Agent skill

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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SKILL.md

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:

  1. Measure first - Use JMH, profilers, or production metrics
  2. Focus on hot paths - 90% of time spent in 10% of code
  3. 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 šŸ”“ 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 šŸ”“ 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

java
// šŸ”“ Still problematic - new String each iteration
String result = "";
for (String s : items) {
    result += s;  // O(n²) - creates n strings
}

// āœ… StringBuilder for loops
StringBuilder sb = new StringBuilder();
for (String s : items) {
    sb.append(s);
}
String result = sb.toString();

// āœ… Or use String.join / Collectors.joining
String result = String.join("", items);

Now Fine: Simple Concatenation

java
// āœ… Fine in Java 9+ - JVM optimizes this
String message = "User " + name + " logged in at " + timestamp;

// āœ… Also fine
return "Error: " + code + " - " + description;

Avoid in Hot Paths: String.format

java
// 🟔 String.format has parsing overhead
log.debug(String.format("Processing %s with id %d", name, id));

// āœ… 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

java
// šŸ”“ 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);
}

// āœ… 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

java
// āœ… Single pass, readable, not in tight loop
List<String> names = users.stream()
    .filter(User::isActive)
    .map(User::getName)
    .sorted()
    .collect(Collectors.toList());

// āœ… Primitive streams avoid boxing
int sum = numbers.stream()
    .mapToInt(Integer::intValue)
    .sum();

Parallel Streams: Use Carefully

java
// šŸ”“ Parallel on small collection - overhead > benefit
smallList.parallelStream().map(...);  // < 10K items

// šŸ”“ Parallel with shared mutable state
List<String> results = new ArrayList<>();
items.parallelStream()
    .forEach(results::add);  // Race condition!

// āœ… 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.

java
// šŸ”“ 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
}

// āœ… Primitive
long sum = 0L;
for (int i = 0; i < 1_000_000; i++) {
    sum += i;
}

Use Primitive Streams

java
// 🟔 Boxing overhead
int sum = list.stream()
    .reduce(0, Integer::sum);

// āœ… 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.

java
// šŸ”“ Compiles pattern every iteration
for (String input : inputs) {
    if (input.matches("\\d{3}-\\d{4}")) {  // Compiles regex!
        process(input);
    }
}

// āœ… 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)

java
// 🟢 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

java
// 🟔 O(n) lookup in loop
List<String> allowed = getAllowed();
for (Request r : requests) {
    if (allowed.contains(r.getId())) { }  // O(n) each time
}

// āœ… O(1) lookup
Set<String> allowed = new HashSet<>(getAllowed());
for (Request r : requests) {
    if (allowed.contains(r.getId())) { }  // O(1)
}

Unbounded Collections

java
// šŸ”“ Memory risk - could grow unbounded
@GetMapping("/users")
public List<User> getAllUsers() {
    return userRepository.findAll();  // Millions of rows?
}

// āœ… 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+)

java
// 🟔 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
}

// āœ… 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)

java
// āœ… 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

šŸ”“ 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

bash
# 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"

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