Hi All, I am porting a machine learning application on Hadoop using MapReduce. The architecture of the application goes like this: 1. run a number of server processes which take around 2-3 minutes to start and then remain as daemon waiting for a client to call for a connection. During the startup these server processes get trained on the trainng dataset.
2. A client is then run which connects to servers and process or test any data that it wants to. The client is basically our job, which we will be converted to the mapreduce model of hadoop. Now, since each server takes a good amount of time to start, needless to say that we want each of these server processes to be pre-running on all the tasktrackers(all nodes) so that when a mapreduce(client) task come to that node, the servers are already running and the client just uses them for its purpose. The server process keeps on running waiting for another map task that may be assigned to that node. That means, a server process is started on each node once and it waits for a connection by a client. When clients( implemeted as map reduce) come to that node they connect to the server, do they their processing and leave(or finish). Can you please tell me how should I go about starting the server on each node. If I am not clear, please ask any questions. Any help in this regard will be greatly appreciated. Thank You! Akhil -- View this message in context: http://www.nabble.com/Implementing-CLient-Server-architecture-using-MapReduce-tp23916757p23916757.html Sent from the Hadoop core-user mailing list archive at Nabble.com.
