Distributed Systems
8 posts — newest first.
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Idempotency Keys: The Thing That Makes a Retry Safe
A timeout never tells you whether the work happened. Idempotency keys resolve that — if you get the concurrency and scope right.
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Rate Limiting Algorithms: Token Bucket, Leaky Bucket, and Sliding Window, Compared
Five algorithms, one trade-off: burst tolerance vs memory vs precision. Plus why LLM APIs limit tokens instead of requests.
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Retries, Backoff, Jitter, and Circuit Breakers: The Four Controls That Decide Whether You Recover or Melt Down
A retry is a load multiplier. Backoff spreads it, jitter decorrelates it, a breaker stops it — and agent loops break all three.
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The CAP theorem in AI-native distributed systems
CAP didn't get repealed when LLMs showed up. How the C/A/P trade-offs shift when the datastore is a vector index, context graph, or retrieval layer.
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Queues and Message Brokers: The Shock Absorber of Distributed Systems
A queue decouples producers from consumers and absorbs bursts. Backpressure, at-least-once delivery, idempotency, DLQs — now in front of every LLM call.
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ACID, BASE, and Isolation Levels: What 'Consistent' Actually Means
ACID promises correctness; BASE trades it for scale — and your default isolation level is weaker than you think. A refresher on what 'consistent' means.
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Consistent Hashing: How Distributed Systems Add and Remove Nodes Without Chaos
Hash-mod-N reshuffles almost everything when a node leaves; consistent hashing moves a fraction. The ring, virtual nodes, and why your CDN depends on it.
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The CAP theorem
Consistency, availability, partition tolerance — you get two. A walk through the CAP trade-offs, database by database.