Most engineers already know how to break down a feature, but in a staff-level system-design interview, you'll be asked to break down an entire product while thinking out loud.
What makes staff+ different: junior and mid-level engineers get guided through problems with hints, but you, as a staff+ candidate, must drive the conversation, probe vague requirements, justify every architectural choice, and defend decisions against pushback — all while drawing on a whiteboard or virtual canvas.
This guide breaks down five system design questions that appear in most senior and staff interview loops, what interviewers actually evaluate at each stage, and frameworks for navigating ambiguity without drowning in unnecessary details.
1. How Would You Design a Rate Limiter?
You encounter this question constantly because rate limiters sit at the front door of nearly every modern API, defending against abuse while preserving a good user experience. Interviewers use it to evaluate whether you can juggle real production trade-offs: algorithm choice, distributed coordination, and graceful degradation when traffic suddenly spikes.
They want evidence that you understand different rate-limiting approaches and their production implications.
Algorithm Selection and Trade-Off
The biggest mistakes include:
- Proposing in-memory counters that fail when traffic routes to different servers
- Skipping algorithm comparison entirely and just saying "use token bucket"
- Ignoring where the limiter actually sits in the architecture
- Forgetting user experience when limits trigger
Instead, you should walk through requirement clarifications that demonstrate staff-level thinking:
Rate limit scope: Per user? Per IP address? Per API key? Understanding granularity shows you think about real abuse patterns.
Limit type: Hard cutoff versus gradual throttling? This affects user experience dramatically.
Time windows: Per second, minute, or hour? Finer granularity means more complex state management.
Burst tolerance: Should legitimate traffic spikes be allowed or strictly rate-limited? This determines algorithm choice.
Then, compare algorithms with specific trade-offs:
- Token bucket: Allows controlled bursts, smooth long-term rate limiting, requires tracking tokens and refill rate
- Leaky bucket: Enforces a fixed rate with no bursts, a more straightforward implementation, potentially frustrating for legitimate spikes
- Sliding window counter: Most accurate but computationally expensive, requires storing timestamps
- Fixed window counter: Simplest implementation, but allows burst at window boundaries (potential abuse)
Your algorithm choice ultimately depends on your specific requirements. If you need to accommodate legitimate traffic bursts while maintaining long-term rate limits, the token bucket delivers the best balance.
Production-Grade Implementation Considerations
For applications requiring strict, predictable traffic shaping regardless of input patterns, the leaky bucket proves superior. When you need accurate limiting without excessive memory overhead, a sliding-window counter provides an excellent middle ground between the simplicity of a fixed window and the precision of a sliding-window log.
Discuss distributed system challenges that separate senior engineers from staff engineers. Race conditions emerge when multiple servers check the same counter simultaneously. You must choose between strict accuracy (expensive synchronization) and slight inaccuracy (better performance).
At the staff level, discuss separating malicious floods from organic virality, offer dynamic configuration for different API tiers, and surface per-route metrics feeding dashboards that help engineers react before customers notice service degradation.
2. How Would You Design a Distributed Cache System?
When interviewers ask you to design distributed caching, they're testing the judgment you'd use to unblock scaling bottlenecks in production. Conceptually, caching seems simple, but it involves deep trade-offs about consistency, eviction policies, and distributed coordination under failure conditions.
They're evaluating your grasp of cache invalidation — one of computer science's famously challenging problems. Stale data corrupts results as quickly as incorrect data. You must weigh strong consistency (safest but slowest) against eventual consistency (fastest but riskier), and explain how each choice affects user experience, operational costs, and failure behavior.
The trap is treating cache as "just add Redis" without discussing invalidation strategy or distributed challenges. Single-node Redis looks fine on whiteboards but collapses once requests span multiple regions or write traffic outpaces network capacity.
Core Architecture and Cache Patterns
Start by clarifying requirements that demonstrate production thinking:
- Read-to-write ratio: 100:1 read-heavy versus 1:1 balanced workloads demand completely different architectures
- Acceptable staleness: Can users see data that's 5 seconds old? 5 minutes old? This determines your invalidation strategy
- Key size distribution: Few hot keys versus evenly distributed access patterns require different sharding approaches
- Failure recovery targets: Can you rebuild the cache from scratch, or must it survive crashes?
Outline core design decisions showing architectural maturity:
Cache patterns: Cache-aside (application manages cache), write-through (write to cache and database synchronously), write-behind (asynchronous database updates).
Eviction policy: Least Recently Used (LRU), Least Frequently Used (LFU), and TTL-based (time-to-live expiration).
Key structure: Namespacing strategy preventing collisions across different data types.
Distributed coordination: Consistent hashing for shard distribution, handling node additions and removals.
These fundamental patterns form the foundation of any distributed cache architecture. Most production systems combine multiple patterns, applying each where it delivers the most significant benefit for specific data types and access patterns.
Distributed Coordination and Failure Handling
Discuss distributed cache challenges that distinguish staff-level thinking. Hot keys create uneven load — celebrity profiles getting millions of requests while average users get dozens. A cache stampede occurs when a popular key expires and thousands of requests are issued to the database simultaneously.
Solutions include request coalescing (single flight pattern) or stale-while-revalidate patterns. Cache warm-up strategies prevent cold starts after deployments.
At the staff level, discuss cache-penetration attacks (requests for keys that never exist), propose dashboards that alert on hit-rate anomalies, describe A/B experiments testing alternative eviction algorithms, and explain regional cache synchronization for global applications.
3. How Would You Design a News Feed System?
Senior-level news feed questions combine multiple complex subsystems — fanout mechanisms, ranking algorithms, real-time updates, and personalization at a massive scale. Building feeds that feel instant yet personalized requires understanding how content flows from creation through delivery.
Interviewers evaluate how you manage genuine complexity without getting overwhelmed. Can you make smart simplifying assumptions rather than trying to solve everything? The trap is designing the entire platform in 45 minutes instead of focusing on core feed-generation mechanics.
Fanout Strategy Selection
To start, clarify the scope before drawing anything:
- Focus area: Feed generation only or include posting pipeline? Scope determines system boundaries
- Update model: Real-time updates or eventual consistency acceptable? This drives architecture decisions
- Personalized ranking: Chronological feeds or engagement-based ranking? Ranking adds significant complexity
- Scale characteristics: User base size, posts per second, follower distribution patterns
Discuss the core architectural decision separating different approaches — fanout strategy:
Fanout on write (push model): Pre-compute feeds when content is created, store in user timelines, enables fast reads, creates expensive writes for celebrity accounts with millions of followers
Fanout on read (pull model): Compute feeds on demand when users request them, slower reads but efficient for celebrity accounts, simpler writes
Hybrid approach: Fanout for regular users, pull for celebrities, matches how Twitter actually operates, requires sophisticated routing logic
The fanout strategy you choose fundamentally shapes your entire feed architecture. The hybrid model, pioneered by X (formerly Twitter) and widely adopted across the industry, represents a mature approach that balances both concerns by treating different user tiers appropriately based on their follower distributions.
System Components and Data Flow
Outline key system components demonstrating architectural thinking. Post service handles content creation and validation. Fanout service manages timeline distribution asynchronously. Feed storage uses Redis for recent items, Cassandra for historical data.
At the staff level, discuss abuse-prevention systems, content-moderation pipelines integrated with feed generation, A/B testing infrastructure for ranking-algorithm experiments, CDN strategies for media attachments, and database sharding by user versus by time, with trade-offs for each approach.
4. How Would You Design a Video Streaming Service?
Video streaming architecture tests whether you understand infrastructure beyond typical web applications. This involves CDN optimization, encoding pipelines, massive storage requirements, and bandwidth cost management — petabytes of data serving millions of concurrent users globally.
They're evaluating whether you grasp video-specific challenges. Encoding transforms raw uploads into multiple resolutions and bitrates. Adaptive streaming adjusts quality based on network conditions. Bandwidth costs dominate operating expenses at scale. These constraints shape every architectural decision.
The trap is focusing only on playback without discussing the complete pipeline from upload through delivery, or ignoring cost optimization.
Upload Pipeline and Content Processing
Clarify requirements to reveal production sophistication:
- Content type: Live streaming or on-demand? Live requires sub-second latency, and on-demand allows aggressive caching.
- Upload patterns: Thousands of creators uploading hourly, or a few professional uploads daily? This affects processing pipeline design.
- Global audience: Single region or worldwide distribution? CDN strategy depends entirely on geographic spread.
- Device support: Mobile, desktop, smart TVs? Each requires different encoding formats and bitrates.
This processing pipeline transforms raw uploads into production-ready content at scale. The pipeline's design prioritizes fault tolerance, allowing individual stage failures without corrupting the entire workflow.
Playback Architecture and Global Distribution
Walk through the upload and processing pipeline to demonstrate deep technical understanding. Original uploads land in blob storage (AWS S3, Google Cloud Storage). Upload triggers the transcoding service to generate multiple resolutions (360p, 720p, 1080p, 4K), each with various bitrates for adaptive streaming.
Thumbnail generation extracts representative frames. Metadata extraction populates searchable fields. Content moderation combines automated scanning with human review queues.
Describe the playback architecture optimized for global scale. Adaptive bitrate streaming via HLS or DASH protocols lets players switch quality seamlessly based on network conditions. DRM systems protect premium content. Access control validates subscriptions before serving streams.
At the staff level, address integrating the recommendation system to feed next-video suggestions. A/B test thumbnail effectiveness to drive engagement, copyright-detection systems to scan uploads, and failover strategies when CDN regions experience outages.
5. How Would You Design a Distributed Message Queue?
Message queues represent fundamental infrastructure powering every high-traffic system. Designing one demonstrates you understand distributed systems deeply — not just using queues, but building reliable asynchronous communication at scale.
They're testing your understanding of message delivery guarantees:
- At-most-once delivery: Fire-and-forget, potential message loss
- At-least-once delivery: Retries until acknowledged, potential duplicates
- Exactly-once delivery: complex deduplication, highest reliability
Each guarantee involves different trade-offs between performance, complexity, and reliability.
The trap is designing a simple queue without addressing distributed challenges, ordering requirements, or comprehensive failure scenarios. Production message queues handle partition rebalancing, leader election, consumer coordination, and graceful degradation across multiple failure modes.
Requirements Clarification
Clarify requirements to demonstrate distributed systems expertise:
- Ordering requirements: Must messages be processed in order globally, or is per-partition ordering sufficient? Global ordering limits scalability dramatically.
- Delivery guarantees: At-most-once, at-least-once, or exactly-once? Each requires different architectural complexity.
- Throughput targets: Thousands or millions of messages per second? This determines the partitioning strategy.
- Message persistence: In-memory (fast but volatile) or disk-based (durable but slower)? Durability requirements shape storage decisions.
- Consumer scaling: Single consumer or consumer groups processing in parallel? This affects partition design.
These requirement decisions cascade through your entire queue architecture.
Distributed Challenges and Failure Scenarios
Address distributed challenges, separating senior from staff thinking. Leader election for partition replicas ensures high availability when brokers fail. Consumer rebalancing redistributes partitions when instances join or leave groups. Offset management tracks each consumer's position, enabling crash recovery.
Consider failure scenarios comprehensively. Broker failure requires promoting replicas and reassigning partitions. Consumer failure triggers rebalancing, distributing work to remaining instances. Network partitions require split-brain prevention. Disk-full conditions need graceful degradation and alerting.
At the staff level, discuss dead-letter queues for permanently failed messages, message replay capabilities for debugging and recovery, and lag metrics that alert when consumers fall behind.
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