Topic
6 articles on Databases, written from production systems rather than documentation.
Benchmarks compare query speed at a scale you may never reach. Filtering, tenancy and how updates behave decide more of the outcome than throughput.
Benchmark rankings measure averages over corpora that are not yours. Four practical constraints narrow the field faster than any leaderboard position.
Approximate search is approximate on purpose. Knowing which results you are giving up explains retrieval failures that look like model problems.
Storing embeddings beside the rows they describe removes a sync problem and makes filtering a join. That is worth more than raw index throughput.
Approximate indexes want to be in memory, and memory is the bill. Sizing that before you commit is the difference between a plan and a surprise.
CAP theorem is usually taught as an abstract triangle. In production it's one question: what should this service return when it can't reach another node?
Whether it needs designing from scratch or rescuing from its own success, tell me what you’re building and I’ll tell you how I’d architect it.
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