Devising sanity checks for large-scale system design proposals

When architecting a large-scale system—be it a globally distributed content delivery network or a high-throughput message queue—vision and creativity are crucial, but they’re not enough. You must verify that your grand design is grounded in reality. Sanity checks are a critical step: quick, high-level validations to ensure your proposal is feasible, stable, and economically justifiable before delving into deeper complexities.

In this guide, we’ll explore practical methods to devise and apply sanity checks to large-scale system designs. Whether you’re preparing for a high-stakes system design interview or firming up a proposal at work, these steps help you refine your architecture, catch hidden flaws early, and ultimately deliver a more robust solution.

Why Sanity Checks Matter

1. Validating Assumptions:
No matter how well-intentioned, every design includes assumptions—about traffic patterns, data sizes, latency requirements, and more. Sanity checks catch assumptions that are too optimistic or unrealistic, preventing wasted effort on flawed architectures.

2. Minimizing Over-Engineering:
Without sanity checks, you risk building systems that are too large, too complex, or too expensive. Quick assessments help you right-size your solution, saving time, cost, and operational headaches.

3. Building Confidence and Credibility:
For interviews and stakeholder presentations, sanity checks demonstrate that you’re not just throwing ideas around—you’re evaluating them. This conveys maturity, professionalism, and trustworthiness.

Steps to Devise Effective Sanity Checks

1. Estimate Core Metrics Early:
   Before diving into architecture details, estimate the magnitude of critical metrics:

   • User Requests per Second (RPS) or Queries per Second (QPS)
   • Data Storage Requirements (GB, TB, PB)
   • Latency Expectations (ms, s)
   • Throughput and Bandwidth Constraints

   These rough numbers form a baseline. For instance, if you’re designing a URL shortener:

   • Assume you’ll need to handle 100 million requests/day (~1,157 requests/second).
   • Estimate storage for billions of URLs.
     Even if approximate, these numbers guide you in selecting realistic components (like load balancers, caches, and database partitions).

2. Check Component Limits:
   Consider each system component and apply rough capacity checks:

   • Databases: If a single database instance can handle 10,000 writes/second and you need 100,000 writes/second, you know you must shard or replicate.
   • Caches: If your cache holds 1 million keys but you anticipate 10 million frequently accessed keys, you’ll need a larger cache cluster or a more efficient eviction policy.

   These comparisons anchor your design choices in achievable performance targets.

3. Map Latency Targets to Network Realities:
   When dealing with globally distributed users, understand that crossing continents imposes network latency. If you need sub-100ms round-trip times worldwide, consider using Content Delivery Networks (CDNs) or regional data centers. Without acknowledging physical limits and speed-of-light constraints, your latency goals might be unattainable.

4. Approximate Costs and Operational Complexity:
   Large-scale systems often require many servers, load balancers, and storage nodes. Even roughly:

   • 100 TB of data might mean dozens of database shards.
   • Millions of requests per second might require thousands of application servers.

   If costs or operational overhead seem astronomical, reconsider your design. Perhaps a more compact architecture, greater caching, or a different data model can achieve similar goals with fewer resources.

5. Simulate Worst-Case Scenarios:
   Ask: “What if a region goes down?” or “What if traffic spikes 10x?” Apply these stress tests conceptually:

   • If losing one data center cripples your system, you need redundancy strategies.
   • If a traffic surge overwhelms your load balancers, you need autoscaling or better rate-limiting.

   Simple mental “what-ifs” can highlight single points of failure or missing resiliency features.

6. Leverage Established Patterns and Benchmarks:
   Familiar patterns from resources like Grokking the System Design Interview and Grokking System Design Fundamentals provide proven architectures for common scenarios. By comparing your design against known successful patterns (like using sharded databases for horizontal scaling or employing message queues for asynchronous workloads), you ensure your proposals align with industry best practices.

Applying Sanity Checks in a Real-World Example

Scenario: Designing a High-Volume E-Commerce Platform

• Estimate Traffic: Suppose you anticipate 500,000 concurrent users during sales. If each user sends an average of 1 request/second, you’re looking at 500k RPS.
• Check DB Capacity: If a single relational database handles 5,000 writes/second comfortably, and you expect at least 50,000 writes/second, you know you need 10 or more shards (or consider NoSQL options with better horizontal scaling).
• Latency Feasibility: With global customers, achieving <100ms may require edge caching via a CDN. If CDN nodes are 200ms away from some regions, no amount of database optimization can fix this—consider additional PoPs (Points of Presence).
• Cost and Complexity: If scaling a relational DB cluster that large seems too complex or expensive, consider a NoSQL store. A quick feasibility check here might show that a NoSQL DB with built-in sharding and horizontal scaling reduces complexity.

This series of sanity checks helps you pivot from an overly complex or unrealistic design toward a balanced, high-level architecture that can scale without crushing costs or performance.

Continuous Validation Through Mock Interviews and Feedback

1. Mock Interviews:
   Engage in System Design Mock Interviews to present your proposals and have experienced engineers challenge your assumptions. They’ll probe your capacity planning, ask for specific throughput numbers, and force you to justify your architecture with sanity checks. This feedback loop rapidly improves your ability to reason about feasibility.

2. Peer Review and Cross-Functional Input:
   Discuss your design with peers, DevOps engineers, or architects who’ve built similar systems. They can validate (or refute) your estimates, highlight overlooked scalability issues, and suggest more accurate benchmarks.

3. Refine with Educational Content:
   Explore blogs from DesignGurus.io, such as the Complete System Design Guide, to understand known performance benchmarks. Using their examples, you can calibrate your sanity checks against established norms.

Tools and Techniques for Quick Validation

• Back-of-the-Envelope Calculations:
  For each layer—front-end load balancers, caches, databases—do a rough calculation of capacity and throughput. Keep these estimates simple and conservative.

• High-Level Resource Sizing Charts:
  Many cloud providers publish guidelines. For instance, if one EC2 instance can handle X requests/second, you can scale up to N instances for N*X RPS. These approximations form the backbone of your sanity checks.

• Comparisons with Known Systems:
  Think about companies you know. If a known large-scale service handles a billion requests/day with a certain known architecture pattern, you can benchmark your proposed system against that known reference point.

Conclusion

Sanity checks are the difference between a shiny but unrealistic design and a grounded, robust architecture. By estimating key metrics, testing component limits, considering physical and financial constraints, and simulating stress scenarios, you transform lofty ideas into practical proposals.

Armed with references, patterns, and continuous feedback—through mock interviews, blogs, and pattern-based courses—you build a habit of critical thinking. Over time, these sanity checks become second nature, allowing you to confidently propose large-scale systems that stand on solid engineering principles, ready to thrive in the real world.

Check out DesognGurus.io for resources related to system-design.