Ok so the problem was solved, it that the file was gziped and it looks that
Spark does not support direct .gz file distribution to workers.
Thank you very much fro the suggestion to merge the files.
Best regards,
Jan
______________________________________________________________
I have tried it out to merge the file to one, Spark is now working with RAM as
I've expected.
Unfortunately after doing this there appears another problem. Now Spark running
on YARN is scheduling all the work only to one worker node as a one big job. Is
there some way, how to force Spark and Yarn to schedule all the work uniformly
across the whole cluster?
I am running job from the following command:
./spark/bin/spark-submit --master yarn-client --py-files
/home/hadoop/my_pavkage.zip /home/hadoop/preprocessor.py
I have also tried to play with options --num-executors and --executor-cores.
But unfortunately I am not able to force Spark to run jobs on more than just
one cluster node.
Thank you in advance for any advice,
Best regards,
Jan
______________________________________________________________
This is a crazy cases that has a few millions of files, the scheduler will run
out of memory. Be default, each file will become a partition, so you will
have more than 1M partitions, also 1M tasks. With coalesce(), it will reduce
the number of tasks, but can not reduce the number of partitions of original
RDD.
Could you pack the small files int bigger ones? Spark works much better than
small files.
On Mon, Nov 3, 2014 at 11:46 AM, <jan.zi...@centrum.cz> wrote:
I have 3 datasets in all the datasets the average file size is 10-12Kb.
I am able to run my code on the dataset with 70K files, but I am not able to
run it on datasets with 1.1M and 3.8M files.
______________________________________________________________
On Sun, Nov 2, 2014 at 1:35 AM, <jan.zi...@centrum.cz> wrote:
Hi,
I am using Spark on Yarn, particularly Spark in Python. I am trying to
run:
myrdd = sc.textFile("s3n://mybucket/files/*/*/*.json")
How many files do you have? and the average size of each file?
myrdd.getNumPartitions()
Unfortunately it seems that Spark tries to load everything to RAM, or at
least after while of running this everything slows down and then I am
getting errors with log below. Everything works fine for datasets smaller
than RAM, but I would expect Spark doing this without storing everything
to
RAM. So I would like to ask if I'm not missing some settings in Spark on
Yarn?
Thank you in advance for any help.
14/11/01 22:06:57 ERROR actor.ActorSystemImpl: Uncaught fatal error from
thread [sparkDriver-akka.actor.default-dispatcher-375] shutting down
ActorSystem [sparkDriver]
java.lang.OutOfMemoryError: GC overhead limit exceeded
14/11/01 22:06:57 ERROR actor.ActorSystemImpl: Uncaught fatal error from
thread [sparkDriver-akka.actor.default-dispatcher-381] shutting down
ActorSystem [sparkDriver]
java.lang.OutOfMemoryError: GC overhead limit exceeded
11744,575: [Full GC 1194515K->1192839K(1365504K), 2,2367150 secs]
11746,814: [Full GC 1194507K->1193186K(1365504K), 2,1788150 secs]
11748,995: [Full GC 1194507K->1193278K(1365504K), 1,3511480 secs]
11750,347: [Full GC 1194507K->1193263K(1365504K), 2,2735350 secs]
11752,622: [Full GC 1194506K->1193192K(1365504K), 1,2700110 secs]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hadoop/spark/python/pyspark/rdd.py", line 391, in
getNumPartitions
return self._jrdd.partitions().size()
File
"/home/hadoop/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py",
line 538, in __call__
File
"/home/hadoop/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py",
line
300, in get_return_value
py4j.protocol.Py4JJavaError14/11/01 22:07:07 INFO scheduler.DAGScheduler:
Failed to run saveAsTextFile at NativeMethodAccessorImpl.java:-2
: An error occurred while calling o112.partitions.
: java.lang.OutOfMemoryError: GC overhead limit exceeded
11753,896: [Full GC 1194506K->947839K(1365504K), 2,1483780 secs]
14/11/01 22:07:09 INFO remote.RemoteActorRefProvider$RemotingTerminator:
Shutting down remote daemon.
14/11/01 22:07:09 ERROR actor.ActorSystemImpl: Uncaught fatal error from
thread [sparkDriver-akka.actor.default-dispatcher-381] shutting down
ActorSystem [sparkDriver]
java.lang.OutOfMemoryError: GC overhead limit exceeded
14/11/01 22:07:09 ERROR actor.ActorSystemImpl: Uncaught fatal error from
thread [sparkDriver-akka.actor.default-dispatcher-309] shutting down
ActorSystem [sparkDriver]
java.lang.OutOfMemoryError: GC overhead limit exceeded
14/11/01 22:07:09 INFO remote.RemoteActorRefProvider$RemotingTerminator:
Remote daemon shut down; proceeding with flushing remote transports.
14/11/01 22:07:09 INFO Remoting: Remoting shut down
14/11/01 22:07:09 INFO remote.RemoteActorRefProvider$RemotingTerminator:
Remoting shut down.
14/11/01 22:07:09 INFO network.ConnectionManager: Removing
ReceivingConnection to
ConnectionManagerId(ip-172-31-18-35.us-west-2.compute.internal,55871)
14/11/01 22:07:09 INFO network.ConnectionManager: Removing
SendingConnection
to ConnectionManagerId(ip-172-31-18-35.us-west-2.compute.internal,55871)
14/11/01 22:07:09 INFO network.ConnectionManager: Removing
SendingConnection
to ConnectionManagerId(ip-172-31-18-35.us-west-2.compute.internal,55871)
14/11/01 22:07:09 INFO network.ConnectionManager: Key not valid ?
sun.nio.ch.SelectionKeyImpl@5ca1c790
14/11/01 22:07:09 INFO network.ConnectionManager: key already cancelled ?
sun.nio.ch.SelectionKeyImpl@5ca1c790
java.nio.channels.CancelledKeyException
at
org.apache.spark.network.ConnectionManager.run(ConnectionManager.scala:386)
at
org.apache.spark.network.ConnectionManager$$anon$4.run(ConnectionManager.scala:139)
14/11/01 22:07:09 INFO network.ConnectionManager: Removing
SendingConnection
to ConnectionManagerId(ip-172-31-18-35.us-west-2.compute.internal,52768)
14/11/01 22:07:09 INFO network.ConnectionManager: Removing
ReceivingConnection to
ConnectionManagerId(ip-172-31-18-35.us-west-2.compute.internal,52768)
14/11/01 22:07:09 ERROR network.ConnectionManager: Corresponding
SendingConnection to
ConnectionManagerId(ip-172-31-18-35.us-west-2.compute.internal,52768) not
found
14/11/01 22:07:10 ERROR cluster.YarnClientSchedulerBackend: Yarn
application
already ended: FINISHED
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/metrics/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/stages/stage/kill,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/static,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/executors/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/executors,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/environment/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/environment,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/storage/rdd/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/storage/rdd,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/storage/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/storage,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/stages/pool/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/stages/pool,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/stages/stage/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/stages/stage,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/stages/json,null}
14/11/01 22:07:10 INFO handler.ContextHandler: stopped
o.e.j.s.ServletContextHandler{/stages,null}
14/11/01 22:07:10 INFO ui.SparkUI: Stopped Spark web UI at
http://ip-172-31-20-69.us-west-2.compute.internal:4040
<http://ip-172-31-20-69.us-west-2.compute.internal:4040>
14/11/01 22:07:10 INFO scheduler.DAGScheduler: Stopping DAGScheduler
14/11/01 22:07:10 INFO cluster.YarnClientSchedulerBackend: Shutting down
all
executors
14/11/01 22:07:10 INFO cluster.YarnClientSchedulerBackend: Stopped
14/11/01 22:07:11 ERROR spark.MapOutputTrackerMaster: Error communicating
with MapOutputTracker
akka.pattern.AskTimeoutException:
Recipient[Actor[akka://sparkDriver/user/MapOutputTracker#132167931]] had
already been terminated.
at akka.pattern.AskableActorRef$.ask$extension(AskSupport.scala:134)
at
org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:106)
at
org.apache.spark.MapOutputTracker.sendTracker(MapOutputTracker.scala:117)
at
org.apache.spark.MapOutputTrackerMaster.stop(MapOutputTracker.scala:324)
at org.apache.spark.SparkEnv.stop(SparkEnv.scala:80)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1024)
at
org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$$anon$1.run(YarnClientSchedulerBackend.scala:131)
Exception in thread "Yarn Application State Checker"
org.apache.spark.SparkException: Error communicating with MapOutputTracker
at
org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:111)
at
org.apache.spark.MapOutputTracker.sendTracker(MapOutputTracker.scala:117)
at
org.apache.spark.MapOutputTrackerMaster.stop(MapOutputTracker.scala:324)
at org.apache.spark.SparkEnv.stop(SparkEnv.scala:80)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1024)
at
org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$$anon$1.run(YarnClientSchedulerBackend.scala:131)
Caused by: akka.pattern.AskTimeoutException:
Recipient[Actor[akka://sparkDriver/user/MapOutputTracker#132167931]] had
already been terminated.
at akka.pattern.AskableActorRef$.ask$extension(AskSupport.scala:134)
at
org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:106)
... 5 more
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