Currently when expanding the KS cluster, the new node's partitions will be unavailable during the rebalance, which for large states can take a very long time, or for small state stores even more than a few ms can be a deal-breaker for micro service use cases.
One workaround is to allow stale data to be read from the state stores when use case allows. Adding the use case from
KAFKA-8994 as it is more descriptive.
"Consider the following scenario in a three node Streams cluster with node A, node S and node R, executing a stateful sub-topology/topic group with 1 partition and `num.standby.replicas=1`
- t0: A is the active instance owning the partition, B is the standby that keeps replicating the A's state into its local disk, R just routes streams IQs to active instance using StreamsMetadata
- t1: IQs pick node R as router, R forwards query to A, A responds back to R which reverse forwards back the results.
- t2: Active A instance is killed and rebalance begins. IQs start failing to A
- t3: Rebalance assignment happens and standby B is now promoted as active instance. IQs continue to fail
- t4: B fully catches up to changelog tail and rewinds offsets to A's last commit position, IQs continue to fail
- t5: IQs to R, get routed to B, which is now ready to serve results. IQs start succeeding again
Depending on Kafka consumer group session/heartbeat timeouts, step t2,t3 can take few seconds (~10 seconds based on defaults values). Depending on how laggy the standby B was prior to A being killed, t4 can take few seconds-minutes.
While this behavior favors consistency over availability at all times, the long unavailability window might be undesirable for certain classes of applications (e.g simple caches or dashboards).
This issue aims to also expose information about standby B to R, during each rebalance such that the queries can be routed by an application to a standby to serve stale reads, choosing availability over consistency."