Insights, tutorials, and best practices for modern developers
6 posts
The debate between API paradigms too often devolves into advocacy rather than analysis. This is a practitioner's assessment of where each protocol excels, where each fails, the performance characteristics that matter in production, and the decision framework engineers actually need to make the right choice for their system.
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In a distributed system, partial failure is not an edge case — it is the steady state. The difference between a system that degrades gracefully and one that cascades to a full outage is a set of deliberate architectural patterns applied consistently. This covers circuit breakers, bulkheads, timeout hierarchies, retry strategies, and the fallback design that keeps your system responsive when dependencies fail.
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The choice between synchronous REST and asynchronous event-driven communication is not a matter of preference — it has direct consequences for system coupling, failure isolation, scalability, and operational complexity. This is the decision framework practitioners actually use, including where each model breaks and what hybrid patterns look like at scale.
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The instinct to introduce a specialized data store for every new use case — Redis for caching, MongoDB for flexible schemas, Elasticsearch for search, Pinecone for vectors — is expensive, operationally burdensome, and often unnecessary. PostgreSQL's extended capabilities cover the majority of these use cases with a single, ACID-compliant system you already understand.
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FastAPI's documentation covers the basics well. What it doesn't cover is the operational reality of running it under sustained load with WebSocket connections, background job queues, structured observability, and zero-downtime deployments. This post covers the patterns that separate a dev-mode server from a production system.
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Most RAG implementations work in demos and collapse at scale. This is a systematic breakdown of every failure mode — chunking strategy, embedding model selection, vector store bottlenecks, and retrieval precision — plus the patterns that actually hold up under production load.
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