System Design Interview – BIGGEST Mistakes to Avoid

Why This Is a Problem

In a system design interview, how you think is just as important as the final solution. Interviewers aren’t only interested in a polished diagram. They want to understand your decision-making process. If you stay silent during the design phase and present only a finished diagram or solution without explaining your reasoning, interviewer can’t assess how you arrived at your choices. This makes it harder for them to evaluate your problem-solving skills and ability to handle trade-offs. Even a perfect diagram can score poorly if the reasoning behind it isn’t clear.

Better Approach – Think Out Loud

Explain your reasoning step-by-step before presenting your design choices. A clear structure helps you communicate effectively and ensures you don’t skip important details

• Gather Requirements – Clarify functional and non-functional requirements.
• Outline the High-Level Architecture – Identify key components and how they interact.
• Define the Data Model – Explain your database choice and schema.
• Discuss Scaling Strategies – Cover vertical and horizontal scaling options.
• Identify Bottlenecks and Trade-offs – Explain potential pain points and how you’d address them.

By walking the interviewer through your process, you demonstrate clear thinking, technical depth and strong communication skills three qualities every top company looks for.

Many candidates immediately start sketching architecture diagrams without first clarifying the requirements.

If the interviewer says, “Design YouTube”, most candidates start drawing microservices, databases and load balancers before asking key questions like

• Are we designing for millions of daily active users or a small startup MVP?
• Do we need live streaming or just video uploads?
• What’s the latency target for video playback?

Why This Is a Problem

Rushing into the solution without proper requirement gathering can

• Show a lack of analytical and requirement analysis skills.
• Lead to solving the wrong problem.
• Waste valuable interview time on irrelevant or unnecessary features.

The Better Way – Start with Requirements

Spend the first 3–5 minutes asking questions to understand the problem space.

• Functional Requirements – What must the system do?
• Non-functional Requirements – Scale, latency, availability, cost limits.
• Assumptions – Geographic distribution, supported devices, user behavior.

Real-World Case

A large e-commerce company once launched a real-time inventory system that worked fine under normal conditions but collapsed during flash sales. Why? The engineering team never clarified peak load requirements before choosing the architecture, leading to costly redesigns later.

Focusing only on features (functional requirements) while ignoring performance, scalability, reliability and security.

If interviewer asked to design WhatsApp, many candidates immediately jump into features like sending messages, group chats and media sharing. But they forget to ask

• How many concurrent users should the system handle?
• What’s the maximum acceptable message delivery delay?
• Should the service be available 99.9% of the time or higher?
• What level of security is required for message data?

These questions address non-functional requirements, which are essential for building a robust and scalable system.

Why This Is a Problem

In the real world, most systems don’t fail because they lack features. They fail because they can’t handle real-world loads, deliver consistent performance or meet reliability and security expectations. If you ignore NFRs, you risk designing a system that works in theory but collapses under real-world conditions. Ignoring NFRs can lead to

• Poor scalability, making the system unusable at peak times.
• Downtime that damages user trust.
• High latency that frustrates users.
• Overspending on infrastructure due to lack of cost optimization.

The Better Way – Include NFRs in Every Design Discussion

When you walk through your design, always cover

• Scalability – Will you scale vertically (more powerful machines) or horizontally (more machines)?
• Availability – What SLA (Service Level Agreement), SLO (Service Level Objective) and SLI (Service Level Indicator) targets are you aiming for?
• Latency – How fast should responses be? Even milliseconds can impact user perception.
• Cost Efficiency – Can you meet goals without overspending on resources?

Real-World Case

Amazon famously discovered that every 100ms delay in page load reduced sales by 1% a direct reminder that performance is revenue. Similarly, if WhatsApp messages took 10 seconds instead of 1 second to deliver, user engagement would drop drastically.

A great system design is not just about what the system does. It’s about how well it does it, under the conditions it will face in the real world.

Some candidates present one “perfect” design without mentioning alternative approaches or the downsides of their choices.

If interviewer asked to design a payment system, you might choose strong consistency for transaction processing. That’s a valid choice but what if the interviewer asks why you didn’t choose eventual consistency? If you can’t explain the trade-off, it shows a lack of critical architectural thinking.

Why This Is a Problem

In the real world, every design decision comes with some trade-offs there is no perfect solution. Failing to acknowledge them signals to the interviewer that you may not understand the limitations of your own design.

The Better Way – Apply Trade-Off Thinking

Always discuss alternatives and why you chose one over the other. Common trade-off discussions include

• SQL vs NoSQL – Strong consistency vs horizontal scalability.
• Monolith vs Microservices – Faster initial development vs increased operational complexity.
• Strong Consistency vs Eventual Consistency – Reliability vs availability (per the CAP theorem).
• Caching Strategies – Faster responses vs risk of stale data.

Real-World Case

For a payment gateway, strong consistency is critical to avoid double charging customers even if it means slightly reduced availability. On the other hand, for a social media news feed, eventual consistency is often acceptable to achieve lower latency and higher scalability.

A strong system design answer doesn’t just describe what you would build. It explains why you chose that approach, what alternatives you considered and the trade-offs involved.

Some candidates overload their design with every popular technology they know like Kafka, Redis, Kubernetes, AI models, complex CI/CD pipelines etc without considering whether the problem actually requires them. This not only adds unnecessary complexity but can also introduce new points of failure and make the system harder to maintain

If you are designing a small internal tool for just 500 employees, adding an event‑driven architecture with Kafka, sharded databases and container orchestration might seem impressive, but it’s unnecessary. Such complexity increases development and maintenance overhead without providing meaningful benefits for the scale and requirements of the project. In this case, a simpler, more focused design would be far more effective.

Why This Is a Problem

• Introduces unnecessary complexity that can make the system harder to build, maintain and troubleshoot.
• Consumes valuable development and operational resources without delivering proportional benefits.
• Signals a lack of pragmatic decision-making, which interviewers see as a red flag in real-world engineering scenarios.

The Better Way – Start Simple, Evolve with Scale

A Good and skilled engineers design for current needs while keeping future scalability in mind. The smartest approach is to scale in phases

• MVP (Minimum Viable Product) – Build a simple, lightweight design that’s quick to implement and easy to iterate on.
• Optimized Phase – Identify and fix bottlenecks using real-world performance data.
• Distributed Phase – Add advanced infrastructure and distributed components only when scaling requirements genuinely demand them.

Real-World Case

A startup once built a highly complex microservices platform with multiple queues, service discovery and container orchestration before they even had 1,000 active users. As a result, the team spent more time debugging infrastructure issues than building core features, delaying product growth.

Some candidates design a system that works perfectly for the current load but completely overlook how it will handle future growth.

If you are designing a file-sharing service for a startup with 10,000 daily users, you might build a single-server setup with a local database. But what happens when the platform grows to 10 million daily users? Without a scaling plan, your design will break under the increased traffic.

Why This Is a Problem

• Shows a lack of long-term thinking.
• Leads to major re-architecture efforts when the system outgrows its initial design.
• Signals to interviewers that you may not understand scalability fundamentals.

The Better Way – Always Include a Scaling Strategy

When presenting your design, explain how it will grow over time

• Vertical vs Horizontal Scaling – Start simple, but be ready to add more servers or increase their capacity.
• Global Distribution – Use multiple regions or data centers to serve users closer to their location.
• CDNs (Content Delivery Networks) – Reduce latency and offload traffic for static assets.
• Replication and Sharding – Distribute database load for better performance and fault tolerance.

Real-World Insight

Instagram started as a small photo-sharing app but quickly went viral. Because its architecture was built with modular components and used cloud-based scaling strategies, the team could handle massive traffic spikes without major downtime or redesign. This preparation allowed them to grow from thousands to hundreds of millions of users smoothly.

Design for current needs → Plan for future growth, follow below steps to handle these

• Step 1 – Clarify Requirements First
• Step 2 – Start Simple (Avoid Over-Engineering) : Design for Today’s Needs
• Step 3 – Add a Scaling Roadmap (Avoid No Scaling Plan)
• Step 4 – Show Trade-Off Thinking

You can say to interviewer : I follow a phased design approach, starting with simple to deliver value quickly, then evolve the architecture as real-world usage grows. This avoids over-engineering while ensuring the system can scale when needed.

For a messaging app MVP. Start with

• Single database (PostgreSQL)
• Simple backend service
• Basic load balancer

For Scalability, the same messaging app, If daily active users grow from 100K to 10M, you can introduce Redis for caching, partition the database by user ID and use a CDN for media delivery

Many candidates focus heavily on system components and APIs but completely skip database schema design and query patterns during the interview.

If interviewer asked to design a social network’s “News Feed”, you might describe the backend services, caching layers and scaling strategies but forget to show how posts, users and relationships are stored and retrieved. Without a proper data model, the design is incomplete.

Why This Is a Problem

• Data structure drives scalability, performance and functionality — a weak design here can cripple the entire system.
• Poor schema design can lead to slow queries, expensive joins and performance bottlenecks.
• Ignoring data modeling makes it harder for interviewers to evaluate your depth in backend system design.

The Better Way – Always Include Data Modeling

When discussing your system design, make sure to cover

• Key Entities and Relationships – Identify the main data objects (e.g., Users, Posts, Comments, Likes) and describe how they relate to each other.
• Indexing Strategies – Specify indexes that will speed up common queries and improve read performance
• Denormalization – Reduce costly joins by duplicating some data, while explaining the trade-offs in storage and consistency.
• Partitioning & Sharding – Outline how you would split data across multiple servers to enable horizontal scalability.
• Data Access Patterns – Explain how the app will read and write data, ensuring the schema supports these patterns efficiently.

Real-World Example (Case Study of Instagram Feed (Social Media))

In 2012, Instagram faced severe performance issues as its user base exploded. The problem wasn’t the servers. It was poorly optimized data access patterns.

Initially, the feed feature was backed by a schema that required complex joins between the Posts, Users and Follows tables to generate each user’s feed in real-time. As the number of posts and relationships grew, these joins became painfully slow, especially during peak hours.

The team solved this by revisiting the data model

• They denormalized the feed by precomputing and storing posts for each user’s feed in a separate table (UserFeed).
• This avoided repeated expensive joins and allowed the feed to be fetched with a simple, indexed query.
• They also added targeted indexes on fields like user_id and created_at to speed up retrieval.

The result? Feed load times dropped dramatically and the platform could handle massive traffic spikes without collapsing.

Bad data modeling can hurt performance, even with powerful servers. Designing your database early with the proper indexing, denormalization and partitioning can be the deciding factor between a system that grows smoothly and one that struggles to keep up.

Some candidates design a technically sound system but completely forget security, privacy and compliance requirements that is essential factors in real-world systems.

If interviewer asked to design a healthcare application in the United States, you must mention HIPAA compliance for protecting patient data. Similarly, a European e-commerce platform handling personal information must follow GDPR regulations. Skipping these considerations signals a lack of awareness about critical real-world constraints.

Why This Is a Problem:

• Puts sensitive data at risk of breaches.
• Can lead to legal penalties and loss of customer trust.
• Makes your design unrealistic for production environments.

The Better Way – Always Address Security and Compliance:

When presenting your system design, explicitly cover how your solution protects data, controls access and meets legal requirements

• Authentication – Verify the identity of users (e.g., OAuth 2.0, JWT).
• Authorization – Ensure users can only access allowed resources.
• Encryption – Use TLS for data in transit and AES for data at rest.
• Compliance (NIT in system design interview) – Address domain-specific regulations (HIPAA, GDPR, PCI DSS).
• Auditing and Monitoring – Keep logs for security reviews and incident response.