And I tried that but got the error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hadoop/spark/python/pyspark/context.py", line 83, in __init__
SparkContext._ensure_initialized(self)
File "/home/hadoop/spark/python/pyspark/context.py", line 165, in
_ensure_initialized
raise ValueError("Cannot run multiple SparkContexts at once")
ValueError: Cannot run multiple SparkContexts at once
On Fri, Feb 28, 2014 at 11:59 AM, Bryn Keller <xol...@xoltar.org> wrote:
> Sorry, typo - that last line should be:
>
> sc = pyspark.Spark*Context*(conf = conf)
>
>
> On Fri, Feb 28, 2014 at 9:37 AM, Mohit Singh <mohit1...@gmail.com> wrote:
>
>> 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
>>
>
>
--
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
