Hi Bryn, Thanks for responding. Is there a way I can permanently configure this setting? like SPARK_EXECUTOR_MEMORY or somethign like that?
On Wed, Feb 26, 2014 at 2:56 PM, Bryn Keller <[email protected]> wrote: > Hi Mohit, > > Try increasing the *executor* memory instead of the worker memory - the > most appropriate place to do this is actually when you're creating your > SparkContext, something like: > > conf = pyspark.SparkConf() > .setMaster("spark://master:7077") > .setAppName("Example") > .setSparkHome("/your/path/to/spark") > .set("spark.executor.memory", "20G") > .set("spark.logConf", "true") > sc = pyspark.SparkConf(conf = conf) > > Hope that helps, > Bryn > > > > On Wed, Feb 26, 2014 at 2:39 PM, Mohit Singh <[email protected]> wrote: > >> Hi, >> I am experimenting with pyspark lately... >> Every now and then, I see this error bieng streamed to pyspark shell .. >> and most of the times.. the computation/operation completes.. and >> sometimes, it just gets stuck... >> My setup is 8 node cluster.. with loads of ram(256GB's) and space( TB's) >> per node. >> This enviornment is shared by general hadoop and hadoopy stuff..with >> recent spark addition... >> >> java.lang.OutOfMemoryError: Java heap space >> at >> com.ning.compress.BufferRecycler.allocEncodingBuffer(BufferRecycler.java:59) >> at com.ning.compress.lzf.ChunkEncoder.<init>(ChunkEncoder.java:93) >> at >> com.ning.compress.lzf.impl.UnsafeChunkEncoder.<init>(UnsafeChunkEncoder.java:40) >> at >> com.ning.compress.lzf.impl.UnsafeChunkEncoderLE.<init>(UnsafeChunkEncoderLE.java:13) >> at >> com.ning.compress.lzf.impl.UnsafeChunkEncoders.createEncoder(UnsafeChunkEncoders.java:31) >> at >> com.ning.compress.lzf.util.ChunkEncoderFactory.optimalInstance(ChunkEncoderFactory.java:44) >> at >> com.ning.compress.lzf.LZFOutputStream.<init>(LZFOutputStream.java:61) >> at >> org.apache.spark.io.LZFCompressionCodec.compressedOutputStream(CompressionCodec.scala:60) >> at >> org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:803) >> at >> org.apache.spark.storage.BlockManager$$anonfun$5.apply(BlockManager.scala:471) >> at >> org.apache.spark.storage.BlockManager$$anonfun$5.apply(BlockManager.scala:471) >> at >> org.apache.spark.storage.DiskBlockObjectWriter.open(BlockObjectWriter.scala:117) >> at >> org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:174) >> at >> org.apache.spark.scheduler.ShuffleMapTask$$anonfun$runTask$1.apply(ShuffleMapTask.scala:164) >> at >> org.apache.spark.scheduler.ShuffleMapTask$$anonfun$runTask$1.apply(ShuffleMapTask.scala:161) >> at scala.collection.Iterator$class.foreach(Iterator.scala:727) >> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) >> at >> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161) >> at >> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102) >> at org.apache.spark.scheduler.Task.run(Task.scala:53) >> at >> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213) >> at >> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49) >> at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178) >> 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:744) >> >> >> >> Most of the settings in spark are default.. So i was wondering if maybe, >> there is some configuration that needs to happen? >> There is this one config I have addded to spark_env file >> SPARK_WORKER_MEMORY=20g >> >> Also, I see tons of these errors as well.. >> 14/02/26 14:33:17 INFO TaskSetManager: Loss was due to >> java.lang.OutOfMemoryError: Java heap space [duplicate 1] >> 14/02/26 14:33:17 INFO TaskSetManager: Starting task 996.0:278 as TID >> 1792 on executor 9: node02 (PROCESS_LOCAL) >> 14/02/26 14:33:17 INFO TaskSetManager: Serialized task 996.0:278 as 4070 >> bytes in 0 ms >> 14/02/26 14:33:17 WARN TaskSetManager: Lost TID 1488 (task 996.0:184) >> 14/02/26 14:33:17 INFO TaskSetManager: Loss was due to >> java.lang.OutOfMemoryError: Java heap space [duplicate 2] >> 14/02/26 14:33:17 INFO TaskSetManager: Starting task 996.0:247 as TID >> 1793 on executor 9: node02 (PROCESS_LOCAL) >> 14/02/26 14:33:17 INFO TaskSetManager: Serialized task 996.0:247 as 4070 >> bytes in 0 ms >> 14/02/26 14:33:17 WARN TaskSetManager: Lost TID 1484 (task 996.0:82) >> 14/02/26 14:33:17 INFO TaskSetManager: Loss was due to >> java.lang.OutOfMemoryError: Java heap space [duplicate 3] >> 14/02/26 14:33:17 INFO TaskSetManager: Starting task 996.0:116 as TID >> 1794 on executor 9: node02 (PROCESS_LOCAL) >> 14/02/26 14:33:17 INFO TaskSetManager: Serialized task 996.0:116 as 4070 >> bytes in 1 ms >> 14/02/26 14:33:17 WARN TaskSetManager: Lost TID 1475 (task 996.0:157) >> 14/02/26 14:33:17 INFO TaskSetManager: Loss was due to >> java.lang.OutOfMemoryError: Java heap space [duplicate 4] >> 14/02/26 14:33:17 INFO TaskSetManager: Starting task 996.0:98 as TID 1795 >> on executor 9: node02 (PROCESS_LOCAL) >> 14/02/26 14:33:17 INFO TaskSetManager: Serialized task 996.0:98 as 4070 >> bytes in 1 ms >> 14/02/26 14:33:17 WARN TaskSetManager: Lost TID 1492 (task 996.0:17) >> >> >> and then... >> >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1649 (task 996.0:115) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1666 (task 996.0:32) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1675 (task 996.0:160) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1657 (task 996.0:349) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1660 (task 996.0:141) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1651 (task 996.0:55) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1669 (task 996.0:126) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1678 (task 996.0:173) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1663 (task 996.0:128) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1672 (task 996.0:28) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1654 (task 996.0:96) >> 14/02/26 14:33:20 WARN TaskSetManager: Lost TID 1699 (task 996.0:294) >> 14/02/26 14:33:20 INFO DAGScheduler: Executor lost: 12 (epoch 16) >> 14/02/26 14:33:20 INFO BlockManagerMasterActor: Trying to remove executor >> 12 from BlockManagerMaster. >> 14/02/26 14:33:20 INFO BlockManagerMaster: Removed 12 successfully in >> removeExecutor >> 14/02/26 14:33:20 INFO Stage: Stage 996 is now unavailable on executor 12 >> (0/379, false) >> >> >> which looks like warnings.. >> >> >> The code I tried to run was: >> subs_count = complex_key.map( lambda x: >> (x[0],int(x[1])).reduceByKey(lambda a,b:a+b)) >> subs_count.take(20) >> >> Thanks >> >> -- >> Mohit >> >> "When you want success as badly as you want the air, then you will get >> it. There is no other secret of success." >> -Socrates >> > > -- Mohit "When you want success as badly as you want the air, then you will get it. There is no other secret of success." -Socrates
