How does this skill help me identify performance bottlenecks more efficiently than manual tools?

This skill automates the execution and analysis of Java Flight Recorder (JFR) sessions, transforming raw binary profiling data into actionable insights about which specific code methods are consuming the most CPU cycles.

What are the technical prerequisites for running javaperf cpu hotspots?

The skill requires a host environment with JDK 8u262+ or 11+, Node.js 18+, and the javaperf MCP server installed to bridge the AI agent with your local JVM processes.

Can I use this skill to troubleshoot live production services?

Yes, by utilizing Java Flight Recorder's low-overhead sampling, this skill can safely profile production services to diagnose high CPU usage or latency spikes with minimal impact on application performance.

Does this skill also analyze memory leaks or thread synchronization issues?

While this skill is purpose-built for CPU execution profiling and call-path analysis, it is not designed to detect memory leaks, heap issues, or thread deadlocks.

What is included in the purchase of this skill?

You receive the specialized AI agent skill configuration and instructions necessary to orchestrate JVM discovery, JFR data parsing, and automated diagnostic reporting via the javaperf MCP server.

javaperf cpu hotspots

I have recorded a 60-second JFR profile for PID 4521. The recording captured 5,842 CPU samples during the load window. The analysis shows the service is heavily CPU-bound, primarily due to intensive JSON serialization and regex matching in the request processing layer.

CPU Hot Spot Analysis

Rank	Method	% Samples	Call Path	Likely Cause
1	`com.auth.Token.verify()`	42.1%	`AuthFilter` -> `JWT.parse`	Re-compiling Regex pattern on every call.
2	`com.data.Mapper.toJson()`	28.4%	`ApiController` -> `Response`	Reflective access in legacy Jackson version.
3	`java.util.regex.Pattern.matcher`	12.8%	`Token.verify`	Side effect of Rank 1 method.
4	`zip.Deflater.deflateBytes`	7.2%	`GzipFilter`	High compression level (9) on small payloads.

Verdict: The service is compute-bound. 70% of CPU time is spent in two specific methods related to security and serialization.

Fixes to try:

Cache Regex Patterns: Move the Pattern.compile call in Token.verify() to a static final constant.
Upgrade Jackson: Update jackson-databind to 2.15+ to benefit from improved property cache lookups.
Adjust GZIP: Lower the compression level to 6 or implement a payload size threshold for compression.

Next steps

Apply the static Regex fix and re-run the profile.
Verify if the com.data.Mapper can be replaced with a pre-generated serializer.
Check compiler_codecache if performance stays inconsistent after fixes.

About this skill

Finds CPU hot spots in a running Java service by recording JFR under load and ranking the hottest methods and call paths via the javaperf MCP server. Use when the user says the Java backend is slow, has high CPU or latency, wants to find hot spots during a load test, or asks which methods burn the most CPU.

Frequently Asked Questions

javaperf cpu hotspots