System Designs

Overview

System design interviews have become increasingly popular in recent years, especially for senior and leadership-level roles where they hold significant weight. These interviews are not only a test of your ability to architect scalable and reliable systems but also an assessment of your depth and breadth of knowledge as a software engineer.

A system design interview can take many different directions. You might be asked to design a system you’re already familiar with—this would be fortunate, as you can leverage your existing expertise. However, you may also be challenged to design something entirely outside your experience, requiring you to think on your feet and apply general principles of system design to unfamiliar domains.

Given the unpredictable nature of these interviews, I recommend dividing your preparation into two key areas:

  • Broadening Your Knowledge: Focus on understanding classic systems and patterns commonly discussed in system design interviews. This includes topics like load balancing, database sharding, distributed caching, message queues, and scaling web services.
  • Deepening Your Expertise: Dive deeply into the systems you’ve worked on in your current and past roles. These are systems where you already have hands-on experience, so you can speak about them with confidence.

Why Split Your Preparation? Balancing breadth and depth in your preparation ensures you’re ready for any scenario. Broad knowledge allows you to handle unfamiliar questions by relying on foundational design principles and patterns. Meanwhile, deep knowledge of your past work demonstrates expertise, practical experience, and your ability to solve real-world problems effectively. With a combination of these two approaches, you’ll be well-prepared to excel in system design interviews, regardless of the specific challenges presented.

Framework of a Solution

Approaching a system design interview can feel overwhelming due to its open-ended nature. However, having a clear and repeatable framework can significantly reduce the stress and help you tackle any problem systematically. Based on my experience, I recommend breaking down the process into the following three steps:

Step 1: Confirm the Requirements

Before diving into the design, it’s crucial to fully understand what the system is expected to achieve. Misunderstanding or skipping this step can lead to a solution that doesn’t align with the interviewer’s expectations. Here’s what to clarify:

  • Functional Requirements: Identify what the system needs to do. What are the key features or actions the system must support? For example, if designing a URL shortener, functional requirements might include shortening URLs, expanding them, and tracking usage metrics.

  • Non-Functional Requirements: Determine the performance, scalability, reliability, and other quality attributes. For instance:

    • How many requests per second must the system handle?
    • What is the acceptable latency for API responses?
    • What level of fault tolerance is required?

  • Back-of-the-Envelope Analysis: Perform rough calculations to estimate the scale of the system. Consider the expected volume of data, traffic, and storage needs. For example:

    • How many users will the system serve?
    • How much storage is required for one year of operation?
    • This analysis helps you make informed decisions later in the design.

Step 2: Design a Minimum Viable Product (MVP)

The next step is to create a simplified version of the system that meets the core requirements. This is not the final, optimized solution but a functional starting point that ensures basic operations. Focus on these components:

  • API Layer: Define the key APIs the system will expose. What are the input and output for each API? Ensure the APIs are intuitive and support the core use cases. For example, for a URL shortener:

    • POST /shorten to create a short URL.
    • GET /expand to retrieve the original URL.
    • Clear API design demonstrates your ability to think from the user’s perspective.

  • Storage Layer: Choose a data storage strategy that supports the MVP. You also need to design the schema of the storage solution you choose. Consider:

    • What kind of database is appropriate (SQL vs. NoSQL)?
    • How will the data be structured and indexed?
    • For the URL shortener example, the storage layer might involve a key-value database where the short URL is the key, and the original URL is the value.

Step 3: Optimize Toward Requirements

Once the MVP is in place, start iterating and optimizing the design to meet the requirements, especially the non-functional ones. Focus on:

  • Scalability: How will the system handle increased traffic? You might add load balancers, implement database sharding, or introduce caching layers.
  • Reliability: What happens if a server fails? Consider adding redundancy, implementing replication, and ensuring fault tolerance.
  • Performance: Can the system meet the required latency? Optimize database queries, implement in-memory caches, and streamline API responses.
  • Cost Efficiency: Is the system designed to minimize unnecessary expenses? Use resources like cloud infrastructure judiciously, and consider cost-effective storage and processing solutions.

During this phase, demonstrate your ability to make trade-offs. For example, prioritizing consistency over availability might be appropriate for a banking application but not for a social media feed.

Make It Your Own

This framework is flexible—you should tailor it to fit your personal style and strengths. The key is consistency. Once you refine a method that works for you, practice it rigorously in every system design interview preparation session. Repeated use of a consistent approach will build confidence and help you present your solutions clearly and effectively.

By following this framework, you’ll be equipped to approach system design interviews methodically and deliver thoughtful, well-structured solutions.

Expand Your Breadth

Expanding your breadth of knowledge is crucial for system design interview preparation. The goal is to familiarize yourself with the most common design patterns and challenges that frequently appear in these interviews. By tackling problems in diverse domains, you’ll develop a solid foundation of principles and techniques that can be applied to a wide variety of scenarios.

Common Systems

Below are some classic system types often encountered in system design interviews, along with examples:

Write-Light, Read-Heavy Systems

Example: URL Shortener Services Characteristics: These systems prioritize fast read operations with relatively infrequent writes. Scalability and caching are key considerations. Techniques: Implement distributed caching, consistent hashing for database partitioning, and CDN integration for fast retrieval.

Write-Heavy Transactional Systems

  • Example: Ticket Booking Services
  • Characteristics: These systems handle frequent write operations with strong consistency requirements to avoid issues like overbooking.
  • Techniques: Use transactional databases, distributed locks, and optimistic or pessimistic concurrency control.

High-Throughput Systems

  • Example: Logging Query Systems
  • Characteristics: Designed to handle a massive influx of data with efficient querying capabilities.
  • Techniques: Implement distributed storage (e.g., Elasticsearch), indexing strategies, and partitioning to support high-speed queries.

Ultra-Low-Latency Systems

  • Example: Monitoring and Alerting Systems
  • Characteristics: These systems require real-time data processing and extremely low response times to ensure timely alerts.
  • Techniques: Use in-memory databases, stream processing frameworks (e.g., Apache Kafka and Flink), and efficient data pipelines.

Event-Driven Systems

  • Example: Logging Ingestion Systems
  • Characteristics: These systems process and react to a continuous stream of events in near real-time.
  • Techniques: Employ message queues, pub/sub architectures, and event sourcing for reliable and scalable ingestion.

Maps-Related Systems

  • Example: Ride-Sharing Systems
  • Characteristics: Systems involving geospatial data processing, real-time updates, and route optimization.
  • Techniques: Use geohashing, spatial indexing, and algorithms like Dijkstra’s or A* for routing.

Data Pipelines

  • Example: Reporting and Analytics Systems
  • Characteristics: These systems ingest, process, and store large volumes of data to generate * insights or reports.
  • Techniques: Use ETL pipelines, data warehouses, and batch or stream processing frameworks like Apache Spark.

Recommendation Systems

  • Example: E-Commerce or Content Recommendation Systems
  • Characteristics: Personalize suggestions based on user behavior or preferences.
  • Techniques: Implement collaborative filtering, content-based filtering, and ranking algorithms.

How to Use This Knowledge

For each of these system types, I recommend practicing by applying the Framework of a Solution outlined earlier:

  • Understand the Requirements: Define both functional and non-functional requirements specific to the system type.

  • Design an MVP: Start with a basic implementation to ensure core functionality. Dive into Optimizations: Explore scalability, performance, reliability, and cost trade-offs to refine the design.

  • Go Beyond the MVP It’s not sufficient to stop at the Minimum Viable Product (MVP) stage in your preparation. Interviews often probe deeper, exploring potential optimizations and design decisions. Spend time understanding the common challenges and advanced techniques associated with each type of system. For example:

    • How do you handle caching invalidation in a read-heavy system?
    • What are the trade-offs of using eventual consistency in an event-driven architecture?
    • How would you design fault tolerance in a ticket booking system?

By mastering these classic system types and their nuances, you’ll not only expand your breadth but also be better equipped to handle unfamiliar design problems in the interview.

Sharpen Your Depth

While expanding your breadth of knowledge is important for system design interviews, diving deep into specific systems can give you a unique advantage. However, I do not recommend attempting to master every detail of every type of system. While this might be a valuable learning experience outside of interview preparation, it is not the most effective use of your time when preparing for interviews.

Instead, focus your efforts on deeply understanding the systems you are currently working on or have worked on in the past. Here’s why this approach is more effective and how to make the most of it:

Why Focus on Your Own Systems?

  • Expertise and Context As someone who has directly contributed to these systems, you already have firsthand knowledge of their architecture, challenges, and nuances. You’re familiar with the business requirements, technical constraints, and day-to-day operational realities that shaped the design decisions.

  • Access to Richer Resources Working on a system gives you access to Subject Matter Experts (SMEs) and documentation. You can consult senior team members, architects, and long-tenured colleagues who understand the history of the system, the rationale behind key design choices, and the lessons learned over time.

  • Transferable Insights Many system design problems share common principles and trade-offs. By deeply analyzing the systems you’ve worked on, you’ll gain a better understanding of patterns and pitfalls that can be applied to other problems, even if the domain is different.

How to Dive Deep Effectively

  • Ask the Five Whys. Challenge yourself to understand the root causes behind design decisions by repeatedly asking “Why?” For example:

    • Why did we choose this database technology?
    • Why do we handle data replication in this specific way?
    • Why does this bottleneck exist, and how could we eliminate it?

  • Analyze Design Trade-offs. Investigate the trade-offs that were made during the system’s development. Questions to explore include:

    • What compromises were made for scalability, reliability, or performance?
    • Were there any alternatives considered, and why were they rejected?
    • How might the system’s design differ if we prioritized different constraints (e.g., cost vs. latency)?

  • Understand Past Issues. Dive into the system’s history to uncover past challenges or failures. Learn about:

    • Outages or bottlenecks and how they were resolved.
    • Scalability limitations and the solutions implemented.
    • Missed opportunities or areas for improvement.

  • Propose Hypothetical Redesigns. Challenge yourself to think about how you would redesign the system if given a clean slate. Consider modern technologies, new patterns, and the lessons learned from working on the system. This exercise helps solidify your understanding and prepares you to discuss improvements confidently during interviews.

  • Consult with Team Members. Engage with senior engineers or architects on your team. Ask them about the “why” behind key decisions and seek their perspective on what they would do differently today. This not only broadens your understanding but also sharpens your ability to articulate design rationale effectively.

How This Helps in Interviews

  • Familiar Patterns. If an interview problem resembles a system you’ve worked on, you’ll have a significant advantage. You’ll be able to draw on your real-world experience to articulate design decisions, trade-offs, and solutions with confidence.

  • Universal Trade-Offs. Even if the interview problem is different, the deep understanding you’ve gained will help you apply engineering principles and trade-offs learned from your work. For example, lessons about caching, data replication, or consistency models are often transferable across domains.

  • Storytelling and Credibility. Sharing your hands-on experience with real systems can make your answers more compelling and credible. It demonstrates your practical understanding of system design beyond theoretical knowledge.

Final Thoughts

Sharpening your depth by studying your own systems is an efficient and rewarding way to prepare for system design interviews. It not only strengthens your technical skills but also boosts your confidence during interviews, allowing you to articulate design decisions clearly and persuasively.

By combining this deep knowledge with a broad understanding of classic system patterns, you’ll be well-equipped to excel in even the most challenging system design interviews.

Last updated on