5h ago · 13 min read · Source: Methods to Handle Time Drift Issues in Distributed Systems Many production incidents start with a moment of cognitive dissonance: logs from service A show an event happening "after" an event in service B, but tracing reveals the opposite. D...
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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 · 34 min read · TLDR: Distributed systems produce anomalies not because the code is buggy — but because physics makes it impossible to be perfectly consistent, available, and partition-tolerant simultaneously. Split brain, stale reads, clock skew, causality violatio...
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Apr 5 · 35 min read · TLDR: NoSQL databases trade cross-entity atomicity for scale — and every database draws that atomicity boundary in a different place. MongoDB's boundary is the document (pre-4.0) or the replica set (4.0+ multi-doc transactions). DynamoDB's boundary i...
CCamma commentedApr 5 · 8 min read · TLDR: In distributed systems, consistency is a spectrum of trade-offs between latency, availability, and correctness. By leveraging session-based patterns like Read-Your-Own-Writes and formal Quorum logic ($W+R > N$), architects can provide the illus...
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Apr 5 · 25 min read · TLDR: Isolation levels control which concurrency anomalies a transaction can see. Read Committed (PostgreSQL and Oracle's default) prevents dirty reads but still silently allows non-repeatable reads, write skew, and lost updates. Repeatable Read adds...
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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 discussionApr 5 · 22 min read · TLDR: Any service that writes to a database and publishes a message in the same logical operation has a dual write problem. try/catch retries don't fix it — they turn failures into duplicates. The Transactional Outbox pattern co-writes business data ...
Join discussionApr 5 · 29 min read · TLDR: Database anomalies are the predictable side-effects of concurrent transactions — dirty reads, phantom reads, write skew, and lost updates. SQL databases use MVCC and isolation levels to prevent them; PostgreSQL's Serializable Snapshot Isolation...
Join discussionApr 5 · 11 min read · Source: Reasons Your Unique Constraint Is Not a Complete Concurrency Strategy You shipped a system where a unique index guards a business invariant. For a while everything looked fine: a unique constraint rejected duplicates, your logs showed an oc...
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