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Jungtaek Lim resolved SPARK-35602. ---------------------------------- Fix Version/s: 3.1.3 3.2.0 Resolution: Fixed Issue resolved by pull request 32788 [https://github.com/apache/spark/pull/32788] > Job crashes with java.io.UTFDataFormatException: encoded string too long > ------------------------------------------------------------------------ > > Key: SPARK-35602 > URL: https://issues.apache.org/jira/browse/SPARK-35602 > Project: Spark > Issue Type: Bug > Components: Structured Streaming > Affects Versions: 3.1.1 > Environment: AWS emr-6.3.0 > Reporter: dejan miljkovic > Assignee: Kousuke Saruta > Priority: Major > Fix For: 3.2.0, 3.1.3 > > > Running stafeful structured streaming app using java. When running on Spark > 3.1.1 app is crashing with java.io.UTFDataFormatException: encoded string too > long. I am not getting this problem when running on Spark 3.0.1 > 21/06/01 17:50:35 WARN DAGScheduler: Broadcasting large task binary with size > 1986.3 KiB21/06/01 17:50:35 WARN DAGScheduler: Broadcasting large task binary > with size 1986.3 KiB21/06/01 17:50:37 WARN TaskSetManager: Lost task 0.0 in > stage 1.0 (TID 0) (ip-10-64-12-189.eu-west-1.compute.internal executor 1): > java.io.UTFDataFormatException: encoded string too long: 156449 bytes at > java.io.DataOutputStream.writeUTF(DataOutputStream.java:364) at > java.io.DataOutputStream.writeUTF(DataOutputStream.java:323) at > org.apache.spark.sql.execution.streaming.state.StateSchemaCompatibilityChecker.createSchemaFile(StateSchemaCompatibilityChecker.scala:102) > at > org.apache.spark.sql.execution.streaming.state.StateSchemaCompatibilityChecker.check(StateSchemaCompatibilityChecker.scala:67) > at > org.apache.spark.sql.execution.streaming.state.StateStore$.$anonfun$getStateStoreProvider$2(StateStore.scala:487) > at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at > scala.util.Try$.apply(Try.scala:213) at > org.apache.spark.sql.execution.streaming.state.StateStore$.$anonfun$getStateStoreProvider$1(StateStore.scala:487) > at scala.collection.mutable.HashMap.getOrElseUpdate(HashMap.scala:86) at > org.apache.spark.sql.execution.streaming.state.StateStore$.getStateStoreProvider(StateStore.scala:483) > at > org.apache.spark.sql.execution.streaming.state.StateStore$.get(StateStore.scala:468) > at > org.apache.spark.sql.execution.streaming.state.StateStoreRDD.compute(StateStoreRDD.scala:125) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at > org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at > org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at > org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at > org.apache.spark.scheduler.Task.run(Task.scala:131) at > org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439) at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500) at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) > at java.lang.Thread.run(Thread.java:748) > 21/06/01 17:50:40 ERROR TaskSetManager: Task 0 in stage 1.0 failed 4 times; > aborting job21/06/01 17:50:40 ERROR WriteToDataSourceV2Exec: Data source > write support > org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@2745b41f is > aborting.21/06/01 17:50:40 ERROR WriteToDataSourceV2Exec: Data source write > support > org.apache.spark.sql.execution.streaming.sources.MicroBatchWrite@2745b41f > aborted.21/06/01 17:50:40 ERROR MicroBatchExecution: Query [id = > adcf4f93-8c51-4a14-9d9d-1e7a858c8a8c, runId = > 86b6c41c-a32f-485d-bbf3-24b844c27739] terminated with > errororg.apache.spark.SparkException: Writing job aborted. at > org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2(WriteToDataSourceV2Exec.scala:388) > at > org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec.writeWithV2$(WriteToDataSourceV2Exec.scala:336) > at > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.writeWithV2(WriteToDataSourceV2Exec.scala:297) > at > org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.run(WriteToDataSourceV2Exec.scala:304) > at > org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:40) > at > org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:40) > at > org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:46) > at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3733) at > org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:3005) at > org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3724) at > org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107) > at > org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232) > at > org.apache.spark.sql.execution.SQLExecution$.executeQuery$1(SQLExecution.scala:110) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:135) > at > org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107) > at > org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:135) > at > org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:253) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:134) > at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772) at > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:68) > at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3722) at > org.apache.spark.sql.Dataset.collect(Dataset.scala:3005) at > org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$16(MicroBatchExecution.scala:589) > at > org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107) > at > org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232) > at > org.apache.spark.sql.execution.SQLExecution$.executeQuery$1(SQLExecution.scala:110) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:135) > at > org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107) > at > org.apache.spark.sql.execution.SQLExecution$.withTracker(SQLExecution.scala:232) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:135) > at > org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:253) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:134) > at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772) at > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:68) > at > org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$15(MicroBatchExecution.scala:584) > at > org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:357) > at > org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:355) > at > org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:68) > at > org.apache.spark.sql.execution.streaming.MicroBatchExecution.runBatch(MicroBatchExecution.scala:584) > at > org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$2(MicroBatchExecution.scala:226) > at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at > org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:357) > at > org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:355) > at > org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:68) > at > org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$1(MicroBatchExecution.scala:194) > at > org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:57) > at > org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:188) > at > org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:333) > at > org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:244) -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: 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