Page History

...

package org.apache.kafka.streams.processor.assignment;

public class AssignmentConfigs {
    public long acceptableRecoveryLag();
    public int maxWarmupReplicas();
    public int numStandbyReplicas();
    public long probingRebalanceIntervalMs();
    public List<String> rackAwareAssignmentTags();
    public intOptional<Integer> trafficCost();
    public intOptional<Integer> nonOverlapCost();
    public String rackAwareAssignmentStrategy();
 }

Finally, as part of this change, we're moving some of the behavior that can fail into the task assignor. In particular, we're moving the bits that compute lags for stateful tasks into the implementation of ApplicationState#kafkaStreamsStates . Users who request the task lags via the computeTaskLags  input flag should make sure to handle failures the way they desire, and can rethrow a thrown TaskAssignmentException  (or just not catch it in the first place) to have Kafka Streams automatically "retry" the rebalance by returning the same assignment and scheduling an immediate followup rebalance. Advanced users who want more control over the "fallback" assignment and/or the timing of immediate followup rebalance(s) can simply swallow the TaskAssignmentException  and use the followupRebalanceDeadline  to schedule followup rebalances, eg to implement a retry/backoff policy

...