Ensure reliable big data operations
Apache HBase is a distributed, scalable NoSQL database built on the Hadoop ecosystem, designed for real-time read and write access to massive datasets. It organizes data into tables, rows, and column families, delivering fast, consistent, low-latency performance for use cases like analytics, messaging, and time-series data. Monitoring HBase is essential to maintain cluster stability, optimize resource usage, and detect issues early. By tracking metrics such as region server health, memory utilization, and Regions-In-Transition (RIT), organizations can avoid downtime, improve responsiveness, and ensure their big data applications run efficiently at scale.
Monitor the health and performance of your HBase clusters
Track overall cluster health with metrics like active and dead region servers, cluster requests, region splits and merges, and RIT to ensure smooth operations.
Monitor resource and process efficiency by analyzing memory usage, JVM health, IPC performance, and HLog split stats to avoid performance bottlenecks.
Analyze HBase logs to identify issues, optimize performance, and gain deeper visibility into cluster activity.
Set threshold-based alerts for spikes in RITs, memory usage, IPC latency, or dead region servers so you can resolve issues before they impact applications.
Visualize all HBase metrics in custom dashboards, alongside other ecosystem components, to gain end-to-end visibility and manage your big data infrastructure proactively.
Use IT automation to recover unresponsive HBase resources quickly, minimizing downtime and improving reliability.
