On 27 Feb 2014, at 07:22, Aaron Davidson <[email protected]> wrote:
> Setting spark.executor.memory is indeed the correct way to do this. If you > want to configure this in spark-env.sh, you can use > export SPARK_JAVA_OPTS=" -Dspark.executor.memory=20g" > (make sure to append the variable if you've been using SPARK_JAVA_OPTS > previously) > > > On Wed, Feb 26, 2014 at 7:50 PM, Bryn Keller <[email protected]> wrote: > Hi Mohit, > > You can still set SPARK_MEM in spark-env.sh, but that is deprecated. This is > from SparkContext.scala: > > if (!conf.contains("spark.executor.memory") && sys.env.contains("SPARK_MEM")) > { > logWarning("Using SPARK_MEM to set amount of memory to use per executor > process is " + > "deprecated, instead use spark.executor.memory") > } > > Thanks, > Bryn > > > On Wed, Feb 26, 2014 at 6:28 PM, Mohit Singh <[email protected]> wrote: > 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 > >
