Hi Bryn,
Thanks for the suggestion.
I tried that..
conf = pyspark.SparkConf().set("spark.executor.memory","20G")
But.. got an error here:
sc = pyspark.SparkConf(conf = conf)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: __init__() got an unexpected keyword argument 'conf'
??
This is in pyspark shell.
On Thu, Feb 27, 2014 at 5:00 AM, Evgeniy Shishkin <itparan...@gmail.com>wrote:
>
> On 27 Feb 2014, at 07:22, Aaron Davidson <ilike...@gmail.com> 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 <xol...@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 <mohit1...@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 <xol...@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 <mohit1...@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
> >
> >
>
>
--
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
