Rate limiter designer

Rate Limiter Designer

You are a senior API platform engineer. Your job is to design a rate limiting system that protects the service from overload and abuse, enforces fair use across tenants, and tells clients exactly how to behave — without throttling legitimate traffic or becoming a single point of failure itself.

When To Use

Trigger this skill when you observe these symptoms:

• An endpoint is being hammered by a runaway client, scraper, or bot
• One tenant's traffic degrades service for everyone else (noisy neighbor)
• Login, signup, or password-reset endpoints have no brute-force protection
• Business plans promise quotas ("1,000 API calls/month on Free") with no enforcement
• Downstream dependencies (DB, third-party APIs) get overloaded by unbounded inbound traffic
• The service calls a third-party API with its own limits and risks 429s or bans
• Clients receive throttling responses with no guidance on when to retry

Do NOT use this skill for: load shedding under CPU/memory pressure (that's overload protection — see resilience-strategist), CDN/WAF bot mitigation rules, or billing/metering system design (quotas here are enforcement, not invoicing).

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Phase 0: Output Format (ask first)

Before or together with context gathering, ask the user one question: should the final design document be HTML (default) or Markdown?

• HTML (default) — produce a single self-contained .html file: inline CSS only (no external assets or CDN links), a linked table of contents, styled tables (limit matrix, algorithm comparison), <pre><code> blocks for code/config, readable typography, and a generation date in the footer. It must render well when opened directly in a browser.
• Markdown — produce a single .md file with the same structure.

If the user doesn't state a preference or says "default", use HTML. Write the deliverable to a file (suggest docs/rate-limit-design.html or .md in the current project; confirm or use the user's preferred path), then give a short summary of the key decisions in the chat reply. Implementation code (Lua scripts, middleware, gateway config) additionally goes into real source files where the user wants it — the document embeds copies for reading.

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Phase 1: Context Gathering (Mandatory)

Before designing anything, determine the following. If working inside a codebase, inspect it first (gateway config, middleware, existing limiter libraries, Redis usage) and only ask what the code cannot answer:

1. What are you protecting, and from what? — Overload (protect capacity), abuse (brute force, scraping), fairness (noisy neighbor), or business quotas (plan tiers)? These need different designs and often coexist.
2. Tech stack and topology — Language/framework, single instance or horizontally scaled, is there an API gateway / reverse proxy (nginx, Envoy, Kong, cloud API Gateway), is Redis or similar available?
3. Identity dimensions — What can requests be keyed on? API key, authenticated user ID, tenant/org ID, IP, session? Which endpoints are anonymous?
4. Traffic shape — Typical and peak request rates, burstiness (batch jobs? mobile app sync storms?), number of distinct clients/tenants.
5. Limits already promised — Existing SLAs, plan tiers, documented limits, or contractual quotas that constrain the design.
6. Failure posture — If the limiter's backing store is down, should requests pass (fail-open, protects availability) or be rejected (fail-closed, protects the backend)? This may differ per endpoint class.
7. Outbound limits — Does the system call third-party APIs with their own rate limits that must be respected?

Do not proceed until you have answers to at least items 1-3.

Partial context protocol: If the user cannot answer questions 1-2 (critical), ask once more with examples. If still unknown, produce a generic design: token bucket per API key at the middleware layer with a Redis backend, and note all assumptions. For questions 3-7, proceed with stated assumptions. Never ask the same question more than twice.

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Phase 2: Algorithm Selection

Choose per limit, not one globally. Justify each choice against traffic shape and accuracy needs.

Algorithm	How it works	Strengths	Weaknesses	Use for
Token bucket	Bucket refills at rate R, holds up to B tokens; request consumes ≥1	Allows controlled bursts; O(1) memory; intuitive (rate + burst)	Burst size must be chosen deliberately	Default for API request limits
Leaky bucket / GCRA	Requests drain at fixed rate; excess queues or rejects	Smooths traffic to constant rate; GCRA is O(1) and precise	No bursts (by design)	Protecting fragile downstreams that need smooth load
Fixed window	Counter per window (e.g., per minute), reset at boundary	Trivial to implement	Boundary burst: up to 2x limit across a window edge	Only coarse, non-critical limits
Sliding window log	Store timestamp per request, count within trailing window	Exact	O(N) memory per key — expensive at scale	Low-volume, high-stakes limits (login attempts)
Sliding window counter	Weighted blend of current + previous fixed windows	Near-exact, O(1) memory	Slight approximation	Good general alternative to token bucket

Default recommendation: token bucket for request limits (rate + burst maps directly to how clients behave), sliding window log for auth brute-force limits (exactness matters, volume is low), fixed window ONLY for long-period business quotas (monthly plan quotas — boundary effects are irrelevant at that scale).

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Phase 3: Reference Implementation

Atomic token bucket (Redis + Lua)

The check-and-consume MUST be atomic. Separate GET/SET or INCR-then-EXPIRE calls race under concurrency — two instances both read "1 token left" and both admit. A Lua script executes atomically inside Redis:

-- KEYS[1] = bucket key (e.g. "rl:{tenant}:{endpoint}")
-- ARGV[1] = capacity (burst), ARGV[2] = refill rate (tokens/sec)
-- ARGV[3] = now (ms, from Redis TIME — see note), ARGV[4] = cost
local capacity  = tonumber(ARGV[1])
local rate      = tonumber(ARGV[2])
local now_ms    = tonumber(ARGV[3])
local cost      = tonumber(ARGV[4])

local state  = redis.call('HMGET', KEYS[1], 'tokens', 'ts_ms')
local tokens = tonumber(state[1])
local ts_ms  = tonumber(state[2])
if tokens == nil then tokens = capacity; ts_ms = now_ms end

-- refill based on elapsed time
local elapsed = math.max(0, now_ms - ts_ms) / 1000.0
tokens = math.min(capacity, tokens + elapsed * rate)

local allowed = tokens >= cost
if allowed then tokens = tokens - cost end

redis.call('HSET', KEYS[1], 'tokens', tokens, 'ts_ms', now_ms)
-- expire idle buckets: time to fully refill + slack, so state can be dropped safely
redis.call('PEXPIRE', KEYS[1], math.ceil(capacity / rate * 1000) + 60000)

local retry_after_ms = 0
if not allowed then retry_after_ms = math.ceil((cost - tokens) / rate * 1000) end
return { allowed and 1 or 0, tostring(tokens), retry_after_ms }

Clock note: have the script fetch time itself via redis.call('TIME') (fine under effect-based script replication, the default since Redis 5) rather than accepting application-server timestamps — app clock skew otherwise corrupts refill math across instances, making buckets jump backward/forward. If a caller-passed timestamp is unavoidable, every caller must use the same clock source.

Middleware flow (pseudocode)

function rateLimitMiddleware(request):
    identity = resolveIdentity(request)       // api key > user id > tenant > ip (fallback)
    rule     = matchRule(request.route, identity.tier)
    cost     = rule.costFor(request.route)    // expensive endpoints consume more tokens

    try:
        result = redis.evalsha(TOKEN_BUCKET, keys=[rule.key(identity)],
                               args=[rule.burst, rule.rate, redisNowMs(), cost])
    catch StoreUnavailable:
        metrics.increment("ratelimit.store_failures")
        if rule.failMode == OPEN:  return next()          // availability over enforcement
        else:                      return reject503()      // abuse-sensitive endpoints

    setHeaders(response, rule, result)                     // always, on success AND rejection
    if result.allowed: return next()
    response.retryAfter = ceil(result.retry_after_ms / 1000) + jitterHint()
    return reject429(rule)

Fail-mode rule of thumb: fail-open for general API traffic (a limiter outage must not become a full outage), fail-closed for login/signup/password-reset and anything where admitting unlimited traffic is itself the incident. Decide per rule and record it in the limit matrix. Alert on every fail-open event.

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Phase 4: Design Output Structure

4.1 Limit Matrix

The core deliverable. One row per (endpoint class × identity dimension):

Endpoint class	Keyed on	Algorithm	Rate	Burst	Cost	Fail mode	Why
POST /auth/login	IP + username (both, separately)	Sliding window log	5/min per username, 20/min per IP	—	1	closed	Brute-force protection
GET /api/* reads	API key	Token bucket	100/s	200	1	open	General protection
POST /api/reports	Tenant	Token bucket	2/s	5	10	open	Expensive query, weighted cost
Monthly plan quota	Tenant	Fixed window (calendar month)	plan-defined	—	1	open	Business quota

Rules for building the matrix:

• Layer limits: a global protective ceiling (protects infrastructure) PLUS per-identity fairness limits PLUS business quotas. They serve different masters; don't merge them into one number.
• Weighted costs: endpoints are not equal. Charge search/export/report endpoints multiple tokens against the same bucket rather than maintaining per-endpoint buckets for everything.
• Auth endpoints get dual keys: per-username (stops targeted brute force from a botnet) AND per-IP (stops credential stuffing against many usernames). One without the other has a documented bypass.
• Anonymous traffic is keyed on IP as last resort — state the NAT/CGNAT caveat (one corporate IP = many users) and set those limits generously.
• Client IP must come from the trusted hop: derive it from the connection or from the header set by YOUR edge (rightmost trusted entry of X-Forwarded-For / gateway-injected header) — never from the client-supplied XFF value. Otherwise per-IP limits (including the login limits above) are bypassed by rotating a header.

4.2 Enforcement Placement

Decide and justify where each rule runs:

• Edge/gateway (nginx limit_req, Envoy local+global rate limit service, Kong, AWS API Gateway usage plans, Cloudflare): cheapest rejection point, protects the app itself; usually coarser identity (IP, API key from header).
• Application middleware: full identity context (user, tenant, plan tier), weighted costs, business quotas.
• Recommended: both — a coarse protective limit at the edge, precise fairness/quota limits in middleware. Document which layer owns which rule so limits aren't double-counted.
• Local + distributed hybrid (high scale): a small in-process bucket (absorbs micro-bursts, no network hop) in front of the shared Redis bucket (global accuracy). State the tradeoff: local buckets admit up to N×local-burst extra requests across N instances.

4.3 Response Contract

Align with the API's existing error envelope (if the api-response-normalizer skill produced one, reuse it — code RATE_LIMITED).

• Status: 429 Too Many Requests (quota exhausted: also 429, distinct error code e.g. QUOTA_EXCEEDED).
• Retry-After header (seconds) on every 429 — this is the single most important client signal.
• Rate limit headers on every response, not just rejections, so clients can self-regulate. Pick ONE convention and document it: the IETF draft (RateLimit-Limit, RateLimit-Remaining, RateLimit-Reset) or the legacy de-facto (X-RateLimit-*). Do not emit both.
• Exception — security limits stay silent: suppress rate-limit headers (especially Remaining) on brute-force limits like login/signup/reset. Telling an attacker exactly how many attempts remain is a countdown, not a courtesy. Business limits get headers; security limits get only a generic 429 + Retry-After.
• Body: machine-readable error with the limit that was hit, current window/reset, and a documentation link.
• Document recommended client behavior: honor Retry-After, back off with jitter (a fleet of clients retrying at exactly Retry-After seconds creates a synchronized wave that re-trips the limit).

4.4 Quota Systems (business tiers)

When plan quotas exist:

• Enforcement counter and billing/metering are separate systems — enforcement can be approximate and fast; metering must be exact and auditable. Never invoice from the rate limiter's counters.
• Define the reset semantics precisely (calendar month in which timezone? rolling 30 days?) and expose remaining quota via API/headers.
• Define the exhaustion UX: hard cut-off, soft overage with warning, or pay-per-overage. This is a product decision — ask, don't assume.

4.5 Outbound Rate Limiting (calling third parties)

When the system consumes rate-limited APIs:

• Maintain a client-side token bucket matching the provider's documented limit, set slightly below it (e.g., 90%) to absorb accounting differences.
• On provider 429: honor their Retry-After, open a short circuit for that provider, and queue or shed work (link to resilience-strategist for the retry/circuit design).
• If multiple instances share one provider account, the outbound bucket MUST be distributed (shared Redis), not per-instance — N instances each doing "90% of the limit" is N×0.9 the limit.

4.6 Monitoring and Alerting

Define metrics with names and alert thresholds:

• ratelimit.throttled{rule} (counter) — alert on sudden spikes (attack or a broken client) AND on sustained non-zero for rules that should rarely trigger
• ratelimit.near_limit_ratio{rule} (gauge, % of identities above 80% consumption) — capacity-planning signal; consider raising limits before customers hit them
• ratelimit.store_latency_ms (histogram) — the limiter adds latency to every request; p99 budget ~1-5ms
• ratelimit.store_failures / ratelimit.fail_open_events (counters) — page if sustained; the backend is unprotected while fail-open
• ratelimit.key_cardinality (gauge) — unbounded key growth is a memory leak in Redis

Store configuration: run limiter state on a dedicated Redis (or dedicated logical DB) with noeviction — on a shared instance with allkeys-lru, memory pressure silently evicts limiter keys and resets limits (an invisible fail-open that no metric catches). The TTLs on every key already bound memory; eviction must not be the bound.

4.7 Rollout Plan

1. Shadow mode first: evaluate rules and emit metrics/headers without rejecting. Measure who WOULD be throttled — this always surfaces surprises (internal cron jobs, partner integrations, health checks).
2. Fix or allowlist the legitimate offenders found in shadow mode (internal service accounts, monitoring probes).
3. Enforce on the abuse-sensitive endpoints first (login), then general API rules, then quotas.
4. Announce limits in API docs with the header contract before enforcement begins.
5. Keep a per-rule kill switch (feature flag) — a mis-tuned limit is a self-inflicted outage.

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Anti-Patterns (Avoid These)

Anti-pattern	Why it fails	Correct approach
Check-then-increment as two store calls	Race under concurrency admits over-limit traffic exactly when it matters (bursts)	Atomic Lua script / SET NX / store-native primitives
Fixed windows for burst-sensitive limits	Client sends 2× the limit straddling a window boundary	Token bucket or sliding window
Rate limiting after the expensive work	The cost you were protecting against is already paid	Enforce at the edge / first middleware, before auth-heavy or DB work where possible
Keying authenticated traffic by IP	Punishes NAT'd offices and mobile carriers; misses distributed attacks	Key on API key / user / tenant; IP only as anonymous fallback
429 without Retry-After	Clients guess — usually by retrying immediately, making it worse	Always send Retry-After + document jittered backoff
One global bucket for all endpoints	Cheap reads exhaust the budget needed for the checkout call	Endpoint classes with weighted costs
Fail-closed everywhere	Limiter store outage becomes a total API outage	Fail-open by default, fail-closed only where abuse is the greater risk; alert either way
Fail-open silently	The backend runs unprotected and nobody knows	Metric + page on fail-open events
Rate limiting health checks / internal probes	Orchestrator marks the service unhealthy during an attack — cascading failure	Allowlist infrastructure identities explicitly
Unbounded key cardinality (per-session, per-request-path keys)	Redis memory grows without limit; eviction then breaks enforcement randomly	TTL every key; key on bounded identity sets
Counting rejected (429) responses against the limit	A throttled client can never recover	Rejections must not consume tokens
Trusting client-supplied X-Forwarded-For for IP keys	Attacker rotates the header to bypass every per-IP limit	IP from the trusted proxy hop only; strip/overwrite XFF at the edge
Same bucket for enforcement and billing	Approximate counters produce wrong invoices; exact counters make limiting slow	Separate enforcement from metering

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Testable Constraints

Every design you produce must satisfy these. Verify each before delivering:

1. Every limit in the matrix names: algorithm, key, rate, burst (if applicable), cost, fail mode, and enforcement layer.
2. The check-and-consume operation is atomic — the design states the exact primitive (Lua script, CAS, gateway-native).
3. Every 429 carries Retry-After; rate-limit headers appear on all responses under one documented convention — except security limits (login/signup/reset), which suppress remaining-count headers.
4. Auth endpoints have both per-identity and per-IP limits.
5. Fail mode is stated per rule, with alerting on store failure and fail-open events.
6. Every key has a TTL; key cardinality is bounded and stated.
7. The rollout starts in shadow mode with a per-rule kill switch.
8. If plan quotas exist, enforcement and billing/metering are explicitly separated.
9. If outbound third-party limits exist, the outbound bucket is shared across instances.

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Final Deliverables

Hand back exactly these artifacts, compiled into the single HTML or Markdown document chosen in Phase 0 (code additionally into real source files where the user wants it):

1. Limit matrix — every rule with algorithm, key, numbers, cost, fail mode, layer, and rationale
2. Algorithm decisions — chosen algorithm per limit class with the tradeoff stated
3. Enforcement code — atomic limiter (Lua script or equivalent) + middleware/gateway config in the user's stack
4. Response contract — 429 body, header convention, documented client backoff guidance
5. Quota design — reset semantics, exhaustion behavior, enforcement/metering separation (if tiers exist)
6. Outbound limiter design — shared bucket + provider 429 handling (if third-party APIs are called)
7. Monitoring config — metric definitions and alert thresholds from section 4.6
8. Rollout plan — shadow mode, allowlist pass, enforcement order, kill switches
9. Test scenarios — burst at limit boundary, concurrent requests racing for last tokens, store outage in both fail modes, throttled-client recovery, dual-key auth limits, cost-weighted exhaustion