Re: Spark on Yarn probably trying to load all the data to RAM
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
Re: Spark on Yarn probably trying to load all the data to RAM
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
Re: Spark on Yarn probably trying to load all the data to RAM
Could you please give me an example or send me a link of how to use Hadoop CombinedFileInputFormat? It sound very interesting to me and it would probably save me several hours of my pipeline computation. Merging of the files is currently the bottleneck in my system. __ Another potential option could be to use Hadoop CombinedFileInputFormat with input split size of say 512 MB or 1 GB. That way you don't need to have a preceding step and I/O of first combining the files together. On Nov 5, 2014 8:23 AM, jan.zi...@centrum.cz jan.zi...@centrum.cz wrote: 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 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 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]
Re: Spark on Yarn probably trying to load all the data to RAM
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}
Re: Spark on Yarn probably trying to load all the data to RAM
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
