fory-performance-optimization

name	fory-performance-optimization
description	Run profile-driven bottleneck optimization across Apache Fory implementations (Java, C++, Python/Cython, Go, Rust, Swift, C#, JavaScript/TypeScript, Dart, Kotlin, Scala). Use when improving serialize/deserialize throughput or latency, recovering regressions against a reference commit, diagnosing flamegraphs, fixing perf-related CI failures, or porting proven optimizations across languages without protocol or API regressions. Use when this capability is needed.
metadata	{"author":"apache"}

Fory Performance Optimization

Mission

Deliver measurable performance improvements in Apache Fory without protocol drift, correctness regressions, benchmark-shape tricks, or accidental API rollback.

Operating Principles

• Start from data, not intuition.
• Profile before changing hot code.
• Change one bottleneck at a time.
• Benchmark sequentially on the same machine state (one benchmark process at a time).
• Keep only measured wins or explicitly requested architecture cleanups.
• Revert speculative changes that do not pay off.
• Align with reference runtimes (usually C++ first, then Rust/Java) when behavior and ownership models differ.

Enforce Hard Constraints

• Preserve wire protocol unless explicitly requested.
• Preserve cross-language semantics and xlang compatibility.
• Never run two benchmarks at the same time on one host; run exactly one benchmark command at a time.
• Do not optimize by changing benchmark payload definitions, field encodings, or benchmark methodology.
• Do not add payload-identity or repeated-input caches that depend on benchmark shape.
• Do not restore removed APIs/legacy wrappers when the user forbids it.
• Do not preserve legacy/dead code or stale docs in optimization rounds; remove them when touched.
• Keep API surface minimal: do not add new API unless required by protocol/correctness or explicitly requested.
• Never add public hacky API for performance shortcuts; keep optimization helpers internal/private and conceptually clean.
• Do not hide regressions behind unsafe compiler flags or benchmark-only code paths.
• Keep optimization surfaces nested-safe; avoid root-only shortcuts unless they are architecturally valid and requested.

Execute Workflow

1. Read context and constraints.

• Read tasks/perf_optimization_rounds.md and tasks/lessons.md.
• Read the relevant spec in docs/specification/ for any path that may affect wire behavior.
• Record explicit user constraints (forbidden APIs, naming, architecture, protocol rules).

2. Define target and baseline.

• Identify one primary KPI (for example Struct Serialize ns/op or ops/sec).
• Benchmark current HEAD.
• If a reference commit is provided, benchmark it once and persist the result in a file (for example tasks/perf_baselines/<id>.md) to avoid repeated reruns.

3. Profile the hotspot.

• Capture a flamegraph or sampled stacks on the exact benchmark command.
• Quantify top costs by bucket (runtime bookkeeping, dispatch, allocation/copy, map/cache operations, buffer growth, metadata parse/validation).
• Tie each bucket to concrete file/line ownership before proposing changes.

4. Form one round hypothesis.

• State one bottleneck and one expected effect.
• Prefer structural fixes over micro-tweaks.
• If another runtime already solved the same bottleneck, port its design shape first.

5. Implement minimal change.

• Touch the smallest surface that can validate the hypothesis.
• Keep invariants explicit: protocol bytes, ownership, cache lifetime, reference semantics, nullability, schema-compatible behavior.

6. Verify correctness.

• Run language-local build/test/lint for the touched implementation.
• Run cross-language checks when runtime/type/protocol behavior can affect xlang.
• Confirm serialized sizes and compatibility expectations where applicable.

7. Benchmark and compare.

• Run targeted benchmark at least twice sequentially.
• Use longer duration when signal is noisy.
• Run one short full-suite sanity benchmark to catch collateral regressions.

8. Decide keep or revert.

• Keep only if gain is repeatable or cleanup is explicitly requested and accepted with measured tradeoff.
• Revert if performance regresses or gain is within noise and complexity increases.
• If a required cleanup regresses, redesign inside the new architecture instead of restoring banned patterns.

9. Log every round.

• Append one round entry to tasks/perf_optimization_rounds.md before starting the next round.
• Include hypothesis, code change, exact commands, before/after numbers, and keep/revert decision.
• Commit retained non-trivial rounds immediately.

10. Re-plan on instability.

• Stop and re-plan when benchmark runs conflict, machine contention is suspected, or profile does not match hypothesis.
• Re-ground on current HEAD after reset/rebase/checkout events before making further changes.

Apply Decision Rules

• Treat <1-2% movement as noise unless repeated under controlled runs.
• Require explicit proof for complexity-increasing optimizations.
• Prefer deleting dead APIs and dead state quickly after refactors.
• Keep naming/API cleanup only if performance remains in band.
• Never run before/after comparisons in parallel.

Use References

• Use references/workflow-checklist.md for execution checklists and stop conditions.
• Use references/language-command-matrix.md for the quick per-language verification matrix, and the canonical runtime docs under ../../languages/*.md for full language-specific rules.
• Use references/bottleneck-playbook.md for hotspot-to-fix mapping.
• Use references/round-template.md to log each optimization round consistently.

Produce Output

When finishing an optimization task, report:

• Baseline command and numbers.
• Final command and numbers.
• Net delta on primary KPI.
• Correctness and compatibility verification run.
• Kept vs reverted rounds and rationale.

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Source: apache/fory — distributed by TomeVault.