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Java rules for High Performance

Identify and apply practical Java high-performance techniques using a measure-first approach, with emphasis on allocation reduction, data layout, concurrency discipline, and evidence-based validation.

What is covered in this Skill?

• Measure-first workflow for Java code optimization
• JVM/runtime-aware coding guidance
• Allocation reduction techniques with bad/good patterns
• CPU hot-path simplification and loop-level efficiency patterns
• Concurrency/backpressure and timeout/cancellation discipline
• I/O, parsing, and serialization efficiency patterns
• Persistence/query and caching strategy guidance
• Java-centric decision workflow: keep/revert based on measured impact

Scope: Practical optimization in application code and APIs. Apply only where profiling indicates real bottlenecks.

Constraints

Performance optimization must be evidence-driven and safe, focused on Java code changes that preserve correctness and maintainability.

• MEASURE-FIRST: Establish baseline behavior and identify Java code hot paths before optimization
• NO PREMATURE OPTIMIZATION: Only optimize code paths identified by profiling evidence
• BEFORE APPLYING: Read the relevant reference(s) for bad/good examples and measurement workflow
• EDGE CASE: If hotspot evidence is unclear, ask clarifying questions before changing code

When to use this skill

• Review Java code for high performance
• Optimize Java hot path
• Reduce Java allocations
• Improve Java latency
• Improve Java throughput

Workflow

1. Identify Java hotspot and baseline behavior

Confirm the performance-sensitive Java path and baseline behavior before changing code.

2. Select the relevant reference(s) by bottleneck

Pick and read only the reference(s) matching the observed hotspot: references/145-refactoring-high-performance-java-memory-allocation.md for allocation pressure, primitives vs. wrappers, escape analysis, collection sizing, data layout, and deduplication; references/145-refactoring-high-performance-java-cpu.md for CPU-bound hot paths, bit-level parsing, branchless arithmetic, loop unrolling, Unsafe caution, and SIMD/vectorization; references/145-refactoring-high-performance-java-code-syntax.md for code shape, lambdas, API return conventions, parsing syntax, I/O strategy, concurrency, and control-flow improvements.

3. Apply targeted optimizations

Implement minimal, evidence-backed changes scoped to the chosen domain(s): memory/allocation, CPU/low-level, or code shape/control flow (and adjacent concurrency, I/O, and persistence/caching in Java code).

4. Validate and compare code-level outcomes

Compare before/after behavior and keep only Java code changes with meaningful, verified gains.

Reference

For detailed guidance, examples, and constraints, see:

• references/145-refactoring-high-performance-java-memory-allocation.md
• references/145-refactoring-high-performance-java-cpu.md
• references/145-refactoring-high-performance-java-code-syntax.md