In this new blog article, we’ll look at the latest version of MirrorMaker 2.0, which includes Apache Kafka cross-cluster mirroring details and the five compelling reasons to use MirrorMaker 2.0 for data replication. MirrorMaker 2.0 is an Apache Kafka-based data replication tool. It serves as a consumer and producer for many Kafka clusters, allowing users to replicate data from one cluster to another quickly and reliably. Kafka-centric architectures become more resilient as a result of this.
MirrorMaker 2.0 (KIP-382) Fixes Mirrormaker 1’s Flaws
MirrorMaker 2.0 offers a fundamental shift in the way you synchronize data between replicated Kafka clusters, providing a more dynamic and automated approach to cluster-to-cluster topic replication. MM2 (KIP-382) addresses the shortcomings of MirrorMaker 1.0 by allowing users to modify configurations dynamically, keep topic attributes in sync across clusters, and considerably increase performance by minimizing rebalances. Internally, MM2 uses the Kafka link architecture to replicate topics from one cluster to another in real-time. A Kafka cluster includes options for auto load balancing and leader election to securely replicate individual topics among brokers and maintain data intact and available in the event of internal node failure.
MirrorMaker’s earlier version relied on the setting of a source consumer/target producer pair to synchronize Kafka clusters. MirrorMaker 2.0, on the other hand, is built on Kafka Connect, which is a game-changer. To begin, there is no longer any need to set up producers and consumers in order to connect. You just need to identify your source and destination clusters when using MirrorMaker 2.0. You next configure and install MirrorMaker 2.0 to link the two clusters.
Five Powerful Reasons To Mirror Your Kafka Cluster Data
Data & Disaster Recovery
Organizations are increasingly turning to Kafka for mission-critical use-cases requiring high availability and quick recovery rates. Enterprise operators, in particular, require the flexibility to transfer applications between clusters quickly in order to preserve business continuity during outages. In many circumstances, records that are out of order or absent are completely unacceptable. MirrorMaker is a popular tool for duplicating topics between clusters, however, it has been shown to be insufficient in big business multi-cluster systems. MirrorMaker 2.0 is a brand-new replication engine created specifically for Kafka disaster recovery and high availability to fulfill the demands of Kafka developers and consumers.
Cross-Cluster Guaranteed To Happen Only Once
Although Kafka supports exactly-once processing, this assurance applies only within a single Kafka cluster and not across multiple clusters. The exactly-once support within a Kafka cluster cannot be used directly by cross-cluster replication. This means that when duplicating data between source and target clusters, MM2 can only guarantee at least once semantics, implying that duplicate records may exist downstream.
Syncs Topic Configuration Between Clusters Automatically
When utilizing MirrorMaker 1, a new or existing topic from the source cluster is automatically produced at the destination cluster, either by the Kafka broker directly if auto.create.topics is enabled, or by MirrorMaker improvements using the Kafka admin client API. The issue arises from the topic’s setup at the destination. Because it relies on cluster defaults at the destination, MMv1 does not guarantee that the topic attributes from the source will be preserved, whereas MirrorMaker 2.0 does.
Similarly, the replication factor on the destination cluster might be different, affecting the availability assurances of the replicated data. Any dynamic changes to the topic attributes will not be immediately reflected, even if the initial topic setup was replicated by an admin. These distinctions can turn into a headache. Thanks to MM2, which seamlessly synchronizes topic settings between clusters.
Internally, MirrorMaker employs a high-level consumer to get data from the source cluster, where partitions are assigned to consumers within a consumer group by a group coordinator. When there is a change in topics, such as when a new topic is created or an old topic is deleted, or when MirrorMaker is bounced back, it causes a consumer rebalance, which halts the mirroring process, creates a backlog in the pipeline and increases the end to end latency observed by the downstream application. Such incessant problems contradict any latency-driven SLAs that a service based on mirrored pipeline must provide. MirrorMaker 2.0 prevents latency spikes and thus offers no rebalancing.
A High-Level Driver
The MirrorMaker.java driver class and ./bin/connect-mirror-maker.sh script create a distributed MM2 cluster that isn’t dependent on an existing Connect cluster. MirrorMaker 2.0 cluster nodes, on the other hand, manage Connect workers internally using a high-level configuration file. Each Kafka cluster must be identified using the configuration file. As a result, MM2 makes use of a high-level driver that uses the mm2.properties configuration file to produce a set of Connect workers. In short, it handles connectors in a dedicated cluster.
In A Nutshell,
Apache Kafka MirrorMaker 2.0 creates a strong replication infrastructure that can be used for a variety of applications. The best part is that you don’t have to do anything yourself. Ksolves is ready to assist you in fully using the potential of MirrorMaker 2 for your business at a low cost.
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