Guys, To change the subject from meta-voting...
We are doing Spark Streaming against a Kafka setup, everything is pretty standard, and pretty current. In particular we are using Spark 2.1, and Kafka 0.10.1, with batch windows that are quite large (5-10 minutes). The problem we are having is pretty well described in the following excerpt from the Spark documentation: "For possible kafkaParams, see Kafka consumer config docs. If your Spark batch duration is larger than the default Kafka heartbeat session timeout (30 seconds), increase heartbeat.interval.ms and session.timeout.ms appropriately. For batches larger than 5 minutes, this will require changing group.max.session.timeout.ms on the broker. Note that the example sets enable.auto.commit to false, for discussion see Storing Offsets below." In our case "group.max.session.timeout.ms" is set to default value, and our processing time per batch easily exceeds that value. I did some further hunting around and found the following SO post <http://stackoverflow.com/questions/39730126/difference-between-session-timeout-ms-and-max-poll-interval-ms-for-kafka-0-10-0> : "KIP-62, decouples heartbeats from calls to poll() via a background heartbeat thread. This, allow for a longer processing time (ie, time between two consecutive poll()) than heartbeat interval." This pretty accurately describes our scenario: effectively our per batch processing time is 2-6 minutes, well within the batch window, but in excess of the max session timeout between polls, causing the consumer to be kicked out of the group. Are there any plans to move the Kafka client up to 0.10.1 and make this feature available to consumers? Or have I missed some helpful configuration that would ameliorate this problem? I recognize changing " group.max.session.timeout.ms" is one solution, though it seems doing heartbeat checking outside of implicitly piggy backing on polling seems more elegant. -Oscar
