Introduction Generating unique identifiers (IDs) in distributed systems is critical for ensuring data integrity, scalability, and reliability across multiple nodes, such as in databases, caches, or message queues. Unique IDs are used to identify records, transactions, or events (e.g., user…

Introduction Idempotency is a critical concept in distributed systems, ensuring that operations can be safely retried without causing unintended side effects, thereby enhancing reliability and fault tolerance. In distributed environments, where network failures, retries, and partial failures are common, idempotency…

Introduction Consistent hashing is a distributed systems technique designed to efficiently distribute data and workloads across a dynamic set of nodes (e.g., servers, caches, or databases) while minimizing data movement when nodes are added or removed. It addresses the limitations…

Introduction In distributed systems, consistency models define how data updates propagate across nodes and what guarantees are provided to clients reading that data. Two fundamental models are Strong Consistency and Eventual Consistency, each offering distinct guarantees and trade-offs that impact…

Introduction The CAP Theorem, proposed by Eric Brewer in 2000 and later proven by Gilbert and Lynch in 2002, is a foundational principle in distributed systems design. It states that a distributed system cannot simultaneously guarantee Consistency (C), Availability (A),…

Introduction Content Delivery Networks (CDNs) are distributed systems designed to optimize the delivery of static and dynamic content by caching data at edge locations closer to users, reducing latency, improving throughput, and offloading origin servers. CDNs are critical for applications…

Introduction Latency, the time delay between a request and its response, is a critical performance metric in modern distributed systems, directly impacting user experience, system throughput, and scalability. High latency can degrade application performance, leading to user dissatisfaction (e.g., 100ms…

Introduction A Bloom filter is a probabilistic data structure designed for space-efficient membership testing, enabling rapid queries to determine whether an element is likely present in a set. It is widely used in applications requiring high performance and low memory…

Introduction Redis (Remote Dictionary Server) is a high-performance, in-memory, key-value data store celebrated for its sub-millisecond latency (< 1ms), high throughput (up to 100,000 req/s per node), and versatility across diverse applications. Its optimized architecture, efficient data structures (e.g., Hash…