Do you have <ip> machine1 in your workers /etc/hosts also? If so try telneting from your machine2 to machine1 on port 5060. Also make sure nothing else is running on port 5060 other than Spark (*lsof -i:5060*)
Thanks Best Regards On Thu, Jun 26, 2014 at 6:35 PM, Shannon Quinn <squ...@gatech.edu> wrote: > Still running into the same problem. /etc/hosts on the master says > > 127.0.0.1 localhost > <ip> machine1 > > <ip> is the same address set in spark-env.sh for SPARK_MASTER_IP. Any > other ideas? > > > On 6/26/14, 3:11 AM, Akhil Das wrote: > > Hi Shannon, > > It should be a configuration issue, check in your /etc/hosts and make > sure localhost is not associated with the SPARK_MASTER_IP you provided. > > Thanks > Best Regards > > > On Thu, Jun 26, 2014 at 6:37 AM, Shannon Quinn <squ...@gatech.edu> wrote: > >> Hi all, >> >> I have a 2-machine Spark network I've set up: a master and worker on >> machine1, and worker on machine2. When I run 'sbin/start-all.sh', >> everything starts up as it should. I see both workers listed on the UI >> page. The logs of both workers indicate successful registration with the >> Spark master. >> >> The problems begin when I attempt to submit a job: I get an "address >> already in use" exception that crashes the program. It says "Failed to bind >> to " and lists the exact port and address of the master. >> >> At this point, the only items I have set in my spark-env.sh are >> SPARK_MASTER_IP and SPARK_MASTER_PORT (non-standard, set to 5060). >> >> The next step I took, then, was to explicitly set SPARK_LOCAL_IP on the >> master to 127.0.0.1. This allows the master to successfully send out the >> jobs; however, it ends up canceling the stage after running this command >> several times: >> >> 14/06/25 21:00:47 INFO AppClient$ClientActor: Executor added: >> app-20140625210032-0000/8 on worker-20140625205623-machine2-53597 >> (machine2:53597) with 8 cores >> 14/06/25 21:00:47 INFO SparkDeploySchedulerBackend: Granted executor ID >> app-20140625210032-0000/8 on hostPort machine2:53597 with 8 cores, 8.0 GB >> RAM >> 14/06/25 21:00:47 INFO AppClient$ClientActor: Executor updated: >> app-20140625210032-0000/8 is now RUNNING >> 14/06/25 21:00:49 INFO AppClient$ClientActor: Executor updated: >> app-20140625210032-0000/8 is now FAILED (Command exited with code 1) >> >> The "/8" started at "/1", eventually becomes "/9", and then "/10", at >> which point the program crashes. The worker on machine2 shows similar >> messages in its logs. Here are the last bunch: >> >> 14/06/25 21:00:31 INFO Worker: Executor app-20140625210032-0000/9 >> finished with state FAILED message Command exited with code 1 exitStatus 1 >> 14/06/25 21:00:31 INFO Worker: Asked to launch executor >> app-20140625210032-0000/10 for app_name >> Spark assembly has been built with Hive, including Datanucleus jars on >> classpath >> 14/06/25 21:00:32 INFO ExecutorRunner: Launch command: "java" "-cp" >> "::/home/spark/spark-1.0.0-bin-hadoop2/conf:/home/spark/spark-1.0.0-bin-hadoop2/lib/spark-assembly-1.0.0-hadoop2.2.0.jar:/home/spark/spark-1.0.0-bin-hadoop2/lib/datanucleus-rdbms-3.2.1.jar:/home/spark/spark-1.0.0-bin-hadoop2/lib/datanucleus-core-3.2.2.jar:/home/spark/spark-1.0.0-bin-hadoop2/lib/datanucleus-api-jdo-3.2.1.jar" >> "-XX:MaxPermSize=128m" "-Xms8192M" "-Xmx8192M" >> "org.apache.spark.executor.CoarseGrainedExecutorBackend" " >> *akka.tcp://spark@localhost:5060/user/CoarseGrainedScheduler*" "10" >> "machine2" "8" "akka.tcp://sparkWorker@machine2:53597/user/Worker" >> "app-20140625210032-0000" >> 14/06/25 21:00:33 INFO Worker: Executor app-20140625210032-0000/10 >> finished with state FAILED message Command exited with code 1 exitStatus 1 >> >> I highlighted the part that seemed strange to me; that's the master port >> number (I set it to 5060), and yet it's referencing localhost? Is this the >> reason why machine2 apparently can't seem to give a confirmation to the >> master once the job is submitted? (The logs from the worker on the master >> node indicate that it's running just fine) >> >> I appreciate any assistance you can offer! >> >> Regards, >> Shannon Quinn >> >> > >
