Josh Looked closer, I think you are correct, not a racing condition. This only shows up on persisting string, other data format looks fine.
Also whe we reverted to 1.4 the issue's gone. Thanks On Thursday, 24 September 2015, Josh Rosen <rosenvi...@gmail.com> wrote: > I believe that this is an instance of > https://issues.apache.org/jira/browse/SPARK-10422, which should be fixed > in upcoming 1.5.1 release. > > On Thu, Sep 24, 2015 at 12:52 PM, Mark Hamstra <m...@clearstorydata.com > <javascript:_e(%7B%7D,'cvml','m...@clearstorydata.com');>> wrote: > >> Where do you see a race in the DAGScheduler? On a quick look at your >> stack trace, this just looks to me like a Job where a Stage failed and then >> the DAGScheduler aborted the failed Job. >> >> On Thu, Sep 24, 2015 at 12:00 PM, robin_up <robin...@gmail.com >> <javascript:_e(%7B%7D,'cvml','robin...@gmail.com');>> wrote: >> >>> Hi >>> >>> After upgrade to 1.5, we found a possible racing condition in >>> DAGScheduler >>> similar to https://issues.apache.org/jira/browse/SPARK-4454. >>> >>> Here is the code creating the problem: >>> >>> >>> app_cpm_load = sc.textFile("/user/a/app_ecpm.txt").map(lambda x: >>> x.split(',')).map(lambda p: Row(app_id=str(p[0]), loc_filter=str(p[1]), >>> cpm_required=float(p[2]) )) >>> app_cpm = sqlContext.createDataFrame(app_cpm_load) >>> app_cpm.registerTempTable("app_cpm") >>> >>> app_rev_cpm_sql = '''select loc_filter from app_cpm''' >>> app_rev_cpm = sqlContext.sql(app_rev_cpm_sql) >>> app_rev_cpm.cache() >>> app_rev_cpm.show() >>> >>> >>> >>> Traceback (most recent call last): >>> File "<stdin>", line 1, in <module> >>> File "/opt/spark/python/pyspark/sql/dataframe.py", line 256, in show >>> print(self._jdf.showString(n, truncate)) >>> File "/opt/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", >>> line 538, in __call__ >>> File "/opt/spark/python/pyspark/sql/utils.py", line 36, in deco >>> return f(*a, **kw) >>> File "/opt/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", >>> line >>> 300, in get_return_value >>> py4j.protocol.Py4JJavaError: An error occurred while calling >>> o46.showString. >>> : org.apache.spark.SparkException: Job aborted due to stage failure: >>> Task 0 >>> in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage >>> 1.0 >>> (TID 4, spark-yarn-dn02): java.util.NoSuchElementException: key not >>> found: >>> UK >>> at scala.collection.MapLike$class.default(MapLike.scala:228) >>> at scala.collection.AbstractMap.default(Map.scala:58) >>> at scala.collection.mutable.HashMap.apply(HashMap.scala:64) >>> at >>> >>> org.apache.spark.sql.columnar.compression.DictionaryEncoding$Encoder.compress(compressionSchemes.scala:258) >>> at >>> >>> org.apache.spark.sql.columnar.compression.CompressibleColumnBuilder$class.build(CompressibleColumnBuilder.scala:110) >>> at >>> >>> org.apache.spark.sql.columnar.NativeColumnBuilder.build(ColumnBuilder.scala:87) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1$$anonfun$next$2.apply(InMemoryColumnarTableScan.scala:152) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1$$anonfun$next$2.apply(InMemoryColumnarTableScan.scala:152) >>> at >>> >>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >>> at >>> >>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >>> at >>> >>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) >>> at >>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) >>> at >>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244) >>> at >>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryColumnarTableScan.scala:152) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryColumnarTableScan.scala:120) >>> at >>> org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:278) >>> at >>> org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171) >>> at >>> org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:262) >>> at >>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at >>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>> at >>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at >>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>> at >>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >>> at org.apache.spark.scheduler.Task.run(Task.scala:88) >>> at >>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>> at >>> >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>> at >>> >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>> at java.lang.Thread.run(Thread.java:745) >>> >>> Driver stacktrace: >>> at >>> org.apache.spark.scheduler.DAGScheduler.org >>> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1280) >>> at >>> >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1268) >>> at >>> >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1267) >>> at >>> >>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>> at >>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>> at >>> >>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1267) >>> at >>> >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697) >>> at >>> >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697) >>> at scala.Option.foreach(Option.scala:236) >>> at >>> >>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697) >>> at >>> >>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1493) >>> at >>> >>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1455) >>> at >>> >>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1444) >>> at >>> org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) >>> at >>> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567) >>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1813) >>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1826) >>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1839) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:215) >>> at >>> >>> org.apache.spark.sql.execution.Limit.executeCollect(basicOperators.scala:207) >>> at >>> >>> org.apache.spark.sql.DataFrame$$anonfun$collect$1.apply(DataFrame.scala:1386) >>> at >>> >>> org.apache.spark.sql.DataFrame$$anonfun$collect$1.apply(DataFrame.scala:1386) >>> at >>> >>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56) >>> at >>> org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:1904) >>> at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:1385) >>> at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1315) >>> at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1378) >>> at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:178) >>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>> at >>> >>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>> at >>> >>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>> at java.lang.reflect.Method.invoke(Method.java:606) >>> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) >>> at >>> py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379) >>> at py4j.Gateway.invoke(Gateway.java:259) >>> at >>> py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) >>> at py4j.commands.CallCommand.execute(CallCommand.java:79) >>> at py4j.GatewayConnection.run(GatewayConnection.java:207) >>> at java.lang.Thread.run(Thread.java:745) >>> Caused by: java.util.NoSuchElementException: key not found: UK >>> at scala.collection.MapLike$class.default(MapLike.scala:228) >>> at scala.collection.AbstractMap.default(Map.scala:58) >>> at scala.collection.mutable.HashMap.apply(HashMap.scala:64) >>> at >>> >>> org.apache.spark.sql.columnar.compression.DictionaryEncoding$Encoder.compress(compressionSchemes.scala:258) >>> at >>> >>> org.apache.spark.sql.columnar.compression.CompressibleColumnBuilder$class.build(CompressibleColumnBuilder.scala:110) >>> at >>> >>> org.apache.spark.sql.columnar.NativeColumnBuilder.build(ColumnBuilder.scala:87) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1$$anonfun$next$2.apply(InMemoryColumnarTableScan.scala:152) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1$$anonfun$next$2.apply(InMemoryColumnarTableScan.scala:152) >>> at >>> >>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >>> at >>> >>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >>> at >>> >>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) >>> at >>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) >>> at >>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244) >>> at >>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryColumnarTableScan.scala:152) >>> at >>> >>> org.apache.spark.sql.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryColumnarTableScan.scala:120) >>> at >>> org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:278) >>> at >>> org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171) >>> at >>> org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:262) >>> at >>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at >>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>> at >>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at >>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >>> at >>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >>> at org.apache.spark.scheduler.Task.run(Task.scala:88) >>> at >>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>> at >>> >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>> at >>> >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>> ... 1 more >>> >>> >>> >>> >>> >>> ----- >>> -- Robin Li >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/Potential-racing-condition-in-DAGScheduler-when-Spark-1-5-caching-tp24810.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> <javascript:_e(%7B%7D,'cvml','user-unsubscr...@spark.apache.org');> >>> For additional commands, e-mail: user-h...@spark.apache.org >>> <javascript:_e(%7B%7D,'cvml','user-h...@spark.apache.org');> >>> >>> >> > -- Robin
