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• Separate Thread Pools: Cross-cluster fetcher threads run in a dedicated thread pool, which is independent from the intra-cluster fetcher thread pool. This separation ensures that cross-cluster replication latency does not impact local replica synchronization.
• Network I/O Overhead: Read-only leaders perform additional network I/O to fetch from source clusters. This overhead is proportional to the number of mirror partitions and the replication throughput. Brokers with many mirror partitions may experience increased CPU usage for network processing and data serialization.
• Memory Footprint: Each mirror fetcher thread maintains its own fetch session state, partition state map, and response buffers. With default configuration, memory overhead is comparable to standard replica fetchers. The metadata manager maintains connection pools and metadata caches, adding minimal memory overhead.
• Bandwidth Consumption: Cross-cluster traffic between source and destination clusters consumes WAN bandwidth. For large-scale deployments, administrators should provision adequate inter-datacenter connectivity or configure throttling.
• State Management: Mirror partition state management is evenly distributed to available brokers to avoid any hot spot, especially during rolling update or restart events.

Future Work

Sync Synchronous mirroring: Currently, mirroring is asynchronous. The source cluster acknowledges the producer without waiting for the destination to replicate the data. Sync mirroring would guarantee that records are replicated to the destination cluster before the source acknowledges the produce request, providing stronger durability guarantees at the cost of higher latency. This would be useful for workloads where zero data loss across clusters is a strict requirement.

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