Performance tests often show healthy response times, throughput, CPU utilization, and memory usage. Yet, production issues can still emerge after an application is deployed. In many cases, the early warning signs were already present during performance testing but remained unnoticed because they were too subtle to stand out in broad performance metrics.
In this webinar, Ram Lakshmanan explores how Java Flight Recorder (JFR) and AI can help engineers identify these early signals before they develop into production incidents. The session focuses on capturing fine-grained JVM activity, analyzing JFR recordings, and correlating performance signals to uncover deviations that conventional metrics and static thresholds may miss.
Attendees will learn how to move beyond high-level performance indicators and adopt a more detailed, signal-based approach to performance analysis.
Why Java Performance Testing Alone Cannot Prevent Production Issues
Performance testing is essential for validating application behavior under load, but healthy aggregate metrics do not always mean an application is free from underlying performance problems. Subtle changes in CPU activity, memory behavior, thread execution, or I/O can remain hidden until workloads or operating conditions change in production.
Java Flight Recorder provides detailed runtime data that helps engineers investigate these behaviors. When combined with AI-assisted analysis, JFR recordings can help correlate multiple signals, identify unusual patterns, and narrow down potential performance problems earlier in the development and testing lifecycle.
Recognizing these signals in the performance lab gives engineering teams an opportunity to investigate and address potential issues before they affect production users.
Key Takeaways
- Identify subtle performance signals during testing that may indicate future production problems.
- Understand how Java Flight Recorder captures fine-grained JVM activity beyond broad performance metrics.
- Investigate deviations in CPU, memory, threads, I/O, and other JVM activities using JFR recordings.
- Explore how AI can correlate multiple performance signals to identify potential issues.
- Move beyond static thresholds toward signal-based performance analysis.
- Learn how earlier detection can help reduce the risk of performance problems reaching production.
Slide Deck
Want to revisit the concepts discussed during the webinar? You can access the presentation slides below.
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Webinar Recording
Watch the webinar recording to learn how JFR and AI can help uncover subtle performance signals, correlate JVM activity, and identify potential production issues during performance testing.
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Q&A Session
The session includes a live Q&A with Ram Lakshmanan, covering audience questions and real-world performance challenges related to JFR, JVM behavior, and early detection of production issues.
Coming soon…
Stay Tuned
Identifying performance problems early requires more than monitoring response times and resource utilization. Understanding what happens inside the JVM can help engineering teams investigate performance deviations before they affect production.
Stay tuned for more technical sessions from the yCrash webinar series, featuring practical insights into Java performance troubleshooting, JVM diagnostics, and production reliability.
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