I'm working on a DStream application. The input are sensors' measurements, the data format is <sensor id><timestamp><measure>
There are 10 thousands sensors, and updateStateByKey is used to maintain the states of sensors, the code looks like following: val inputDStream = ... val keyedDStream = inputDStream.map(...) // use sensorId as key val stateDStream = keyedDStream.updateStateByKey[...](udpateFunction) Here's the question: In a cluster with 10 worker nodes, is it possible to partition the input dstream, so that node 1 handles sendor 0-999, node 2 handles 1000-1999, and so on? Also, is it possible to keep state stream for sensor 0 - 999 on node 1, 1000 to 1999 on node 2, and etc. Right now, I see sensor state stream is shuffled for every batch, which used lot of network bandwidth and it's unnecessary. Any suggestions? Thanks! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/how-to-setup-steady-state-stream-partitions-tp13850.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
