Apr 7 · 7 min read · As modern applications grow, they often move beyond a single database server. Systems that once served a few thousand users may eventually need to support millions of requests across different regions
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Apr 6 · 8 min read · In the world of distributed systems, scalability, availability, and consistency are paramount. For years, the CAP theorem provided a foundational understanding of the trade-offs involved. However, as
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Apr 5 · 8 min read · 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...
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Apr 5 · 23 min read · 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...
Join discussionMar 9 · 14 min read · 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...
Join discussionMar 9 · 13 min read · 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...
Join discussionMar 8 · 8 min read · In the realm of distributed systems, the CAP Theorem is a fundamental principle that every system designer must understand. It states that a distributed data store cannot simultaneously guarantee Cons
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