FranzView offers a high level overview of your most critical metrics
Kafka clusters provide numerous metrics to monitor cluster health. FranzView puts important application metrics front and center to alert developers of problems with cluster health. Time series data is represented graphically for metrics that are important for debugging. CPU and Disk usage trends to ensure your cluster has sufficient system resources
Monitoring CPU and Disk usage trends enable developers to ensure clusters have sufficient system resources to meet service-level objectives.
Changes in underreplicated partition number, active controller count, and offline partitions are useful application health checks to immediately assess cluster health.
ASSESS
While also allowing you to see specific metrics on a per broker and per topic basis
FranzView displays real-time per broker stats to measure message processing and gauge critical service-level indicators: latency and throughput.
A convenient search bar lets the user filter to see only the performance of a single broker as a useful measure of the traffic each broker is receiving from producing clients.
The TotalTimeMs metric family provides insight into the total time a message spends in the request queue, being processed by the leader, awaiting a response from another broker following the data partition of the message, and the time required for a response.
MANAGE
Once you know how your cluster is performing franzView helps you to easily make changes
FranzView provides tools for all the common topic administration tasks: listing, describing, creating and deleting topics.
A feature-rich data-grid allows for users to filter topics on key metrics such as minimum in-sync replicas (Under Min ISR). Out of sync replicas reduce cluster reliability and could result in data loss, so this is a value that kafka managers will want to watch.
To horizontally scale a topic, a Kafka manager may choose to reassign partitions across brokers. The topic grid provides easy access to commands to reassign or delete partitions to manage cluster resources.
