I have recently encountered a similar problem with Guava version collision with Hadoop.
Isn't it more correct to upgrade Hadoop to use the latest Guava? Why are they staying in version 11, does anyone know? *Romi Kuntsman*, *Big Data Engineer* http://www.totango.com On Wed, Jan 7, 2015 at 7:59 AM, Niranda Perera <niranda.per...@gmail.com> wrote: > Hi Sean, > > I removed the hadoop dependencies from the app and ran it on the cluster. > It gives a java.io.EOFException > > 15/01/07 11:19:29 INFO MemoryStore: ensureFreeSpace(177166) called with > curMem=0, maxMem=2004174766 > 15/01/07 11:19:29 INFO MemoryStore: Block broadcast_0 stored as values in > memory (estimated size 173.0 KB, free 1911.2 MB) > 15/01/07 11:19:29 INFO MemoryStore: ensureFreeSpace(25502) called with > curMem=177166, maxMem=2004174766 > 15/01/07 11:19:29 INFO MemoryStore: Block broadcast_0_piece0 stored as > bytes in memory (estimated size 24.9 KB, free 1911.1 MB) > 15/01/07 11:19:29 INFO BlockManagerInfo: Added broadcast_0_piece0 in > memory on 10.100.5.109:43924 (size: 24.9 KB, free: 1911.3 MB) > 15/01/07 11:19:29 INFO BlockManagerMaster: Updated info of block > broadcast_0_piece0 > 15/01/07 11:19:29 INFO SparkContext: Created broadcast 0 from hadoopFile > at AvroRelation.scala:45 > 15/01/07 11:19:29 INFO FileInputFormat: Total input paths to process : 1 > 15/01/07 11:19:29 INFO SparkContext: Starting job: collect at > SparkPlan.scala:84 > 15/01/07 11:19:29 INFO DAGScheduler: Got job 0 (collect at > SparkPlan.scala:84) with 2 output partitions (allowLocal=false) > 15/01/07 11:19:29 INFO DAGScheduler: Final stage: Stage 0(collect at > SparkPlan.scala:84) > 15/01/07 11:19:29 INFO DAGScheduler: Parents of final stage: List() > 15/01/07 11:19:29 INFO DAGScheduler: Missing parents: List() > 15/01/07 11:19:29 INFO DAGScheduler: Submitting Stage 0 (MappedRDD[6] at > map at SparkPlan.scala:84), which has no missing parents > 15/01/07 11:19:29 INFO MemoryStore: ensureFreeSpace(4864) called with > curMem=202668, maxMem=2004174766 > 15/01/07 11:19:29 INFO MemoryStore: Block broadcast_1 stored as values in > memory (estimated size 4.8 KB, free 1911.1 MB) > 15/01/07 11:19:29 INFO MemoryStore: ensureFreeSpace(3481) called with > curMem=207532, maxMem=2004174766 > 15/01/07 11:19:29 INFO MemoryStore: Block broadcast_1_piece0 stored as > bytes in memory (estimated size 3.4 KB, free 1911.1 MB) > 15/01/07 11:19:29 INFO BlockManagerInfo: Added broadcast_1_piece0 in > memory on 10.100.5.109:43924 (size: 3.4 KB, free: 1911.3 MB) > 15/01/07 11:19:29 INFO BlockManagerMaster: Updated info of block > broadcast_1_piece0 > 15/01/07 11:19:29 INFO SparkContext: Created broadcast 1 from broadcast at > DAGScheduler.scala:838 > 15/01/07 11:19:29 INFO DAGScheduler: Submitting 2 missing tasks from Stage > 0 (MappedRDD[6] at map at SparkPlan.scala:84) > 15/01/07 11:19:29 INFO TaskSchedulerImpl: Adding task set 0.0 with 2 tasks > 15/01/07 11:19:29 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID > 0, 10.100.5.109, PROCESS_LOCAL, 1340 bytes) > 15/01/07 11:19:29 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID > 1, 10.100.5.109, PROCESS_LOCAL, 1340 bytes) > 15/01/07 11:19:29 WARN TaskSetManager: Lost task 1.0 in stage 0.0 (TID 1, > 10.100.5.109): java.io.EOFException > at > java.io.ObjectInputStream$BlockDataInputStream.readFully(ObjectInputStream.java:2722) > at java.io.ObjectInputStream.readFully(ObjectInputStream.java:1009) > at > org.apache.hadoop.io.DataOutputBuffer$Buffer.write(DataOutputBuffer.java:63) > at > org.apache.hadoop.io.DataOutputBuffer.write(DataOutputBuffer.java:101) > at org.apache.hadoop.io.UTF8.readChars(UTF8.java:216) > at org.apache.hadoop.io.UTF8.readString(UTF8.java:208) > at org.apache.hadoop.mapred.FileSplit.readFields(FileSplit.java:87) > at > org.apache.hadoop.io.ObjectWritable.readObject(ObjectWritable.java:237) > at > org.apache.hadoop.io.ObjectWritable.readFields(ObjectWritable.java:66) > at > org.apache.spark.SerializableWritable$$anonfun$readObject$1.apply$mcV$sp(SerializableWritable.scala:43) > at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:985) > at > org.apache.spark.SerializableWritable.readObject(SerializableWritable.scala:39) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) > at java.lang.reflect.Method.invoke(Method.java:597) > at > java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:969) > at > java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1871) > at > java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1775) > at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1327) > at > java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1969) > at > java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893) > at > java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1775) > at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1327) > at > java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1969) > at > java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893) > at > java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1775) > at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1327) > at java.io.ObjectInputStream.readObject(ObjectInputStream.java:349) > at > org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62) > at > org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:87) > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178) > at > java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:895) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:918) > at java.lang.Thread.run(Thread.java:662) > > > I'm running the program using IDE. Not using spark-submit. Can we not > submit an app straight from the IDE to the spark cluster? > > Cheers > > On Tue, Jan 6, 2015 at 3:53 PM, Sean Owen <so...@cloudera.com> wrote: > >> Oh, are you actually bundling Hadoop in your app? that may be the >> problem. If you're using stand-alone mode, why include Hadoop? In any >> event, Spark and Hadoop are intended to be 'provided' dependencies in the >> app you send to spark-submit. >> >> On Tue, Jan 6, 2015 at 10:15 AM, Niranda Perera <niranda.per...@gmail.com >> > wrote: >> >>> Hi Sean, >>> >>> My mistake, Guava 11 dependency came from the hadoop-commons indeed. >>> >>> I'm running the following simple app in spark 1.2.0 standalone local >>> cluster (2 workers) with Hadoop 1.2.1 >>> >>> public class AvroSparkTest { >>> public static void main(String[] args) throws Exception { >>> SparkConf sparkConf = new SparkConf() >>> .setMaster("spark://niranda-ThinkPad-T540p:7077") >>> //("local[2]") >>> .setAppName("avro-spark-test"); >>> >>> JavaSparkContext sparkContext = new JavaSparkContext(sparkConf); >>> JavaSQLContext sqlContext = new JavaSQLContext(sparkContext); >>> JavaSchemaRDD episodes = AvroUtils.avroFile(sqlContext, >>> >>> "/home/niranda/projects/avro-spark-test/src/test/resources/episodes.avro"); >>> episodes.printSchema(); >>> episodes.registerTempTable("avroTable"); >>> List<Row> result = sqlContext.sql("SELECT * FROM >>> avroTable").collect(); >>> >>> for (Row row : result) { >>> System.out.println(row.toString()); >>> } >>> } >>> } >>> >>> As you pointed out, this error occurs while adding the hadoop >>> dependency. this runs without a problem when the hadoop dependency is >>> removed and the master is set to local[]. >>> >>> Cheers >>> >>> On Tue, Jan 6, 2015 at 3:23 PM, Sean Owen <so...@cloudera.com> wrote: >>> >>>> -dev >>>> >>>> Guava was not downgraded to 11. That PR was not merged. It was part of >>>> a discussion about, indeed, what to do about potential Guava version >>>> conflicts. Spark uses Guava, but so does Hadoop, and so do user programs. >>>> >>>> Spark uses 14.0.1 in fact: >>>> https://github.com/apache/spark/blob/master/pom.xml#L330 >>>> >>>> This is a symptom of conflict between Spark's Guava 14 and Hadoop's >>>> Guava 11. See for example >>>> https://issues.apache.org/jira/browse/HIVE-7387 as well. >>>> >>>> Guava is now shaded in Spark as of 1.2.0 (and 1.1.x?), so I would think >>>> a lot of these problems are solved. As we've seen though, this one is >>>> tricky. >>>> >>>> What's your Spark version? and what are you executing? what mode -- >>>> standalone, YARN? What Hadoop version? >>>> >>>> >>>> On Tue, Jan 6, 2015 at 8:38 AM, Niranda Perera < >>>> niranda.per...@gmail.com> wrote: >>>> >>>>> Hi, >>>>> >>>>> I have been running a simple Spark app on a local spark cluster and I >>>>> came across this error. >>>>> >>>>> Exception in thread "main" java.lang.NoSuchMethodError: >>>>> com.google.common.hash.HashFunction.hashInt(I)Lcom/google/common/hash/HashCode; >>>>> at org.apache.spark.util.collection.OpenHashSet.org >>>>> $apache$spark$util$collection$OpenHashSet$$hashcode(OpenHashSet.scala:261) >>>>> at >>>>> org.apache.spark.util.collection.OpenHashSet$mcI$sp.getPos$mcI$sp(OpenHashSet.scala:165) >>>>> at >>>>> org.apache.spark.util.collection.OpenHashSet$mcI$sp.contains$mcI$sp(OpenHashSet.scala:102) >>>>> at >>>>> org.apache.spark.util.SizeEstimator$$anonfun$visitArray$2.apply$mcVI$sp(SizeEstimator.scala:214) >>>>> at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141) >>>>> at >>>>> org.apache.spark.util.SizeEstimator$.visitArray(SizeEstimator.scala:210) >>>>> at >>>>> org.apache.spark.util.SizeEstimator$.visitSingleObject(SizeEstimator.scala:169) >>>>> at >>>>> org.apache.spark.util.SizeEstimator$.org$apache$spark$util$SizeEstimator$$estimate(SizeEstimator.scala:161) >>>>> at >>>>> org.apache.spark.util.SizeEstimator$.estimate(SizeEstimator.scala:155) >>>>> at >>>>> org.apache.spark.util.collection.SizeTracker$class.takeSample(SizeTracker.scala:78) >>>>> at >>>>> org.apache.spark.util.collection.SizeTracker$class.afterUpdate(SizeTracker.scala:70) >>>>> at >>>>> org.apache.spark.util.collection.SizeTrackingVector.$plus$eq(SizeTrackingVector.scala:31) >>>>> at >>>>> org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:249) >>>>> at >>>>> org.apache.spark.storage.MemoryStore.putIterator(MemoryStore.scala:136) >>>>> at >>>>> org.apache.spark.storage.MemoryStore.putIterator(MemoryStore.scala:114) >>>>> at >>>>> org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) >>>>> at >>>>> org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) >>>>> at >>>>> org.apache.spark.storage.BlockManager.putSingle(BlockManager.scala:992) >>>>> at >>>>> org.apache.spark.broadcast.TorrentBroadcast.writeBlocks(TorrentBroadcast.scala:98) >>>>> at >>>>> org.apache.spark.broadcast.TorrentBroadcast.<init>(TorrentBroadcast.scala:84) >>>>> at >>>>> org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:34) >>>>> at >>>>> org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:29) >>>>> at >>>>> org.apache.spark.broadcast.BroadcastManager.newBroadcast(BroadcastManager.scala:62) >>>>> at org.apache.spark.SparkContext.broadcast(SparkContext.scala:945) >>>>> at org.apache.spark.SparkContext.hadoopFile(SparkContext.scala:695) >>>>> at >>>>> com.databricks.spark.avro.AvroRelation.buildScan$lzycompute(AvroRelation.scala:45) >>>>> at >>>>> com.databricks.spark.avro.AvroRelation.buildScan(AvroRelation.scala:44) >>>>> at >>>>> org.apache.spark.sql.sources.DataSourceStrategy$.apply(DataSourceStrategy.scala:56) >>>>> at >>>>> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) >>>>> at >>>>> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) >>>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) >>>>> at >>>>> org.apache.spark.sql.catalyst.planning.QueryPlanner.apply(QueryPlanner.scala:59) >>>>> at >>>>> org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:418) >>>>> at >>>>> org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:416) >>>>> at >>>>> org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:422) >>>>> at >>>>> org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:422) >>>>> at org.apache.spark.sql.SchemaRDD.collect(SchemaRDD.scala:444) >>>>> at >>>>> org.apache.spark.sql.api.java.JavaSchemaRDD.collect(JavaSchemaRDD.scala:114) >>>>> >>>>> >>>>> While looking into this I found out that Guava was downgraded to >>>>> version 11 in this PR. >>>>> https://github.com/apache/spark/pull/1610 >>>>> >>>>> In this PR OpenHashSet.scala:261 line hashInt has been changed to >>>>> hashLong. >>>>> But when I actually run my app, "java.lang.NoSuchMethodError: >>>>> com.google.common.hash.HashFunction.hashInt" error occurs, >>>>> which is understandable because hashInt is not available before Guava >>>>> 12. >>>>> >>>>> So, I''m wondering why this occurs? >>>>> >>>>> Cheers >>>>> -- >>>>> Niranda Perera >>>>> >>>>> >>>> >>> >>> >>> -- >>> Niranda >>> >> >> > > > -- > Niranda >
