Hello, I have a cluster 1 master and 2 slaves running on 1.1.0. I am having problems to get both slaves working at the same time. When I launch the driver on the master, one of the slaves is assigned the receiver task, and initially both slaves start processing tasks. After a few tens of batches, the slave running the receiver starts processing all tasks, and the other won't execute any task more. If I cancel the execution and start over, the roles may switch if the other slave gets to be assigned the receiver, but the behaviour is the same, and the other slave will stop processing tasks after a short while. So both slaves are working, essentially, but never at the same time in a consistent way. No errors on logs, etc.
I have tried increasing partitions (up to 100, while slaves have 4 cores each) but no success :-/ I understand that Spark may decide not to distribute tasks to all workers due to data locality, etc. but in this case I think there is something else, since one slave cannot keep up with the processing rate and the total delay keeps growing: I have set up the batch interval to 1s, but each batch is processed in 1.6s so after some time the delay (and the enqueued data) is just too much. Does Spark take into consideration this time restriction on the scheduling? I mean total processing time <= batch duration. Any configuration affecting that? Am I missing something important? Any hints or things to tests? Thanks in advance! ;-) -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-scheduling-control-tp16778.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
