It's something due to the columnar compression. I've seen similar intermittent issues when caching DataFrames. "sportingpulse.com" is a value in one of the columns of the DF. ________________________________ From: Ted Yu<mailto:yuzhih...@gmail.com> Sent: 10/30/2015 6:33 PM To: Zhang, Jingyu<mailto:jingyu.zh...@news.com.au> Cc: user<mailto:user@spark.apache.org> Subject: Re: key not found: sportingpulse.com in Spark SQL 1.5.0
I searched for sportingpulse in *.scala and *.java files under 1.5 branch. There was no hit. mvn dependency doesn't show sportingpulse either. Is it possible this is specific to EMR ? Cheers On Fri, Oct 30, 2015 at 2:57 PM, Zhang, Jingyu <jingyu.zh...@news.com.au<mailto:jingyu.zh...@news.com.au>> wrote: There is not a problem in Spark SQL 1.5.1 but the error of "key not found: sportingpulse.com<http://sportingpulse.com/>" shown up when I use 1.5.0. I have to use the version of 1.5.0 because that the one AWS EMR support. Can anyone tell me why Spark uses "sportingpulse.com<http://sportingpulse.com/>" and how to fix it? Thanks. Caused by: java.util.NoSuchElementException: key not found: sportingpulse.com<http://sportingpulse.com> 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$1.next(InMemoryColumnarTableScan.scala:120) at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:278) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78) at org.apache.spark.rdd.RDD.iterator(RDD.scala:262) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:63) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) This message and its attachments may contain legally privileged or confidential information. It is intended solely for the named addressee. If you are not the addressee indicated in this message or responsible for delivery of the message to the addressee, you may not copy or deliver this message or its attachments to anyone. Rather, you should permanently delete this message and its attachments and kindly notify the sender by reply e-mail. Any content of this message and its attachments which does not relate to the official business of the sending company must be taken not to have been sent or endorsed by that company or any of its related entities. No warranty is made that the e-mail or attachments are free from computer virus or other defect.
