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System Design Mock Interview: Design Rate Limiter | Token Bucket, Leaky Bucket, Sliding Logs

This document summarizes the key content of a system design mock interview. I highly recommend watching the full video if you can.

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One-Page Executive Summary (2–3 min skim)

  • Problem Prompt (One-liner): Design a rate limiter that protects APIs from abuse, supports paid vs. free quotas, and works in distributed deployments.
  • Primary Scope: Core rate-limiting algorithms (token bucket, leaky bucket, fixed window counter, sliding logs, sliding-window counter) and their behavior under bursty traffic and multi-region setups.
    Out of Scope: Exact API contracts, production SLAs/metrics, vendor-specific configs.
  • Non-Functional Priorities: Consistent enforcement, low latency overhead, memory efficiency, fair usage, resilience to DDoS/brute force, and cost control.
  • Key Constraints & Numbers (as examples in video): per-user limits like 5 req/min or 10 req/min; developer trial quotas; concurrency limits (parallel sessions); geo/IP scoping.
  • High-Level Architecture (Text):
    1. Clients → Load Balancer(s)
    2. Rate Limiter service (enforcement)
    3. Shared store for counters/logs (e.g., Redis/Cassandra)
    4. Application servers
    5. Optional: local in-memory cache + background sync to shared store
    6. Optional: sticky sessions or distributed locks to reduce races (with trade-offs)
  • Top Trade-offs:
    • Burst tolerance vs. smoothness (token bucket vs. leaky bucket)
    • Precision vs. memory (sliding logs vs. sliding-window counter)
    • Strict global accuracy vs. latency/availability (locks vs. relaxed limits)
    • Simplicity vs. multi-region consistency
  • Biggest Risks/Failure Modes:
    • Race conditions across regions causing over-allow
    • Hot keys (popular users/tenants) overloading storage
    • Overly strict locks increasing tail latency
    • Fixed windows causing boundary spikes (“double dip” at minute edges)
    • Unbounded queues in leaky bucket causing memory pressure
  • 5-Min Review Flashcards:
    • Q: Why rate limit? A: Protect UX, security (brute force), and cost.
    • Q: Token bucket in one line? A: Tokens refill over time; requests spend tokens to allow bursts up to bucket size.
    • Q: Leaky bucket in one line? A: Queue + constant drain rate; smooths bursts, drops when queue full.
    • Q: Fixed window pitfall? A: Boundary spikes allow ~2× near window edges.
    • Q: Sliding logs advantage? A: Real-time window accuracy by tracking recent timestamps.
    • Q: Sliding-window counter benefit? A: Similar accuracy with fewer entries via per-second counters.
    • Q: Distributed consistency options? A: Sticky sessions, locks, or relax enforcement slightly.
    • Q: Operational win from local cache? A: Fewer store round trips at the cost of eventual consistency.

Ask AI: Executive Summary

Interview Tags (for later filtering)

  • Product Pattern: rate-limit
  • System Concerns: throttling, backpressure, high-availability, geo-replication, hot-key
  • Infra/Tech (mentioned): redis, cassandra, load-balancer

Ask AI: Interview Tags

Problem Understanding

  • Original Prompt (paraphrase): Build rate limiting to curb bot spikes and monetize APIs by tiering quotas; support per-user, concurrency, IP/location, and server-level limits.
  • Use Cases:
    • Free developer tier (e.g., 10 req/min) vs. enterprise tier
    • Brute-force prevention on login/promo/booking
    • Cost containment for autoscaling/pay-as-you-go services
    • Location/IP-based throttles during targeted campaigns
  • Out of Scope: Detailed auth, billing integration, analytics dashboards.
  • APIs: Not stated in video.

Ask AI: Problem Understanding

Requirements & Constraints

Given in Video

  • Per-identity throttling: user-based, concurrent session caps, IP/location-based, and server-scope limits.
  • Distributed setup with multiple regions and shared state introduces races; sticky sessions and locks are options with downsides.
  • Optional local cache + async sync to shared store improves performance but allows slight drift.

Assumptions (conservative)

  • Sub-millisecond to low-millisecond overhead target for the limiter path.
  • Quotas configurable per tenant/endpoint; enforcement returns 429 on reject.
  • Observability events on allow/deny for audits.

Ask AI: Requirements and Constraints

Back-of-the-Envelope Estimation

Not stated in video—skipping numerical estimation.

Ask AI: Estimation

High-Level Architecture

  • Ingress: Clients → regional Load Balancers (LBs).
  • Enforcement: Regional Rate Limiter services apply algorithm-specific checks.
  • State: Shared datastore for counters/logs (examples shown: Redis or Cassandra).
  • App Tier: Requests that pass are forwarded to App Servers.
  • Optional Consistency Helpers: Sticky sessions (per-user affinity) or distributed locks on shared keys.
    • Sticky sessions trade off balance & fault tolerance.
    • Locks serialize updates but add latency under contention.
  • Optional Performance Path: Each limiter keeps a local cache of recent keys; a background sync service reconciles with the shared store asynchronously (slight over-allow tolerated).

Ask AI: High-Level Architecture

Deep Dives by Subsystem

8.1 Token Bucket

  • Role: Allow bursts up to bucket size; tokens refill at a fixed rate; requests consume tokens.
  • State per key: last refill time, available tokens.
  • Behavior: If tokens ≥ 1 → allow & decrement; else reject.
  • Distributed Concerns: Concurrent updates to the same key can race in shared storage.

Ask AI: Subsystem - Token Bucket

8.2 Leaky Bucket

  • Role: Smooth bursts via FIFO queue drained at constant rate; overflow is dropped.
  • Tuning: Queue capacity (burst tolerance) and drain rate (throughput ceiling).
  • Risk: Excessive queueing under spikes increases latency or memory usage.

Ask AI: Subsystem - Leaky Bucket

8.3 Fixed Window Counter

  • Role: Count requests per fixed intervals (e.g., per minute); allow until threshold then reject.
  • Pitfall: Edge “double dip” lets near-window-boundary bursts exceed target rate.

Ask AI: Subsystem - Fixed Window Counter

8.4 Sliding Logs

  • Role: Track timestamps for each request; at decision time, count events within last T.
  • Pros: Accurate real-time windows.
  • Cons: Memory heavy (stores many timestamps).

Ask AI: Subsystem - Sliding Logs

8.5 Sliding-Window Counter

  • Role: Reduce memory by aggregating counts per small buckets (e.g., per second) within rolling window.
  • Behavior: Sum counts of buckets within last T; drop if ≥ limit.
  • Benefit: Near sliding-log accuracy with far fewer entries.

Ask AI: Subsystem - Sliding-Window Counter

8.6 Distributed Enforcement (Multi-Region)

  • Problem: Same user hitting different regions simultaneously → race updates in shared store → over-allow.
  • Options:
    • Sticky sessions (per-user affinity to a region/app). Trade-off: imbalance/fault tolerance.
    • Distributed locks per key when reading/updating. Trade-off: extra latency, potential contention.
    • Relax limits slightly; accept small error margins for performance.
    • Local cache + async reconciliation to shared DB.

Ask AI: Subsystem - Distributed Enforcement

Trade-offs & Alternatives

Topic Option A Option B Video’s Leaning Rationale (from video)
Burst Handling Token Bucket Leaky Bucket Both presented Token allows short bursts; leaky smooths strictly.
Windowing Fixed Window Sliding (logs/counter) Sliding Avoid boundary spikes; better fairness.
Consistency Locks Relaxed limits Mixed Locks add latency; small drift may be acceptable.
Affinity Sticky Sessions Any-region Mixed Affinity reduces races but hurts balance/failover.

Ask AI: Trade-offs

Reliability, Availability, and Performance

  • Consistency: Global strictness is hard in multi-region; eventual reconciliation or affinity/locks are discussed.
  • Latency Budget: Locks and cross-region calls add latency; local caches reduce datastore trips.
  • Backpressure: Leaky bucket provides steady processing; fixed window can cause sudden load spikes.
  • Degradation: When buckets empty or queues full → shed load (429) to protect core services.

Ask AI: Reliability and Performance

Security & Privacy

  • Abuse Prevention: Throttle brute-force attempts (login, promo, booking).
  • PII/Encryption: Not stated in video.

Ask AI: Security and Privacy

Observability

  • Metrics/Logs/Tracing: Not stated in video. (Would track allow/deny counts, latency, hot keys in practice—assumption.)

Ask AI: Observability

Follow-up Questions (from interviewer, if any)

Not stated in video.

Ask AI: Follow-up Questions

Candidate Questions (if modeled)

Not stated in video.

Ask AI: Candidate Questions

Key Takeaways

  • Pick algorithm by desired burstiness vs. smoothness characteristics.
  • Fixed window is simple but unfair at edges; prefer sliding approaches for accuracy.
  • Sliding-window counter is a strong default: accurate enough and memory efficient.
  • Distributed enforcement is non-trivial; accept small drift or pay with locks/affinity.
  • Local caches + async sync lower latency but require careful error budgets.
  • IP/location and concurrency limits complement per-user quotas.

Ask AI: Key Takeaways

Glossary

  • Token Bucket: Refillable tokens; spend to pass.
  • Leaky Bucket: FIFO queue drained at a constant rate.
  • Fixed Window: Counter in fixed intervals (minute/hour).
  • Sliding Logs: Per-request timestamps; real-time window count.
  • Sliding-Window Counter: Per-bucket aggregates within a rolling window.
  • Sticky Sessions: LB keeps a user bound to a region/instance.
  • Hot Key: Disproportionately active identity causing contention.

Ask AI: Glossary

Freshness Review (inline notes)

  • Locks and sticky sessions are presented as options.
    [Personal note: Prefer algorithms that minimize cross-region coordination (e.g., approximate counters or per-edge enforcement) to reduce lock contention in 2025.]
  • Fixed window counter is shown for simplicity.
    [Personal note: Likely outdated; consider sliding-window counter to avoid boundary spikes — verify for your stack.]
  • Local cache + async sync proposed to reduce latency.
    [Personal note: Add guardrails (per-key error budgets and periodic reconciliation) to bound drift in modern deployments.]

Attribution

About the summarizer

I'm Ali Sol, a PHP Developer. Learn more: