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Fast thread

Universal Java Thread Dump Analyzer

Ram Lakshmanan

Ram Lakshmanan

Founder & Architect, JVM Diagnostic Tools

About Ram Lakshmanan
Ram Lakshmanan is the founder and architect of popular JVM diagnostic tools: GCeasy, fastThread, HeapHero, and yCrash. Ram has a deep focus on Java performance engineering & troubleshooting. He has helped several Fortune 500 companies including Apple, Visa, ServiceNow, and Workday to diagnose and resolve complex production issues.

On this blog, Ram shares his real-world experiences, engineering challenges, and lessons from building diagnostic tools used in some of the world’s most demanding production environments. His writing combines practical advice with hands-on examples in a simple, easy-to-understand language.

When developers are stuck with mysterious OutOfMemoryError, long GC pauses, or unresponsive applications, Ram’s tools and techniques provide the clarity they need.

Tools Architected by Ram:

  • GCeasy: Analyzes Java GC logs to reduce pause times and optimize memory usage.
  • fastThread: Diagnoses thread dump issues like deadlocks, BLOCKED threads, and CPU spikes.
  • HeapHero: Visualizes heap dumps to detect memory leaks and optimize object footprint.
  • yCrash: Automates JVM root cause analysis by capturing and analyzing 360° production artifacts.

Follow Ram’s Work

Ram speaks at various developer conferences all over the world and conducts performance engineering workshops to share JVM tuning strategies and production troubleshooting techniques.

Recent Blog Posts by Ram

How to Reduce the Thread Pool Size in Java Applications

coming soon!

I Rarely Do Thread Dump Analysis Is It Worth Purchasing a  fastThread Subscription?

The article advocates for purchasing a fastThread subscription despite infrequent use, comparing its value to vital surgeries. It highlights fastThread's role in minimizing costly production downtimes, safeguarding brand reputation, facilitating swift problem resolution, and promoting sustainable stability through deep JVM analysis, ultimately enhancing operational efficiency for organizations.

Business Case for fastThread: Optimizing Java Troubleshooting and Reducing Downtime Costs

Thread dumps are crucial for diagnosing application performance issues, revealing problems like slowdowns and code bugs. Analyzing them manually is complex, but tools like fastThread streamline this process using machine learning for efficient insights. This reduces troubleshooting time, enhances security, and boosts operational efficiency, delivering significant cost savings for enterprises.

Effective Methods to Diagnose and Troubleshoot CPU Spikes in Java Applications

Java applications can have performance problems due to sudden CPU spikes. Standard monitoring tools often have trouble finding the exact code paths causing these spikes. Instead, non-intrusive methods, like analyzing threads and capturing thread dumps, give clearer insights. Looking closely at threads in the RUNNABLE state is one way to diagnose these issues without changing the live production environment.

How to Read Thread Dumps – easily & efficiently

Thread dumps are essential for finding performance issues in Java applications, like slow response times or high CPU usage. They give detailed snapshots of running threads, showing their states and stack traces. Helpful tips for analyzing thread dumps include spotting bottlenecks, deadlocks, and too many idle threads, all of which are useful for solving performance problems effectively.

How a Major Financial Institution Resolved Middleware Outage Using fastThread

A major financial institution in North America experienced serious outages in its middleware application, which is essential for banking services. The Site Reliability Engineering (SRE) team analyzed thread dumps and found that a bug in the Oracle JDBC driver was causing too many active threads. Updating to a patched driver fixed the issue, stabilized operations, and helped prevent future outages.

Want to Learn More?

Explore JVM performance training and DevOps case studies shared by Ram and the yCrash team.

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