22 posts tagged with "Replication"

Kafka Replicator is Confluent's Kafka Connect connector for asynchronously copying selected topics from one Kafka cluster to another. It can preserve topic settings, translate consumer positions, and fit into an existing Confluent Platform operating model.

Replicator keeps a second Kafka cluster close to the source's current state. It does not create historical restore points, so it cannot recover the state before a bad write or deletion.

A Kafka MirrorMaker 2 setup needs two cluster aliases, a directional flow, topic and group filters, and a running dedicated Connect process. You must then verify remote topics, copied records, translated consumer offsets, and measured replication delay.

MirrorMaker 2 provides asynchronous cross-cluster replication. It does not keep a historical restore point, and a running connector does not prove that an application can fail over safely.

Kafka replication factor is the number of brokers that store each topic partition. A factor of three gives each partition one leader replica and two follower replicas. Kafka can then keep serving the partition when one broker fails, provided another eligible replica is current.

More replicas improve tolerance for broker and disk failure. They also consume more storage, network bandwidth, and recovery capacity. The right value depends on your failure domains and durability target, not a universal rule.

Kafka MirrorMaker 2 is Apache Kafka's framework for copying records and selected metadata between independent Kafka clusters. It runs on Kafka Connect, reads from a source cluster, and writes to a target cluster through a directional replication flow.

MirrorMaker helps with regional availability, cluster migration, and centralized data access. It does not create an independent historical backup, and it cannot restore a topic to a time before a bad record or deletion.

Kafka replication stores copies of each partition on multiple brokers. One replica leads reads and writes while followers copy its ordered log and stand ready to take over. This design keeps partitions available through many broker failures. It does not create a historical backup or protect every copy from a bad delete.

Theory says your Kafka DR plan will work. Production has other ideas. These Kafka disaster recovery stories walk through four failures that actually happen — a region outage, an accidental topic deletion, a multi-datacenter partition, and a Kubernetes broker cascade — and the choices that decided whether recovery took minutes or hours. Learning from someone else's outage is cheaper than living through your own.

MirrorMaker 2 is the standard tool for Kafka cross-cluster replication, but the default configuration is rarely production-ready. These Kafka MirrorMaker best practices close the gap between a working tutorial and a replication flow you can trust for disaster recovery and migration. The short version: match tasks to partitions, sync offsets and configs, alert on lag before it hurts, and never run MirrorMaker on your broker nodes.

Choosing the right Kafka backup tool is the difference between a five-minute recovery and a five-hour scramble. There is no single winner. Each tool solves a specific problem: MirrorMaker 2 and MSK Replicator keep a warm cluster for regional failover, Kafka Connect S3 Sink archives topics to cheap object storage, and point-in-time backup restores a topic to the moment before a bad deploy. This guide compares all four, shows where each fits, and gives you a decision matrix so you can match tools to requirements instead of the other way around.

MSK Replicator is a fully managed feature of Amazon MSK that copies topic data, consumer group offsets, and topic configurations between MSK clusters — across regions or across accounts — without you running any replication infrastructure. AWS manages the brokers, but cross-region disaster recovery for your streams is still your responsibility. MSK Replicator closes that gap. This guide covers its architecture, how to set it up for cross-region DR, failover procedures, and when to choose it over MirrorMaker 2.

Kafka disaster recovery is the practice of keeping a second, independent copy of your streaming data and cluster metadata so you can resume service after a region outage, a bad deploy, or human error. Kafka's built-in replication — replication factor 3 with min.insync.replicas=2 — survives broker failure, but it does not survive a lost region or a deleted topic. The right DR design depends on three numbers: your recovery time objective (RTO), your recovery point objective (RPO), and your budget. This guide compares active-passive and active-active architectures and gives you a framework to choose.