# system-design-101 | ByteByteGoHq | Knowledge Base | Instagit

Explain complex systems using visuals and simple terms. Help you prepare for system design interviews.

GitHub Stars: 79.9k

Repository: https://github.com/ByteByteGoHq/system-design-101

---

## Articles

### [Common Causes of Deadlocks and How to Prevent Them](/ByteByteGoHq/system-design-101/what-are-the-common-causes-of-deadlocks-and-how-to-prevent-them)

Learn common deadlock causes and prevention strategies like resource ordering and lock-free algorithms. Understand circular waits and eliminate Coffman conditions for robust systems.

- Tags: deep-dive
- Published: 2026-02-28

### [How the 12-Factor App Methodology Applies to Modern Cloud Deployments](/ByteByteGoHq/system-design-101/how-does-the-12-factor-app-methodology-apply-to-modern-cloud-deployments)

Discover how the 12-factor app methodology guides modern cloud deployments on containerized and serverless platforms. Enhance portability, scalability, and maintainability.

- Tags: best-practices
- Published: 2026-02-28

### [Security Implications of JWT vs Session-Based Authentication](/ByteByteGoHq/system-design-101/what-are-the-security-implications-of-jwt-vs-session-based-authentication)

Explore JWT vs session-based authentication security. Learn stateless JWT scalability and CSRF resistance versus centralized session revocation plus CSRF risks. Make informed choices.

- Tags: deep-dive
- Published: 2026-02-28

### [How to Design for High Availability in Cloud-Native Applications: Architectural Patterns and Best Practices](/ByteByteGoHq/system-design-101/how-do-you-design-for-high-availability-in-cloud-native-applications)

Learn architectural patterns and best practices for designing high availability in cloud-native applications. Eliminate single points of failure with redundancy and monitoring.

- Tags: best-practices
- Published: 2026-02-28

### [Trade-offs Between Vertical and Horizontal Partitioning: Database Scaling Strategies](/ByteByteGoHq/system-design-101/what-are-the-trade-offs-between-vertical-and-horizontal-partitioning)

Explore vertical vs horizontal partitioning trade-offs for database scaling. Understand column splitting for storage and row distribution for horizontal scaling benefits and challenges.

- Tags: deep-dive
- Published: 2026-02-28

### [How Nginx Achieves High Performance as a Reverse Proxy: 7 Architectural Optimizations](/ByteByteGoHq/system-design-101/how-does-nginx-achieve-high-performance-as-a-reverse-proxy)

Discover how Nginx achieves high performance as a reverse proxy with its event-driven, non-blocking architecture. Learn about 7 key optimizations handling thousands of connections efficiently.

- Tags: architecture
- Published: 2026-02-28

### [Key Differences Between Synchronous and Asynchronous Messaging Patterns](/ByteByteGoHq/system-design-101/what-are-the-key-differences-between-synchronous-and-asynchronous-messaging-patterns)

Understand the key differences between synchronous and asynchronous messaging. Learn how asynchronous patterns offer higher throughput and resilience by decoupling producers and consumers.

- Tags: deep-dive
- Published: 2026-02-28

### [How to Handle Hotspot Accounts in High‑Traffic Payment Systems: 3 Architectural Strategies](/ByteByteGoHq/system-design-101/how-do-you-handle-hotspot-accounts-in-high-traffic-payment-systems)

Learn to handle hotspot accounts in high-traffic payment systems with 3 strategies: rate limiting, sub-account sharding, and cache-first writes for high throughput and no row-lock contention.

- Tags: architecture
- Published: 2026-02-28

### [The 10 Most Important Kubernetes Design Patterns for Production](/ByteByteGoHq/system-design-101/what-are-the-most-important-kubernetes-design-patterns-for-production)

Master 10 essential Kubernetes design patterns for production workloads. Ensure reliability, scalability, and automation with these proven strategies.

- Tags: best-practices
- Published: 2026-02-28

### [How Does the CAP Theorem Affect Database Design Choices? A Practical Guide](/ByteByteGoHq/system-design-101/how-does-the-cap-theorem-affect-database-design-choices)

Understand how the CAP theorem impacts database design. Learn to choose between CP and AP systems for your specific application needs, from financial data to social feeds.

- Tags: deep-dive
- Published: 2026-02-28

### [Distributed Locks Best Practices: 10 Rules for Production Systems](/ByteByteGoHq/system-design-101/what-are-the-best-practices-for-implementing-distributed-locks)

Master distributed locks best practices for production systems. Learn to implement them securely with Redis SET NX PX, unique IDs, Lua scripts, and short hold times to prevent deadlocks.

- Tags: best-practices
- Published: 2026-02-28

### [How to Design for Eventual Consistency in Distributed Databases: 4 Architectural Patterns](/ByteByteGoHq/system-design-101/how-do-you-design-for-eventual-consistency-in-distributed-databases)

Master eventual consistency in distributed databases. Explore 4 architectural patterns like event-driven messaging and CQRS for successful data synchronization.

- Tags: architecture
- Published: 2026-02-28

### [How Does Redis Persist Data? A Deep Dive into AOF, RDB, and Trade-offs](/ByteByteGoHq/system-design-101/how-does-redis-persist-data-and-what-are-the-trade-offs)

Discover how Redis persists data using AOF, RDB, and mixed strategies. Understand the trade-offs in durability, recovery speed, and write latency to optimize your Redis setup.

- Tags: deep-dive
- Published: 2026-02-28

### [UDP vs TCP in Distributed Systems: Real‑World Use Cases and Decision Guide](/ByteByteGoHq/system-design-101/what-are-the-real-world-use-cases-for-udp-vs-tcp-in-distributed-systems)

Understand UDP vs TCP in distributed systems. Learn real-world use cases for reliable workloads like databases and latency-sensitive apps like streaming and gaming. Make informed decisions.

- Tags: deep-dive
- Published: 2026-02-28

### [How Consistent Hashing Minimizes Data Movement During Cluster Rebalancing](/ByteByteGoHq/system-design-101/how-does-consistent-hashing-minimize-data-movement-during-cluster-rebalancing)

Learn how consistent hashing minimizes data movement during cluster rebalancing by mapping nodes and keys to a circular ring, reducing key migration needs.

- Tags: deep-dive
- Published: 2026-02-28

### [How to Implement the Read Replica Pattern for Database Scaling](/ByteByteGoHq/system-design-101/how-do-you-implement-the-read-replica-pattern-for-database-scaling)

Easily implement the read replica pattern for database scaling. Separate reads from writes to handle heavy workloads efficiently using application code or middleware.

- Tags: how-to-guide
- Published: 2026-02-28

### [Trade‑Offs Between Optimistic and Pessimistic Locking in Databases: A Complete Guide](/ByteByteGoHq/system-design-101/what-are-the-trade-offs-between-optimistic-and-pessimistic-locking-in-databases)

Explore optimistic vs pessimistic locking trade-offs in databases. Learn how to prevent concurrent modifications and detect collisions for optimal performance and consistency. Maximize throughput with version checks.

- Tags: deep-dive
- Published: 2026-02-28

### [How Does Kafka Achieve High Throughput and Low Latency?](/ByteByteGoHq/system-design-101/how-does-kafka-achieve-high-throughput-and-low-latency)

Discover how Kafka achieves high throughput and low latency with sequential I/O, zero-copy networking, immutable logs, and partition parallelism. Optimize your streaming data.

- Tags: deep-dive
- Published: 2026-02-28

### [Key Differences Between Load Balancers, Reverse Proxies, and API Gateways](/ByteByteGoHq/system-design-101/what-are-the-key-differences-between-load-balancers-reverse-proxies-and-api-gateways)

Understand load balancers, reverse proxies, and API gateways. Learn their key differences in traffic distribution, security, and microservice management for better system design.

- Tags: deep-dive
- Published: 2026-02-28

### [Why Consistent Hashing Is Essential for Distributed Systems at Scale](/ByteByteGoHq/system-design-101/why-is-consistent-hashing-important-for-distributed-systems-at-scale)

Discover why consistent hashing is essential for distributed systems at scale. Learn how it minimizes data rebalancing and prevents cascading failures when scaling up or down.

- Tags: deep-dive
- Published: 2026-02-28