Category
cap theorem
4 articles across 4 sub-topics
Choosing the Right Database: CAP Theorem and Practical Use Cases
TLDR: Database selection is a trade-off between consistency, availability, and scalability. By using the CAP Theorem as a compass and matching your data access patterns to the right storage engine (Relational, Document, KV, or Wide-Column), you can b...
Azure Cosmos DB Consistency Levels Explained: Strong, Bounded Staleness, Session, Consistent Prefix, and Eventual
TLDR: Cosmos DB offers five consistency levels — Strong, Bounded Staleness, Session, Consistent Prefix, Eventual — each with precise, non-obvious internal mechanics. Session does not mean HTTP session; it means a client-side token that tracks what yo...
Understanding Consistency Patterns: An In-Depth Analysis
TLDR TLDR: Consistency is about whether all nodes in a distributed system show the same data at the same time. Strong consistency gives correctness but costs latency. Eventual consistency gives speed but requires tolerance for briefly stale reads. C...
BASE Theorem Explained: How it Stands Against ACID
TLDR TLDR: ACID (Atomicity, Consistency, Isolation, Durability) is the gold standard for banking. BASE (Basically Available, Soft state, Eventual consistency) is the standard for social media. BASE intentionally sacrifices instant accuracy in exchan...