Yes, hostname is enough. I think currently it is hard for user code to get the worker list from standalone master. If you can get the Master object, you could get the worker list, but AFAIK may be it is difficult to get this object. All you could do is to manually get the worker list and assigned its hostname to each receiver.
Thanks Jerry From: Du Li [mailto:l...@yahoo-inc.com] Sent: Thursday, March 5, 2015 2:29 PM To: Shao, Saisai; User Subject: Re: distribution of receivers in spark streaming Hi Jerry, Thanks for your response. Is there a way to get the list of currently registered/live workers? Even in order to provide preferredLocation, it would be safer to know which workers are active. Guess I only need to provide the hostname, right? Thanks, Du On Wednesday, March 4, 2015 10:08 PM, "Shao, Saisai" <saisai.s...@intel.com<mailto:saisai.s...@intel.com>> wrote: Hi Du, You could try to sleep for several seconds after creating streaming context to let all the executors registered, then all the receivers can distribute to the nodes more evenly. Also setting locality is another way as you mentioned. Thanks Jerry From: Du Li [mailto:l...@yahoo-inc.com.INVALID] Sent: Thursday, March 5, 2015 1:50 PM To: User Subject: Re: distribution of receivers in spark streaming Figured it out: I need to override method preferredLocation() in MyReceiver class. On Wednesday, March 4, 2015 3:35 PM, Du Li <l...@yahoo-inc.com.INVALID<mailto:l...@yahoo-inc.com.INVALID>> wrote: Hi, I have a set of machines (say 5) and want to evenly launch a number (say 8) of kafka receivers on those machines. In my code I did something like the following, as suggested in the spark docs: val streams = (1 to numReceivers).map(_ => ssc.receiverStream(new MyKafkaReceiver())) ssc.union(streams) However, from the spark UI, I saw that some machines are not running any instance of the receiver while some get three. The mapping changed every time the system was restarted. This impacts the receiving and also the processing speeds. I wonder if it's possible to control/suggest the distribution so that it would be more even. How is the decision made in spark? Thanks, Du
