50+ system architecture diagrams covering load balancers, caches, databases, message queues, and real-world systems
This interactive reader gives you a comprehensive overview of System Design Cheat Sheets. In the full product you will find:
Software engineers preparing for system design interviews at top tech companies. From L5 to staff-level loops.
The first two chapters of this guide are available free. Use the table of contents on the left to navigate. The remaining chapters are available in the full product.
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| Chapter | Title | Description |
|---|---|---|
| 1 | System Design Fundamentals | Core building blocks: load balancers, caching strategies, database selection, message queues, and API design patterns that form the foundation of every distributed system. |
| 2 | Designing a URL Shortener | End-to-end walkthrough of designing a URL shortener like TinyURL. Requirements, estimations, data model, API design, and trade-off analysis. |
| 3+ | Full Product | All remaining chapters with complete content |
System design is the process of defining the architecture, components, modules, interfaces, and data flow of a system to satisfy specified requirements. It bridges the gap between requirements gathering and implementation, ensuring the system is scalable, reliable, maintainable, and cost-effective.
System design answers the question: *How do we build this at scale?* It involves making architectural choices about how software components interact, how data flows through the system, how the system handles failure, and how it grows with user demand. The goal is to produce a blueprint that guides implementation teams and documents trade-offs.
Every system design decision involves trade-offs. Recognizing them is the most important skill in any system design interview.
| Trade-off | Description |
|---|---|
| Latency vs Throughput | Low latency (fast single request) often reduces throughput (total requests/sec). Batch processing improves throughput at the cost of latency. |
| Consistency vs Availability | Strict consistency requires coordinating replicas, which reduces availability during partitions. Relaxed consistency improves uptime but can serve stale data. |
| Read vs Write Optimization | Systems optimized for fast reads (e.g., denormalized reads, caches) often have slower or more complex writes, and vice versa. |
| Cost vs Performance | Adding more servers or faster hardware improves performance but increases operational cost. |
The CAP theorem states that a distributed data store can provide only two of three guarantees simultaneously:
In practice, networks fail (partitions happen), so you choose between CP (sacrifice availability) and AP (sacrifice consistency). Real systems often offer tunable consistency levels instead of binary choices.
Distributes incoming traffic across multiple servers to prevent any single node from becoming a bottleneck. Common algorithms: round-robin, least connections, IP hash. Load balancers also handle health checks and automatic failover.
Stores frequently accessed data in fast memory (Redis, Memcached) to reduce database load and response times. Cache strategies include cache-aside, write-through, write-behind, and refresh-ahead. The key challenge is managing cache invalidation and staleness.
Splits a database across multiple servers by partitioning data (e.g., by user ID hash or geographic region). Sharding improves write throughput and keeps dataset sizes manageable but complicates queries that span shards and makes rebalancing difficult.
Decouple producers from consumers using durable message brokers (Kafka, RabbitMQ). Producers write events to a queue; consumers process them asynchronously. This pattern enables load smoothing, fault isolation, and independent scaling of services.
Distribute static and dynamic content across a global network of edge servers to minimize latency for users worldwide. CDNs cache content at locations physically close to users, reducing round-trip time and origin server load.
1. Clarify requirements — Functional and non-functional. Ask about scale, traffic patterns, consistency needs, and constraints.
2. Estimate capacity — Rough calculations for QPS, storage, bandwidth, and cache size. Order-of-magnitude estimates are fine.
3. Design a high-level architecture — Draw the main components: client, API gateway, load balancer, application servers, database, cache, CDN.
4. Deep-dive into components — For each major piece, discuss specific technologies, data models, API contracts, and trade-offs.
5. Address bottlenecks — Identify single points of failure, hotspots, and scaling limits. Propose mitigations.
6. Summarize trade-offs — End by recapping key decisions and what you sacrificed for each.
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