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          "content": "Produce a 7-part mini design doc for a rate limiter library: 1) overview, 2) API surface, 3) algorithms compared, 4) storage backends, 5) failure modes, 6) testing plan, 7) rollout plan. Cover all seven parts, one section each."
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              "content": "# Design Doc: `ratelimit` — A Pluggable Rate Limiter Library\n\n---\n\n## 1) Overview\n\n**What:** A library that provides request rate limiting with pluggable algorithms and storage backends, usable in-process or distributed across a fleet.\n\n**Goals:**\n- Correct under concurrency (atomic check-and-consume, no over-admission)\n- Multiple algorithms behind one interface; swappable backends (in-memory, Redis)\n- Low overhead: < 1µs per decision in-process, single round-trip for distributed\n- Observable: every decision emits limit, remaining, and retry-after data\n\n**Non-goals:** Not an API gateway, quota/billing system, or traffic shaper. No opinionated auth or key-extraction policy beyond a pluggable `KeyFunc`.\n\n**Assumptions:** Callers identify limits by string key (e.g., `user:42`, `ip:10.0.0.1`). Clock skew exists between app servers; backend time is authoritative when distributed.\n\n---\n\n## 2) API Surface\n\n```go\ntype Decision struct {\n    Allowed    bool\n    Limit      int\n    Remaining  int\n    RetryAfter time.Duration // 0 if allowed\n    ResetAt    time.Time\n}\n\ntype Rule struct {\n    Algorithm Algorithm     // TokenBucket (default), FixedWindow, SlidingWindowCounter, Log\n    Rate      float64       // events per second\n    Burst     int64         // max instantaneous burst\n    Window    time.Duration // for windowed algorithms\n    Backend   Backend\n    OnFailure FailurePolicy // FailOpen (default) | FailClosed\n}\n\ntype Limiter interface {\n    // Allow atomically checks and consumes `cost` units for `key`.\n    Allow(ctx context.Context, key string, cost int64) (Decision, error)\n}\n\n// Convenience surface\nfunc New(rule Rule, opts ...Option) *Limiter\nfunc (l *Limiter) Allow(ctx context.Context, key string) (Decision, error) // cost = 1\nfunc (l *Limiter) Limit(keyFn KeyFunc) func(http.Handler) http.Handler     // HTTP middleware\n```\n\n**Key semantics:**\n- `Allow` is the only mutation path; check and consume happen in one atomic operation — callers never read state separately.\n- `cost > 1` supports weighted operations (e.g., expensive queries).\n- A future `Wait(ctx)` blocking variant can reuse the same core; not in v1.\n- Middleware extracts keys via `KeyFunc` (by API key, IP, etc.) — policy lives with the caller.\n\n---\n\n## 3) Algorithms Compared\n\n| Algorithm | Memory/key | Accuracy | Burst behavior | Distributed cost | Notes |\n|---|---|---|---|---|---|\n| Fixed window | O(1) | Low | Up to 2× limit at window boundaries | Trivial (`INCR` + TTL) | Cheapest; fine for coarse caps |\n| Sliding window counter | O(1) | Good | Damped, near-limit | Easy | Weighted blend of adjacent windows |\n| Sliding window log | O(events) | Exact | Exact | Expensive (sorted set per key) | Rarely worth the cost |\n| **Token bucket** | O(1) | Exact avg. rate | Bursts up to `Burst` | Moderate (Lua script) | **Default** |\n| Leaky bucket | O(1) | Exact avg. rate | Smooths/shapes output | Moderate | Use when downstream needs smoothing |\n\n**Recommendation:** Token bucket as the default — it bounds average rate, permits controlled bursts, and tolerates clock skew because refill is computed lazily from elapsed time. Fixed window for cheap, coarse \"N per day\" caps. Sliding window counter when window semantics must be strict but memory is constrained. The log algorithm ships but is documented as an anti-pattern at scale.\n\n---\n\n## 4) Storage Backends\n\n**Backend contract:** Implementations must support atomic check-and-consume per key, per-key TTL, and return backend-authoritative time. Reference implementations:\n\n- **In-memory** (default): Sharded maps with per-key lazy expiry, bounded by max-keys + LRU eviction. Single-node only.\n- **Redis** (recommended for distributed): One Lua script per algorithm that reads state, computes the decision, writes it, and sets TTL in a single round trip. Keys namespaced per rule. On Redis Cluster, use hash tags to keep related keys co-located.\n- **Memcached / Postgres**: Documented as best-effort; weaker atomicity guarantees, suitable only for approximate limiting.\n\n**Design note:** Algorithm logic lives in the backend's atomic operation (script), not in a read-then-write dance from the client. This is the single biggest correctness requirement for backends and is enforced by the contract test suite (§6).\n\n---\n\n## 5) Failure Modes\n\n| Failure | Behavior / Mitigation |\n|---|---|\n| Backend unreachable | Configurable `FailOpen` (default: allow; protects availability) vs `FailClosed` (for security-critical endpoints). Backend calls have a strict timeout budget (~50ms) before fallback. |\n| Clock skew across app servers | Distributed decisions use backend time (Redis `TIME`), not app clocks. Token bucket's lazy refill is naturally tolerant. |\n| Non-atomic check-then-set | Eliminated by design: single-script atomicity required by the backend contract. |\n| Key cardinality explosion (e.g., spoofed IPs) | Per-rule max-keys cap + eviction in in-memory backend; cardinality alerts in Redis. |\n| Hot keys | In-memory sharding locally; document Redis key hashing; optional two-tier mode (local approximate counter, central authoritative) as a follow-up. |\n| Retry storms after 429s | `RetryAfter` is always returned; callers/middleware expected to honor it. |\n| Silent over-admission | Metrics: `allowed`, `rejected`, `backend_errors`, `backend_latency` — rejection-rate anomalies page the owner. |\n\n---\n\n## 6) Testing Plan\n\n- **Deterministic unit tests:** Injected fake clock; test boundary conditions exactly — window rollover, empty-then-full bucket, cost exceeding remaining capacity.\n- **Property-based tests:** Over arbitrary traces, invariant holds: allowed tokens per window ≤ limit; tokens consumed ≤ `rate × elapsed + burst`. Run against all algorithms.\n- **Concurrency tests:** N goroutines on one key under `-race`; assert total admissions never exceed the theoretical maximum.\n- **Backend contract suite:** Every `Backend` implementation must pass the same behavioral battery (atomicity under parallel load, TTL correctness, error propagation). This makes adding a new backend cheap and safe.\n- **Integration:** Real Redis via testcontainers in CI; test failover, latency injection past the timeout budget, and fail-open/fail-closed transitions.\n- **Benchmarks:** ns/decision and allocs/op for in-memory; p99 round-trip latency for Redis; memory ceiling with 1M keys.\n\n---\n\n## 7) Rollout Plan\n\n1. **Phase 0 — Internal alpha:** Library used by one internal service, in-memory backend, feature-flagged.\n2. **Phase 1 — Shadow mode (2 weeks):** Enforce for zero traffic; compute and log every decision, compare against actual traffic. Tune limits using observed p99/p95 patterns; this is where bad limits get caught before they page anyone.\n3. **Phase 2 — Canary:** Enforce for 1–5% of traffic / one non-critical route. Watch false-rejection rate (target < 0.1%) and backend latency.\n4. **Phase 3 — Gradual enforcement:** Expand per-route with config-driven limits; ship dashboard (rejection rates, top keys, backend health) and runbook.\n5. **Phase 4 — GA:** Public docs, semver, deprecation policy; distributed backend on by default for multi-instance services.\n\n**Rollback:** Enforcement is behind a single config flag that disables it instantly (decisions still logged in shadow mode). `FailOpen` is the default, so a library or backend failure degrades to \"no limiting,\" never an outage.\n\n---\n\n*Open questions for review: (1) should `Wait()` ship in v1, (2) gRPC interceptor adapters in-tree or separate module?*",
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              "reasoning": "The user wants a 7-part mini design doc for a rate limiter library. They've given clear structure: 1) overview, 2) API surface, 3) algorithms compared, 4) storage backends, 5) failure modes, 6) testing plan, 7) rollout plan. One section each, all seven covered.\n\nThis is a technical design document request. The format should be a structured design doc with clear headings. Since it's a \"mini\" design doc, each section should be substantive but not bloated. Let me think about what a good rate limiter library design doc would contain.\n\nLet me think through each section:\n\n**1) Overview**\n- Purpose: what the library does, who it's for\n- Goals: correctness under concurrency, pluggable backends, multiple algorithms, low overhead\n- Non-goals: not a full API gateway, not doing auth, not a quota/billing system\n- Scope: in-process + distributed rate limiting\n- Maybe key concepts: limit keys, rules, decision results\n\n**2) API Surface**\nThis needs concrete code. Let me pick a language-agnostic or pick something. A common approach: show a Go-ish or Python-ish API. Actually a design doc usually picks a language. Let me do a language-agnostic sketch with pseudocode-ish, or pick Go since rate limiters (like rate package, or uber's ratelimit) are common in Go. Or Python. Let me write pseudocode-style with clear types.\n\nKey API elements:\n- Limiter interface: `Allow(key, cost) -> Decision`\n- Decision type: allowed bool, remaining, retryAfter, resetAt\n- Rule/Policy configuration: rate, burst, algorithm, window\n- Middleware adapters (HTTP middleware, gRPC interceptor)\n- Backend registration: in-memory, Redis\n- Keying: by user ID, IP, API key — key extraction\n- Cost/weight: allow multiple tokens per request\n- Observability hooks: metrics, logging\n\nImportant design decisions to flag:\n- Sync Allow vs async (blocking vs non-blocking)\n- Whether decision is atomic (allow + consume in one call)\n- Clock injection for testing\n\n**3) Algorithms compared**\nClassic four:\n- Fixed window counter: simple, cheap, boundary burst problem (2x burst at window edges)\n- Sliding window log: most accurate, memory heavy O(n) per key\n- Sliding window counter (weighted blend of adjacent fixed windows): good approximation, memory-light\n- Token bucket: smooth average rate, allows bursts up to bucket size, good default\n- Leaky bucket (as a meter or queue): smooths output rate\n\nCompare on: memory, accuracy, burst behavior, distributed suitability (atomicity requirements), clock skew tolerance.\n\nTable would be good here. Recommend: token bucket as default, sliding window counter when strict window semantics needed, fixed window for cheap coarse limits.\n\nDistributed considerations: token bucket with Redis Lua script for atomic check-and-decrement; sliding window log needs sorted sets — expensive; fixed window counter is easiest atomic (INCR with TTL).\n\n**4) Storage backends**\n- In-process: sync.Map or sharded map with mutexes; single node only\n- Redis: Lua scripts for atomicity, key expiry, cluster caveats (hash tags)\n- Other: Memcached (CAS, weaker atomicity), Postgres (advisory locks or upsert, low throughput), in-memory cluster via something like consistency hashing? Keep it to 3-4.\n- Backend interface requirements: atomic compare-and-update, TTL support, clock semantics (server time vs client time — clock skew)\n- Local caching layer / two-tier: local check + periodic sync? Maybe mention as advanced.\n\n**5) Failure modes**\n- Backend down: fail open vs fail closed (recommend configurable, default fail-open for availability unless security-critical)\n- Timeouts: budget the Redis call, fall back\n- Clock skew across instances: use server-side time (Redis TIME) where possible; token bucket with lazy refill is more tolerant\n- Hot keys / thundering herd: shard, cache\n- Memory exhaustion (in-process): max keys, LRU eviction, unbounded keys from IP spoofing\n- Race conditions: atomicity of check-and-increment\n- Retry storms / retry-after honored\n- Key cardinality explosion\n\n**6) Testing plan**\n- Unit tests: deterministic clock injection, boundary conditions (exact boundary, window rollover, burst capacity)\n- Algorithm property tests: long-run rate never exceeds limit, burst never exceeds bucket\n- Concurrency tests: race detector, N goroutines hammering one key, assert no over-admission\n- Integration tests: real Redis in CI (testcontainers), failover scenarios, latency injection\n- Load/perf tests: throughput per decision, memory ceiling\n- Chaos: kill Redis mid-test, verify fail-open behavior\n- Golden/fuzz: property-based testing (e.g., total allowed tokens over time)\n\n**7) Rollout plan**\n- Phase 0: alpha internally, feature flag\n- Phase 1: shadow mode / log-only — make decisions but don't enforce; tune limits based on observed traffic\n- Phase 2: canary — enforce for 1% of traffic or internal service\n- Phase 3: gradual enforcement with per-route limits, dashboard, alerting\n- Phase 4: GA — docs, versioning, deprecation policy\n- Rollback plan: kill switch via config, fail-open toggle\n- Metrics to watch: false rejections, backend latency, memory\n\nFormat considerations: The user asked for a design doc with 7 numbered sections, one section each. Headers with the 7 titles, numbered to match. Should use code blocks for API surface. A table for algorithm comparison would be great. Keep each section tight since it's a \"mini\" design doc — but substantive. The whole thing should be scannable. Length: this is inherently a longer response given 7 sections requested, but \"mini\" means I should be economical. Maybe 3-6 bullets or short paragraphs per section plus one code block and one table.\n\nLet me draft the API surface more concretely. I'll do pseudocode/Go-flavored:\n\n```go\n// Decision is returned for every Limit call.\ntype Decision struct {\n    Allowed    bool\n    Limit      int\n    Remaining  int\n    RetryAfter time.Duration // zero if allowed\n    ResetAt    time.Time\n}\n\ntype Limiter interface {\n    // Allow atomically checks and consumes cost against the rule for key.\n    Allow(ctx context.Context, key string, cost int64) (Decision, error)\n}\n\ntype Rule struct {\n    Algorithm Algorithm      // token bucket default\n    Rate      float64        // events per second\n    Burst     int64          // max burst\n    Window    time.Duration  // for windowed algorithms\n    Backend   Backend\n    OnFailure FailurePolicy  // FailOpen | FailClosed\n}\n\nfunc New(opts ...Option) *Limiter\nfunc (l *Limiter) Allow(ctx, key string) (Decision, error)        // cost=1 sugar\nfunc (l *Limiter) Limit(next http.Handler) http.Handler           // HTTP middleware\n```\n\nKey extraction: `KeyFunc func(*http.Request) (string, error)` for middleware.\n\nThat's a reasonable surface. Maybe also mention `Wait(ctx)` blocking variant? Could mention briefly. Keep it mini.\n\nFor the algorithms table:\n\n| Algorithm | Memory/key | Accuracy | Burst behavior | Distributed cost | Notes |\n| Fixed window | O(1) counter | Low | 2× burst at boundaries | Trivial (INCR+TTL) | cheapest |\n| Sliding window counter | O(1) ~2 counters | Good | damped | Easy | approximates log |\n| Sliding window log | O(events) | Exact | exact | Expensive (ZSET) | rarely worth it |\n| Token bucket | O(1) | Avg-rate exact | Permits bursts up to burst size | Moderate (Lua) | good default |\n| Leaky bucket | O(1) | smooths output | queues/shapes | Moderate | for shaping |\n\nStorage backends section:\n- Interface: `Take(ctx, key, rule, cost, now) -> Decision` — push algorithm logic into atomic script vs push raw state? Two design choices: (a) backend stores state, library computes — requires atomic read-modify-write; (b) backend executes an algorithm script (Lua). Recommend (b) for Redis via Lua; (a) for in-process.\n- InMemory: sharded maps, per-key lazy expiry, max keys + LRU\n- Redis: single Lua script per algorithm, keys with TTL, cluster hash-tag note\n- Multi-tier optional: local approximate + central authoritative\n\nFailure modes:\n- fail open vs closed (configurable per rule), timeout budget\n- clock skew: use backend time; token bucket tolerant\n- backend partition/degraded: degrade to local limiter? Optional\n- memory blowup: cardinality caps, eviction\n- atomicity violations: never check-then-set in two round trips\n- retry/queue feedback: honor RetryAfter\n- silent bugs: metrics on decision outcomes\n\nTesting:\n- deterministic fake clock\n- property tests: over any trace, sum allowed per window ≤ limit (windowed), or tokens ≤ rate*time + burst\n- concurrency race tests\n- backend contract test suite: any Backend implementation must pass same behavioral suite (this is a nice design doc element)\n- integration with real Redis, failure injection\n- benchmarks: ns/decision, allocs\n\nRollout:\n- v0 internal, shadow mode\n- canary enforcement\n- gradual per-route enforcement, limit tuning from shadow data\n- GA with dashboards, runbook, kill switch\n- rollback: config flag to disable enforcement instantly; fail-open default\n\nNow format: use markdown headers for each of the 7 sections, numbered. Include the code block in section 2, table in section 3. Keep the doc title at top. The user said \"one section each\" — meaning one section per part, don't merge. I'll comply exactly.\n\nLength target: mini design doc — maybe 700-1100 words. Let me aim for concise but complete. Bullets over prose where possible.\n\nLet me write it.\n\nTitle: \"Design Doc: `ratelimit` — a pluggable rate limiter library\" or similar.\n\nSection 1 Overview: 3-5 bullets: what/why, goals, non-goals, assumptions.\n\nSection 2 API: code block + a few notes on semantics (atomicity, keying, cost).\n\nSection 3: table + recommendation line.\n\nSection 4: bullets per backend + interface requirements.\n\nSection 5: bullets, each failure mode with mitigation.\n\nSection 6: bullets by test type.\n\nSection 7: phased plan + rollback.\n\nI'll write in Go-flavored pseudocode and note it's illustrative. Should be careful to keep it tight — mini doc. Let me write."
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