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At the time of writing, the Diskless Topics KIP (KIP-1500 and other sub-KIPs) are still under discussion, so there will be future KIPs to support this feature.

Active-Active Writes

Active-active topology is not initially supported in Cluster Mirroring, though it could potentially be achieved through topic prefixing and removing the reliance on topic ID for mirroring. This is a candidate for a future improvement KIP. 

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Command workflows

Public Interfaces

Command-Line

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• Performance Benchmark: Measure replication throughput and latency across WAN.
• Scalability Test: Replicate 1000 topics with 100,000 partitions across clusters.
• Failover Test: Simulate source cluster failure, measure consumer recovery time.
• Long-Running Stability: Run continuous replication for 7 days, verify no memory leaks or performance degradation.
• Security Validation: Test all authentication mechanisms (SASL PLAIN, SCRAM, Kerberos, mTLS) via kafka-mirrors.sh config files.

Future Work

Active-Active Writes

Active-active topology is not initially supported in Cluster Mirroring, though it could potentially be achieved through topic prefixing and removing the reliance on topic ID for mirroring. This is a candidate for a future improvement KIP. 

Instead, bidirectional mirroring is supported, but only when mirroring different topics between clusters, allowing records produced to either cluster to be consumed from both. Unlike MirrorMaker 2, Cluster Mirroring does not need special cycle detection or prevention logic because the read-only enforcement inherently blocks the conditions that would create infinite replication loops.

Synchronous Mirroring

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.

Future extensions to synchronous mirroring could enable preservation of transactional semantics across clusters. Streaming platforms using exactly-once mode (Apache Kafka Streams, Apache Flink, Apache Spark) rely on the source cluster's transactional protocol and coordination. During failover or migration scenarios, transactional metadata for pending transactions does not transfer to the destination cluster, potentially breaking exactly-once guarantees. Supporting transactional cross-cluster replication would require coordinating transactional metadata and ensuring transaction state consistency across clusters—something MM2's Connect-based architecture cannot support.

Tiered Storage

storage: Mirror topics in the destination cluster currently only replicate data from local storage on the source broker. Integrating with tiered storage would allow mirroring to handle data that has been offloaded to remote storage (e.g., S3, HDFS), enabling full replication of topics with long retention periods without requiring all data to reside in local broker storage.

Diskless Topics

topics: Diskless topics store data exclusively in tiered storage, with no local log segments on brokers. Supporting mirroring for diskless topics requires adapting the fetch and replication mechanisms to work without local storage, which introduces changes to how mirror offsets are tracked and how truncation is handled during failover.

Source Quotas

cluster mirroring replication quota:Add source-side throttling that allows source cluster leaders to limit bandwidth served to all mirror fetchers, similar to how how leader.replication.throttled.rate controls intra-cluster replication. This provides independent control over mirror catch-up traffic without impacting local replication or consumer workloads. Combined with destination-side throttling, operators gain complete bidirectional bandwidth control for mirror traffic.

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