Reputation: 53806
Can Spark be configured so that instead of binding to address 127.0.1.1 for port 7077, can instead be bound to 0.0.0.0 . In same way as port 8080 is bound :
netstat -pln
(Not all processes could be identified, non-owned process info
will not be shown, you would have to be root to see it all.)
Active Internet connections (only servers)
Proto Recv-Q Send-Q Local Address Foreign Address State PID/Program name
tcp 0 0 127.0.1.1:7077 0.0.0.0:* LISTEN 2864/java
tcp 0 0 0.0.0.0:8080 0.0.0.0:* LISTEN 2864/java
tcp 0 0 127.0.1.1:6066 0.0.0.0:* LISTEN 2864/java
tcp 0 0 0.0.0.0:22 0.0.0.0:* LISTEN -
udp 0 0 0.0.0.0:68 0.0.0.0:* -
udp 0 0 192.168.192.22:123 0.0.0.0:* -
udp 0 0 127.0.0.1:123 0.0.0.0:* -
udp 0 0 0.0.0.0:123 0.0.0.0:* -
udp 0 0 0.0.0.0:21415 0.0.0.0:* -
Active UNIX domain sockets (only servers)
Proto RefCnt Flags Type State I-Node PID/Program name Path
unix 2 [ ACC ] STREAM LISTENING 7195 - /var/run/dbus/system_bus_socket
unix 2 [ ACC ] SEQPACKET LISTENING 405 - /run/udev/control
Reason I'm asking this is that I'm unable to connect workers to master node and I think the issue is that the master ip is not discoverable.
Error when try to connect slave to master :
15/04/02 21:58:18 WARN Remoting: Tried to associate with unreachable remote address [akka.tcp://sparkMaster@raspberrypi:7077]. Address is now gated for 5000 ms, all messages to this address will be delivered to dead letters. Reason: Connection refused: raspberrypi/192.168.192.22:7077
15/04/02 21:58:18 INFO RemoteActorRefProvider$RemoteDeadLetterActorRef: Message [org.apache.spark.deploy.DeployMessages$RegisterWorker] from Actor[akka://sparkWorker/user/Worker#1677101765] to Actor[akka://sparkWorker/deadLetters] was not delivered. [10] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
Upvotes: 2
Views: 5036
Reputation: 11274
In spark-env.sh you can set SPARK_MASTER_IP=<ip>
.
A hostname would also work fine (via SPARK_STANDALONE_MASTER=<hostname>
), just make sure the workers connect to exactly the same hostname as the master binds to (i.e. the spark:// address that is shown in Spark master UI).
Upvotes: 3