Vector Ingestion at 50M Rows: The Pitfalls Your 1,000-Doc Prototype Won't Reveal
victorialslocum · x · 2026-09-03
A vector search prototype on 1,000 documents says little about ingesting 50 million rows, where mistakes get expensive. Common traps: embedding providers rate-limiting mid-job, HTTP 200 batch responses hiding per-object failures, retries with fresh IDs duplicating objects and re-paying for vectorization, and client crashes from loading the full corpus into memory. Ingestion design must precede the job: server-side batching over persistent connections paced by queue depth, and treating failures as a queue via batch.failedobjects.
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