java-profiling
|
Works with
---
name: java-profiling
description: |
license: MIT
---
# Java/JVM Performance Profiling
## When NOT to Use This Skill
- **Node.js/JavaScript profiling** - Use the `nodejs-profiling` skill for V8 profiler and heap analysis
- **Python profiling** - Use the `python-profiling` skill for cProfile and tracemalloc
- **Application-level optimization** - This is for JVM-level profiling, not algorithm optimization
- **Database query tuning** - Use database-specific profiling tools
- **Frontend performance** - Use browser DevTools for client-side profiling
> **Deep Knowledge**: Use `mcp__documentation__fetch_docs` with technology: `java` for comprehensive JFR configuration, GC tuning, and JVM diagnostics.
## Java Flight Recorder (JFR)
### Starting JFR
```bash
# Start recording with application
java -XX:+FlightRecorder \
-XX:StartFlightRecording=duration=60s,filename=recording.jfr \
-jar app.jar
# Start recording on running JVM
jcmd <pid> JFR.start duration=60s filename=recording.jfr
# Continuous recording (always-on)
java -XX:+FlightRecorder \
-XX:FlightRecorderOptions=stackdepth=256 \
-XX:StartFlightRecording=disk=true,maxsize=500m,maxage=1d \
-jar app.jar
# Dump current recording
jcmd <pid> JFR.dump filename=dump.jfr
```
### JFR Configuration
```
# custom.jfc
<?xml version="1.0" encoding="UTF-8"?>
<configuration version="2.0">
<event name="jdk.CPULoad">
<setting name="enabled">true</setting>
<setting name="period">1 s</setting>
</event>
<event name="jdk.GCHeapSummary">
<setting name="enabled">true</setting>
</event>
<event name="jdk.ObjectAllocationInNewTLAB">
<setting name="enabled">true</setting>
<setting name="stackTrace">true</setting>
</event>
</configuration>
```
### Analyzing JFR Files
```bash
# Print JFR summary
jfr summary recording.jfr
# Print specific events
jfr print --events jdk.CPULoad recording.jfr
jfr print --events jdk.ExecutionSample --json recording.jfr
# Export to JSON
jfr print --json recording.jfr > recording.json
```
## jcmd Diagnostics
### Process Information
```bash
# List all Java processes
jcmd
# VM info
jcmd <pid> VM.version
jcmd <pid> VM.flags
jcmd <pid> VM.system_properties
jcmd <pid> VM.command_line
# Thread dump
jcmd <pid> Thread.print
# Heap info
jcmd <pid> GC.heap_info
# Class histogram
jcmd <pid> GC.class_histogram
```
### Memory Analysis
```bash
# Native memory tracking (requires -XX:NativeMemoryTracking=summary)
jcmd <pid> VM.native_memory summary
# Heap dump
jcmd <pid> GC.heap_dump /path/to/dump.hprof
# Force GC
jcmd <pid> GC.run
```
## GC Tuning
### GC Selection
```bash
# G1GC (default in JDK 9+, recommended for heap > 4GB)
java -XX:+UseG1GC -jar app.jar
# ZGC (low latency, JDK 15+)
java -XX:+UseZGC -jar app.jar
# Shenandoah (low latency, OpenJDK)
java -XX:+UseShenandoahGC -jar app.jar
# Parallel GC (throughput)
java -XX:+UseParallelGC -jar app.jar
```
### GC Logging
```bash
# JDK 9+ unified logging
java -Xlog:gc*:file=gc.log:time,uptime,level,tags:filecount=5,filesize=10m \
-jar app.jar
# Common GC flags
java -XX:+PrintGCDetails \
-XX:+PrintGCDateStamps \
-XX:+PrintTenuringDistribution \
-Xloggc:gc.log \
-jar app.jar
```
### G1GC Tuning
```bash
java -XX:+UseG1GC \
-XX:MaxGCPauseMillis=200 \ # Target pause time
-XX:G1HeapRegionSize=16m \ # Region size
-XX:InitiatingHeapOccupancyPercent=45 \ # Start marking at 45%
-XX:G1ReservePercent=10 \ # Reserve for promotions
-XX:ConcGCThreads=4 \ # Concurrent GC threads
-XX:ParallelGCThreads=8 \ # Parallel GC threads
-jar app.jar
```
## Memory Optimization
### Heap Sizing
```bash
# Set heap size
java -Xms4g -Xmx4g -jar app.jar # Fixed heap (recommended for production)
# Metaspace sizing
java -XX:MetaspaceSize=256m -XX:MaxMetaspaceSize=512m -jar app.jar
# Direct memory
java -XX:MaxDirectMemorySize=256m -jar app.jar
```
### Memory Leak Detection
```java
// Common leak patterns
// ❌ Bad: Static collections that grow
private static List<Object> cache = new ArrayList<>();
public void process(Object obj) {
cache.add(obj); // Never removed
}
// ✅ Good: Bounded cache with eviction
private static final Cache<String, Object> cache = Caffeine.newBuilder()
.maximumSize(10_000)
.expireAfterWrite(Duration.ofMinutes(10))
.build();
// ❌ Bad: Unclosed resources
public void readFile(String path) {
InputStream is = new FileInputStream(path);
// Missing is.close()
}
// ✅ Good: Try-with-resources
public void readFile(String path) {
try (InputStream is = new FileInputStream(path)) {
// Process
}
}
// ❌ Bad: ThreadLocal not cleaned
private static ThreadLocal<Connection> connHolder = new ThreadLocal<>();
public void process() {
connHolder.set(getConnection());
// Missing connHolder.remove()
}
// ✅ Good: Always clean ThreadLocal
public void process() {
try {
connHolder.set(getConnection());
// Process
} finally {
connHolder.remove();
}
}
```
## CPU Profiling
### async-profiler (recommended)
```bash
# Profile CPU
./profiler.sh -d 30 -f profile.html <pid>
# Profile allocations
./profiler.sh -d 30 -e alloc -f alloc.html <pid>
# Profile locks
./profiler.sh -d 30 -e lock -f lock.html <pid>
# Flame graph output
./profiler.sh -d 30 -f flamegraph.html -o flamegraph <pid>
```
### JMC (Java Mission Control)
```bash
# Open JFR recording in JMC
jmc recording.jfr
```
## Common Bottleneck Patterns
### Synchronization Issues
```java
// ❌ Bad: Coarse-grained locking
public synchronized void process(String key, Object value) {
cache.put(key, value);
compute(value);
}
// ✅ Good: Fine-grained locking
private final ConcurrentHashMap<String, Object> cache = new ConcurrentHashMap<>();
public void process(String key, Object value) {
cache.put(key, value); // Lock-free for different keys
compute(value);
}
// ✅ Good: Read-write lock
private final ReadWriteLock lock = new ReentrantReadWriteLock();
public Object get(String key) {
lock.readLock().lock();
try { return cache.get(key); }
finally { lock.readLock().unlock(); }
}
```
### String Operations
```java
// ❌ Bad: String concatenation in loop
String result = "";
for (String s : list) {
result += s; // Creates new String each iteration
}
// ✅ Good: StringBuilder
StringBuilder sb = new StringBuilder();
for (String s : list) {
sb.append(s);
}
String result = sb.toString();
// ✅ Good: String.join for simple cases
String result = String.join("", list);
```
### Collection Optimization
```java
// ❌ Bad: ArrayList when size known
List<String> list = new ArrayList<>();
for (int i = 0; i < 10000; i++) {
list.add(getData(i)); // Multiple resizes
}
// ✅ Good: Pre-size collections
List<String> list = new ArrayList<>(10000);
// ✅ Good: Use primitive collections for performance
// Use Eclipse Collections, Trove, or fastutil
IntList list = new IntArrayList(10000);
```
### Boxing/Unboxing
```java
// ❌ Bad: Autoboxing in hot path
public long sum(List<Long> numbers) {
long sum = 0;
for (Long n : numbers) {
sum += n; // Unboxing each iteration
}
return sum;
}
// ✅ Good: Use primitive streams
public long sum(List<Long> numbers) {
return numbers.stream().mapToLong(Long::longValue).sum();
}
// ✅ Good: Primitive arrays when possible
public long sum(long[] numbers) {
return Arrays.stream(numbers).sum();
}
```
## JIT Optimization
### Warm-up
```java
// Warm-up critical paths before measuring
public static void main(String[] args) {
// Warm-up phase
for (int i = 0; i < 10_000; i++) {
criticalMethod(i);
}
// Measurement phase
long start = System.nanoTime();
for (int i = 0; i < 100_000; i++) {
criticalMethod(i);
}
long duration = System.nanoTime() - start;
}
```
### JIT Logging
```bash
# Print JIT compilation
java -XX:+PrintCompilation -jar app.jar
# Print inlining decisions
java -XX:+UnlockDiagnosticVMOptions -XX:+PrintInlining -jar app.jar
# Disable specific optimizations for debugging
java -XX:+UnlockDiagnosticVMOptions -XX:DisableIntrinsic=_hashCode -jar app.jar
```
## Profiling Checklist
| Check | Tool | Command |
|-------|------|---------|
| CPU hotspots | JFR | `jcmd <pid> JFR.start` |
| Memory usage | jcmd | `jcmd <pid> GC.heap_info` |
| GC behavior | GC logs | `-Xlog:gc*` |
| Thread contention | JFR | `jdk.JavaMonitorWait` events |
| Memory leaks | Heap dump | `jcmd <pid> GC.heap_dump` |
| Class loading | jcmd | `jcmd <pid> VM.classloaders` |
## Production Flags
```bash
java \
-server \
-Xms4g -Xmx4g \
-XX:+UseG1GC \
-XX:MaxGCPauseMillis=200 \
-XX:+UseStringDeduplication \
-XX:+AlwaysPreTouch \
-XX:+DisableExplicitGC \
-XX:+HeapDumpOnOutOfMemoryError \
-XX:HeapDumpPath=/var/log/heap.hprof \
-Xlog:gc*:file=/var/log/gc.log:time,uptime:filecount=5,filesize=10m \
-jar app.jar
```
## Anti-Patterns
| Anti-Pattern | Why It's Wrong | Correct Approach |
|-------------|----------------|------------------|
| `System.out.println()` in hot paths | Extremely slow, synchronous I/O | Use SLF4J with async appenders |
| Creating many short-lived objects | GC pressure, allocation overhead | Reuse objects, use object pools |
| `synchronized` on hot paths | Thread contention, poor scalability | Use `ConcurrentHashMap`, `ReentrantLock`, or lock-free structures |
| String concatenation with `+` in loops | Creates many intermediate strings | Use `StringBuilder` |
| Autoboxing in loops | Creates wrapper objects | Use primitive types |
| Not sizing collections | Frequent resizing, memory churn | Pre-size with `new ArrayList<>(expectedSize)` |
| `finalize()` for cleanup | Unpredictable, deprecated | Use try-with-resources or explicit cleanup |
| Ignoring GC logs | Miss performance degradation | Always enable GC logging in production |
| One-size-fits-all heap | Wrong GC pauses for workload | Tune heap based on app behavior |
| Not using connection pooling | Connection creation overhead | Use HikariCP or similar |
## Quick Troubleshooting
| Issue | Diagnosis | Solution |
|-------|-----------|----------|
| Long GC pauses | Heap too large or wrong GC | Use ZGC/Shenandoah or tune G1GC pause targets |
| `OutOfMemoryError: Java heap space` | Memory leak or undersized heap | Analyze heap dump, increase `-Xmx` if needed |
| `OutOfMemoryError: Metaspace` | Too many classes loaded | Increase `-XX:MaxMetaspaceSize` or fix classloader leak |
| High CPU usage | Hot loop, inefficient algorithm | CPU profile with JFR or async-profiler |
| Thread contention | Lock competition | Thread dump analysis, reduce lock scope |
| Slow startup | Class loading, initialization | Use AppCDS, lazy initialization |
| Memory leak | Unclosed resources, static collections | Heap dump comparison, find growing objects |
| GC overhead limit exceeded | GC taking > 98% of time | Fix memory leak or increase heap |
| Full GC too frequent | Old gen filling up | Tune heap ratio, fix object tenure issues |
| Application unresponsive | Deadlock or long GC | Thread dump to find deadlock, GC logs |
## Related Skills
- [Spring Boot](../../backend-frameworks/spring-boot/SKILL.md)
- [Performance Profiling MCP](../../../mcp-servers/performance-profiler/)More Performance skills
seo-audit
coreyhaines31/marketingskills
When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.
competitor-profiling
coreyhaines31/marketingskills
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
vercel-optimize
vercel-labs/agent-skills
Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

