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Grafana exporter11/25/2023 The “Topics” view provides topic-level statistics and shows activity details at the topic level.The “Brokers” drill-down panel provides more details about how your brokers are performing and includes the most important broker-level metrics.The “Cluster overview” panel provides a summary of the most important metrics at the cluster level: total brokers, cluster throughput rates, health of ksqlDB and Kafka Connect clusters, etc.Control Center functionality is focused on Kafka and event streaming, allowing operators to quickly assess cluster health and performance, create and inspect topics, set up and monitor data flows, and more. This post is the first in a series about monitoring the Confluent ecosystem by wiring up Confluent Platform with Prometheus and Grafana.īelow, you can see some examples of using Confluent Control Center. Prometheus and Grafana, on the other hand, provide a playground for creating dashboards pertaining to ad hoc needs, with aggregations from various systems. Monitoring Your Event Streams: Tutorial for Observability Into Apache Kafka ClientsĬonfluent Control Center provides a UI with “most important” metrics and allows teams to quickly understand and alert on what’s going on with the clusters.Monitoring Your Event Streams: Integrating Confluent with Prometheus and Grafana (this article).Then this two-part blog series is for you: Can I configure some Grafana dashboards for Confluent clusters?.What if I could correlate this service’s data spike with metrics from Confluent clusters in a single UI pane?. Can I export JMX data from Confluent clusters to my monitoring system with minimal configuration?.If you’ve ever asked a question along these lines: However, sometimes, you might also like to import monitoring data into a third-party metrics aggregation platform for service correlations, consolidated dashboards, root cause analysis, or more fine-grained alerts. That’s why operators prefer tooling such as Confluent Control Center for administering and monitoring their deployments. Self-managing a highly scalable distributed system with Apache Kafka ® at its core is not an easy feat.
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