Ensure reliable stream and batch data processing
Apache Flink is a powerful, distributed stream-processing framework designed for real-time and batch data workloads. It excels at high-throughput, low-latency data processing and supports event-time semantics, fault tolerance, and stateful computations at scale. From financial transactions to log analytics and IoT pipelines, Flink is built to deliver reliable, near-instant data insights.
Monitoring Flink is crucial to keep streaming applications responsive, ensure jobs are executed efficiently, and maintain overall cluster stability. By tracking key metrics like job states, task performance, TaskManager resources, and JobManager configurations, organizations can detect bottlenecks early, optimize execution, and ensure uninterrupted processing of continuous data flows.
Monitor the health and performance of your Flink clusters
Track overall job and task performance with metrics like total jobs, running tasks, finished tasks, failed tasks, and canceled tasks to ensure stable execution.
Monitor TaskManager efficiency by analyzing memory usage, task slots, process size, and bind hosts for optimal workload distribution.
Gain visibility into JobManager performance by monitoring execution failover strategy, RPC address/port, memory (heap, off-heap, metaspace, overhead), and process size.
Set alerts for abnormal states such as task failures, high memory usage, or slow scheduling to detect problems before they affect critical data pipelines.
Visualize Flink metrics in custom dashboards, along with Hadoop, Kafka, and ecosystem components, for full-stack observability.
Automate recovery actions to handle failed tasks or unresponsive managers, minimizing downtime and improving data reliability.
