System Design: How to Design a Scalable System and Ace the Interview

## Introduction Imagine your service suddenly goes viral. Users arrive by the thousands per minute, but the system starts to slow down and then crashes entirely. Sound familiar? This is the classic problem of lacking a well-thought-out architecture. System Design is the art and science of designing distributed systems that can withstand high loads, remain fault-tolerant, and scale easily. In this article, we'll break down the key principles and patterns that will help you not only prepare for a System Design interview but also create truly reliable solutions. ## What is System Design and Why Is It Important? System Design is the process of defining the architecture, components, modules, and interfaces of a system to meet specified requirements. In the era of microservices, cloud computing, and big data, the ability to design scalable systems has become a mandatory skill for senior developers and architects. In interviews, you may be asked to design Twitter, YouTube, or Uber—and how clearly you understand the trade-offs determines your success. ## CAP Theorem: The Foundation of Distributed Systems The CAP theorem (Brewer's theorem) is a cornerstone of System Design. It states that in a distributed system, it is impossible to simultaneously guarantee all three properties: | Property | Description | |----------|-------------| | **C**onsistency | All nodes see the same data at the same time | | **A**vailability | Every request receives a response (not necessarily correct) | | **P**artition tolerance | The system continues to operate despite network partitions between nodes | In practice, you choose **CP** (consistency + partition tolerance) or **AP** (availability + partition tolerance). For example, banking systems typically choose CP, while social networks choose AP. This trade-off determines how the service behaves during failures. ## Scaling: Vertical vs Horizontal When load increases, you need to boost performance. There are two approaches: - **Vertical scaling (scale up)** — adding resources (CPU, RAM) to a single server. Simple, but has physical limits and high cost. - **Horizontal scaling (scale out)** — adding new servers. More flexible, cheaper in the long run, but requires complex architecture—load balancers, sharding, and replication. For modern high-load systems, horizontal scaling is the standard. For example, Netflix or Amazon use thousands of servers to distribute traffic. ## Sharding: How to Partition Data Sharding (partitioning) is splitting a database into independent parts (shards), each stored on a separate server. This distributes load and increases capacity. Main strategies: - **Range-based** — data is distributed by key ranges (e.g., users A-M on one shard, N-Z on another). Simple, but can lead to imbalance. - **Hash-based** — the key is hashed, and the shard is chosen by the remainder of division. Ensures even distribution, but adding new shards is complex. - **Geographic** — data is stored closer to the user (reduces latency). Example: In a pizza ordering system, sharding by city allows local request processing. ## Caching: Speeding Up Response Caching is temporarily storing frequently requested data in fast storage (e.g., Redis or Memcached). This reduces database load and decreases response time. Important: - **Cache-Aside** — the application first checks the cache; if missing, reads from the DB and populates the cache. - **Write-Through** — data is written simultaneously to the DB and cache. - **TTL (Time-To-Live)** — cache expiration to prevent stale data. Cache invalidation strategy is one of the hardest tasks. Poorly configured caching can lead to reading outdated data or cascading failures (cache stampede). ## Load Balancing and Fault Tolerance

System Design: How to Design a Scalable System and Ace the Interview

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