We want to utilize the new rebalance protocol to mitigate the stop-the-world effect during the rebalance as our tasks are long running task.
But after the upgrade when we try to kill an instance to let rebalance happen when there is some load(some are long running tasks >30S) there, the CPU will go sky-high. It reads ~700% in our metrics so there should be several threads are in a tight loop. We have several consumer threads consuming from different partitions during the rebalance. This is reproducible in both the new CooperativeStickyAssignor and old eager rebalance rebalance protocol. The difference is that with old eager rebalance rebalance protocol used the high CPU usage will dropped after the rebalance done. But when using cooperative one, it seems the consumers threads are stuck on something and couldn't finish the rebalance so the high CPU usage won't drop until we stopped our load. Also a small load without long running task also won't cause continuous high CPU usage as the rebalance can finish in that case.
"executor.kafka-consumer-executor-4" #124 daemon prio=5 os_prio=0 cpu=76853.07ms elapsed=841.16s tid=0x00007fe11f044000 nid=0x1f4 runnable [0x00007fe119aab000]"executor.kafka-consumer-executor-4" #124 daemon prio=5 os_prio=0 cpu=76853.07ms elapsed=841.16s tid=0x00007fe11f044000 nid=0x1f4 runnable [0x00007fe119aab000] java.lang.Thread.State: RUNNABLE at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator.poll(ConsumerCoordinator.java:467) at org.apache.kafka.clients.consumer.KafkaConsumer.updateAssignmentMetadataIfNeeded(KafkaConsumer.java:1275) at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1241) at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1216) at
By debugging into the code we found it looks like the clients are in a loop on finding the coordinator.
I also tried the old rebalance protocol for the new version the issue still exists but the CPU will be back to normal when the rebalance is done.
Also tried the same on the 2.4.1 which seems don't have this issue. So it seems related something changed between 2.4.1 and 2.5.0.