
performance-smell-detection
Detect potential code-level performance smells in Java - streams, collections, boxing, regex, object creation. Provides awareness, not absolutes - always measure before optimizing. For JPA/database performance, use jpa-patterns instead.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/decebals/claude-code-java --skill performance-smell-detection- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads performance-smell-detection automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Performance Smell Detection Skill
Identify potential code-level performance issues in Java code.
Philosophy
"Premature optimization is the root of all evil" - Donald Knuth
This skill helps you notice potential performance smells, not blindly "fix" them. Modern JVMs (Java 21/25) are highly optimized. Always:
- Measure first - Use JMH, profilers, or production metrics
- Focus on hot paths - 90% of time spent in 10% of code
- Consider readability - Clear code often matters more than micro-optimizations
When to Use
- Reviewing performance-critical code paths
- Investigating measured performance issues
- Learning about Java performance patterns
- Code review with performance awareness
Scope
This skill: Code-level performance (streams, collections, objects)
For database: Use jpa-patterns skill (N+1, lazy loading, pagination)
For architecture: Use architecture-review skill
Quick Reference: Potential Smells
| Smell | Severity | Context |
|---|---|---|
| Regex compile in loop | 🔴 High | Always worth fixing |
| String concat in loop | 🟡 Medium | Still valid in Java 21/25 |
| Stream in tight loop | 🟡 Medium | Depends on collection size |
| Boxing in hot path | 🟡 Medium | Measure first |
| Unbounded collection | 🔴 High | Memory risk |
| Missing collection capacity | 🟢 Low | Minor, measure if critical |
String Operations (Java 9+ / 21 / 25)
What Changed
Since Java 9 (JEP 280), string concatenation with + uses invokedynamic, not StringBuilder. The JVM optimizes simple concatenation well.
Java 25 adds String::hashCode constant folding for additional optimization in Map lookups with String keys.
Still Valid: StringBuilder in Loops
// 🔴 Still problematic - new String each iteration
String result = "";
for (String s : items) {
result += s; // O(n²) - creates n strings
}
// ✅ StringBuilder for loops
StringBuilder sb = new StringBuilder();
for (String s : items) {
sb.append(s);
}
String result = sb.toString();
// ✅ Or use String.join / Collectors.joining
String result = String.join("", items);
Now Fine: Simple Concatenation
// ✅ Fine in Java 9+ - JVM optimizes this
String message = "User " + name + " logged in at " + timestamp;
// ✅ Also fine
return "Error: " + code + " - " + description;
Avoid in Hot Paths: String.format
// 🟡 String.format has parsing overhead
log.debug(String.format("Processing %s with id %d", name, id));
// ✅ Parameterized logging (SLF4J)
log.debug("Processing {} with id {}", name, id);
Stream API (Nuanced View)
The Reality
Streams have overhead, but it's often acceptable:
- < 100 items: Streams can be 2-5x slower (but still microseconds)
- 1K-10K items: Difference narrows significantly
- > 10K items: Often within 50% of loops
- GraalVM: Can optimize streams to match loops
Recommendation: Prefer streams for readability. Optimize to loops only when profiling shows a bottleneck.
When Streams Are Problematic
// 🔴 Stream created per iteration in hot loop
for (int i = 0; i < 1_000_000; i++) {
boolean found = items.stream()
.anyMatch(item -> item.getId() == i);
}
// ✅ Pre-compute lookup structure
Set<Integer> itemIds = items.stream()
.map(Item::getId)
.collect(Collectors.toSet());
for (int i = 0; i < 1_000_000; i++) {
boolean found = itemIds.contains(i);
}
When Streams Are Fine
// ✅ Single pass, readable, not in tight loop
List<String> names = users.stream()
.filter(User::isActive)
.map(User::getName)
.sorted()
.collect(Collectors.toList());
// ✅ Primitive streams avoid boxing
int sum = numbers.stream()
.mapToInt(Integer::intValue)
.sum();
Parallel Streams: Use Carefully
// 🔴 Parallel on small collection - overhead > benefit
smallList.parallelStream().map(...); // < 10K items
// 🔴 Parallel with shared mutable state
List<String> results = new ArrayList<>();
items.parallelStream()
.forEach(results::add); // Race condition!
// ✅ Parallel for CPU-intensive + large collections
List<Result> results = largeDataset.parallelStream() // > 10K items
.map(this::expensiveCpuComputation)
.collect(Collectors.toList());
Boxing/Unboxing
Still a Real Issue
Boxing creates objects on heap, adds GC pressure. JVM caches small values (-128 to 127) but not larger ones.
Future: Project Valhalla will improve this significantly.
// 🔴 Boxing in tight loop - creates millions of objects
Long sum = 0L;
for (int i = 0; i < 1_000_000; i++) {
sum += i; // Unbox, add, box
}
// ✅ Primitive
long sum = 0L;
for (int i = 0; i < 1_000_000; i++) {
sum += i;
}
Use Primitive Streams
// 🟡 Boxing overhead
int sum = list.stream()
.reduce(0, Integer::sum);
// ✅ Primitive stream
int sum = list.stream()
.mapToInt(Integer::intValue)
.sum();
Regex
Always Pre-compile in Loops
This advice is not outdated - Pattern.compile is expensive.
// 🔴 Compiles pattern every iteration
for (String input : inputs) {
if (input.matches("\\d{3}-\\d{4}")) { // Compiles regex!
process(input);
}
}
// ✅ Pre-compile
private static final Pattern PHONE = Pattern.compile("\\d{3}-\\d{4}");
for (String input : inputs) {
if (PHONE.matcher(input).matches()) {
process(input);
}
}
Collections
Capacity Hint (Minor Optimization)
// 🟢 Low severity - but free optimization if size known
List<User> users = new ArrayList<>(expectedSize);
Map<String, User> map = new HashMap<>(expectedSize * 4 / 3 + 1);
Right Collection for the Job
// 🟡 O(n) lookup in loop
List<String> allowed = getAllowed();
for (Request r : requests) {
if (allowed.contains(r.getId())) { } // O(n) each time
}
// ✅ O(1) lookup
Set<String> allowed = new HashSet<>(getAllowed());
for (Request r : requests) {
if (allowed.contains(r.getId())) { } // O(1)
}
Unbounded Collections
// 🔴 Memory risk - could grow unbounded
@GetMapping("/users")
public List<User> getAllUsers() {
return userRepository.findAll(); // Millions of rows?
}
// ✅ Pagination
@GetMapping("/users")
public Page<User> getUsers(Pageable pageable) {
return userRepository.findAll(pageable);
}
Modern Java (21/25) Patterns
Virtual Threads for I/O (Java 21+)
// 🟡 Traditional thread pool for I/O - wastes OS threads
ExecutorService executor = Executors.newFixedThreadPool(100);
for (Request request : requests) {
executor.submit(() -> callExternalApi(request)); // Blocks OS thread
}
// ✅ Virtual threads - millions of concurrent I/O operations
try (ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor()) {
for (Request request : requests) {
executor.submit(() -> callExternalApi(request));
}
}
Structured Concurrency (Java 21+ Preview)
// ✅ Structured concurrency for parallel I/O
try (StructuredTaskScope.ShutdownOnFailure scope = new StructuredTaskScope.ShutdownOnFailure()) {
Future<User> user = scope.fork(() -> fetchUser(id));
Future<Orders> orders = scope.fork(() -> fetchOrders(id));
scope.join();
scope.throwIfFailed();
return new UserProfile(user.resultNow(), orders.resultNow());
}
Performance Review Checklist
🔴 High Severity (Usually Worth Fixing)
- Regex Pattern.compile in loops
- Unbounded queries without pagination
- String concatenation in loops (StringBuilder still valid)
- Parallel streams with shared mutable state
🟡 Medium Severity (Measure First)
- Streams in tight loops (>100K iterations)
- Boxing in hot paths
- List.contains() in loops (use Set)
- Traditional threads for I/O (consider Virtual Threads)
🟢 Low Severity (Nice to Have)
- Collection initial capacity
- Minor stream optimizations
- toArray(new T[0]) vs toArray(new T[size])
When NOT to Optimize
- Not a hot path - Setup code, config, admin endpoints
- No measured problem - "Looks slow" is not a measurement
- Readability suffers - Clear code > micro-optimization
- Small collections - 100 items processed in microseconds anyway
Analysis Commands
# Find regex in loops (potential compile overhead)
grep -rn "\.matches(\|\.split(" --include="*.java"
# Find potential boxing (Long/Integer as variables)
grep -rn "Long\s\|Integer\s\|Double\s" --include="*.java" | grep "= 0\|+="
# Find ArrayList without capacity
grep -rn "new ArrayList<>()" --include="*.java"
# Find findAll without pagination
grep -rn "findAll()" --include="*.java"
FAQ
What does the performance-smell-detection skill do?
Detect potential code-level performance smells in Java - streams, collections, boxing, regex, object creation. Provides awareness, not absolutes - always measure before optimizing. For JPA/database performance, use jpa-patterns instead.
How do I install performance-smell-detection?
Run npx skills add https://github.com/decebals/claude-code-java --skill performance-smell-detection in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/performance-smell-detection/ (personal) or .claude/skills/performance-smell-detection/ (project).
Which AI agents can use performance-smell-detection?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is performance-smell-detection safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains performance-smell-detection?
It is published by decebals in decebals/claude-code-java. It has 315 recorded installs.
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