduler.Task.run(Task.scala:141)\n\tat
org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:592)\n\tat
org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1474)\n\tat
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:595)\n\tat
java.util.concurr
ent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat
java.lang.Thread.run(Thread.java:750)\n\nDriver stacktrace:"
+ val e1 = "Job aborted due to stage failure: Task 0 in stage 1.0 failed 1
times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1) (10.221.98.22
executor driver): org.apache.spark.SparkArithmeticException: [DIVIDE_BY_ZERO]
Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL
instead. If necessary set \"spark.sql.ansi.enabled\" to \"false\" to bypass
this error.\n== SQL (line 1, position 8) ==\nselect a/b from src\n
^^^\n\n\tat
org.apache.spark.sql.errors.QueryExecutionErrors$.divideByZeroError(QueryExecutionErrors.scala:226)\n\tat
org.apache.spark.sql.errors.QueryExecutionErrors.divideByZeroError(QueryExecutionErrors.scala)\n\tat
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(generated.java:54)\n\tat
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat
org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvalu
ator$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:43)\n\tat
org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)\n\tat
org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:890)\n\tat
org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:890)\n\tat
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\n\tat
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)\n\tat
org.apache.spark.rdd.RDD.iterator(RDD.scala:328)\n\tat
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93)\n\tat
org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)\n\tat
org.apache.spark.scheduler.Task.run(Task.scala:141)\n\tat
org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:592)\n\tat
org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1474)\n\tat
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:595)\n\tat
java.util.concur
rent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\n\tat
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n\tat
java.lang.Thread.run(Thread.java:750)\n\nDriver stacktrace:"
Review Comment:
cc @yaooqinn . My hunch is that UI calls certain functions to construct
error message by itself, which doesn't have the new SQL state handling.
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