Lucas Bradstreet created KAFKA-9048:
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Summary: Improve partition scalability in replica fetcher
Key: KAFKA-9048
URL: https://issues.apache.org/jira/browse/KAFKA-9048
Project: Kafka
Issue Type: Task
Components: core
Reporter: Lucas Bradstreet
https://issues.apache.org/jira/browse/KAFKA-9039
([https://github.com/apache/kafka/pull/7443]) improves the performance of the
replica fetcher (at both small and large numbers of partitions), but it does
not improve its complexity or scalability in the number of partitions.
I took a profile using async-profiler for the 1000 partition JMH replica
fetcher benchmark. The big remaining culprits are:
* ~18% looking up logStartOffset
* ~45% FetchSessionHandler$Builder.add
* ~19% FetchSessionHandler$Builder.build
*Suggestions*
# The logStartOffset is looked up for every partition on each doWork pass.
This requires a hashmap lookup even though the logStartOffset changes rarely.
If the replica fetcher could be notified of updates to the logStartOffset, then
we could reduce the overhead to a function of the number of updates to the
logStartOffset instead of O(n) on each pass.
# The use of FetchSessionHandler means that we maintain a partitionStates
hashmap in the replica fetcher, and a sessionPartitions hashmap in the
FetchSessionHandler. On each incremental fetch session pass, we need to
reconcile these two hashmaps to determine which partitions were added/updated
and which partitions were removed. This reconciliation process is especially
expensive, requiring multiple passes over the fetching partitions, and hashmap
remove and puts for most partitions. The replica fetcher could be smarter by
maintaining the fetch session *updated* hashmap containing
FetchRequest.PartitionData(s) directly, as well as *removed* partitions list so
that these do not need to be generated by reconciled on each fetch pass.
# maybeTruncate requires an O(n) pass over the elements in partitionStates
even if there are no partitions in truncating state. If we can maintain some
additional state about whether truncating partitions exist in partitionStates,
or if we could separate these states into a separate data structure, we would
not need to iterate across all partitions on every doWork pass. I’ve seen
clusters where this work takes about 0.5%-1% of CPU, which is minor but will
become more substantial as the number of partitions increases.
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