Details
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Improvement
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Status: Resolved
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Minor
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Resolution: Fixed
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Description
The existing controller code updates metrics after processing every event. This can slow down event processing on the controller tremendously. In one profiling we see that updating metrics takes nearly 100% of the CPU for the controller event processing thread. Specifically the slowness can be attributed to two factors:
1. Each invocation to update the metrics is expensive. Specifically trying to calculate the offline partitions count requires iterating through all the partitions in the cluster to check if the partition is offline; and calculating the preferred replica imbalance count requires iterating through all the partitions in the cluster to check if a partition has a leader other than the preferred leader. In a large cluster, the number of partitions can be quite large, all seen by the controller. Even if the time spent to check a single partition is small, the accumulation effect of so many partitions in the cluster can make the invocation to update metrics quite expensive. One might argue that maybe the logic for processing each single partition is not optimized, we checked the CPU percentage of leaf nodes in the profiling result, and found that inside the loops of collection objects, e.g. the set of all partitions, no single function dominates the processing. Hence the large number of the partitions in a cluster is the main contributor to the slowness of one invocation to update the metrics.
2. The invocation to update metrics is called many times when the is a high number of events to be processed by the controller, one invocation after processing any event.
This ticket is used to track how we change the logic for the OfflinePartitionCount metric.
And KAFKA-7350 will be used to track progress for the PreferredReplicaImbalanceCount metric.
