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From Aaron Davidson <ilike...@gmail.com>
Subject Re: JVM error
Date Thu, 27 Feb 2014 04:22:39 GMT
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 <xoltar@xoltar.org> 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 <mohit1007@gmail.com> 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 <xoltar@xoltar.org> 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 <mohit1007@gmail.com>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
>>
>
>

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