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              "content": "# Rate Limiter Library — Mini Design Doc\n\n## 1. Overview\n\nA reusable rate limiting library that lets services enforce request quotas consistently across processes and languages. Goals: pluggable algorithms, pluggable storage (in-memory to distributed), sub-millisecond hot path, and graceful degradation when backing stores fail. Non-goals: auth, billing, full API gateway features.\n\nKey requirements:\n- Enforce limits per key (user ID, IP, API key) with configurable windows\n- p99 overhead < 1ms for local backends, < 10ms for distributed\n- Fail-open or fail-closed configurable per deployment\n- Thread-safe and coroutine-friendly\n\n## 2. API Surface\n\n```python\nlimiter = RateLimiter(\n    algorithm=\"token_bucket\",       # or \"sliding_window\", etc.\n    backend=RedisBackend(url=...),  # or MemoryBackend(), etc.\n    clock=SystemClock(),            # injectable for testing\n)\n\n# Core calls\ndecision = limiter.allow(key=\"user:42\", limit=100, period=\"1m\")\n# -> Decision(allowed: bool, remaining: int, retry_after: timedelta | None)\n\n# Decorator / middleware adapters\n@limiter.protect(key=lambda req: req.user_id, limit=100, period=\"1m\")\ndef handler(req): ...\n\n# Batch check (atomic across multiple keys)\nlimiter.allow_many([(\"user:42\", 100, \"1m\"), (\"ip:1.2.3.4\", 10, \"1s\")])\n```\n\nDesign notes:\n- `Decision` is a rich object, not a bool, so callers can set `Retry-After` headers.\n- Limits are passed at call time, not registration time, so multiple limits can coexist.\n- Clock injection enables deterministic tests.\n\n## 3. Algorithms Compared\n\n| Algorithm | Accuracy | Memory | Burst behavior | Notes |\n|---|---|---|---|---|\n| Fixed window | Low (boundary bursts 2×) | Lowest | Bad at edges | Cheap; fine for coarse limits |\n| Sliding window log | Exact | High (store every hit) | Smooth | Costly at high RPS |\n| Sliding window counter | Good | Low | Good | Weighted blend of adjacent fixed windows |\n| Token bucket | Good | Low | Configurable burst | Default choice; supports uneven refill |\n| Leaky bucket | Good | Low | Smooths output | Best for shaping outbound traffic |\n\n**Decision:** ship token bucket and sliding window counter as defaults; fixed window as a cheap fallback; sliding window log for audit-grade accuracy. All implement a common `Algorithm` interface taking `(state, now, limit)` → `(decision, new_state)` so backends stay algorithm-agnostic.\n\n## 4. Storage Backends\n\n- **MemoryBackend** — lock-protected hash map, per-process. Zero network hop; use for single-instance services or as L1 cache.\n- **RedisBackend** — Lua scripts for atomic check-and-update; primary distributed option. Use hash slots and key prefixing for multi-tenant setups.\n- **SQLBackend** — transactional upserts; for low-QPS limits where Redis isn't already in the stack.\n- **Tiered (Memory + Redis)** — local cache with short TTL for read-mostly keys; accepts slight over-admission in exchange for latency. Bounds documented and tunable.\n\nBackend interface: `get(key)`, `compare_and_set(key, expected, new, ttl)`. Backends never implement algorithm logic — that lives in the algorithm layer so behavior is identical everywhere.\n\n## 5. Failure Modes\n\n- **Backend unavailable:** default **fail-open** (allow all) with a metric alarm; fail-closed opt-in for high-security endpoints. Config is per-limiter, not global.\n- **Clock skew (distributed backends):** all TTLs computed server-side (Redis `EXPIRE`), never client timestamps.\n- **Thundering herd on recovery:** jittered retry/backoff on backend reconnect; limits re-hydrate from persistent state, restarting from zero only if state was lost.\n- **Redis latency spikes:** circuit breaker trips after N consecutive timeouts; limiter fails open while breaker is open, half-opens to probe recovery.\n- **Memory pressure (MemoryBackend):** LRU eviction with a hard cap; evicted keys restart fresh (documented over-admission risk).\n- **Hot keys:** key sharding in Redis for extreme cases (split one key into N sub-keys, sum lazily).\n\n## 6. Testing Plan\n\n- **Unit:** each algorithm with a fake clock — boundary conditions (first request, window edge, exactly-at-limit, refill math), property-based tests (never allows more than N in any window).\n- **Backend contract tests:** one shared suite all backends must pass (atomicity under concurrency, TTL behavior, CAS semantics). Run against real Redis/Postgres in CI via containers.\n- **Integration:** concurrent load tests asserting the count of admitted requests ≤ limit + documented tolerance (tiered backend).\n- **Chaos:** kill Redis mid-test; assert fail-open/fail-closed behavior and circuit breaker transitions.\n- **Performance:** benchmark suite tracking p50/p99 overhead per backend; regression gate in CI.\n- **Fuzzing:** adversarial key patterns and concurrent callers via randomized scheduling.\n\n## 7. Rollout Plan\n\n1. **Alpha (weeks 1–2):** MemoryBackend + token bucket, internal dogfood in one low-risk service, fail-open only.\n2. **Beta (weeks 3–6):** RedisBackend, full algorithm set, dashboards for admission rate and backend latency. Shadow mode in two services (log decisions, don't enforce).\n3. **GA (weeks 7–10):** enforce in shadow-mode services behind a flag at 1% → 25% → 100% traffic, with rollback = flag off. Publish runbook for backend outages.\n4. **Post-GA:** tiered backend, additional language SDKs, and a monthly review of false-allow/false-deny metrics.\n\nSuccess criteria: < 0.01% erroneous denials, zero incidents attributable to the limiter, p99 overhead within budget at GA.",
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