Redis & TheoVale
Redis Redis
TheoVale TheoVale
Redis Redis
Hey Theo, I’ve been wrestling with how to index a massive collection of medieval manuscripts efficiently. Any thoughts on a data model that keeps retrieval times minimal without losing detail?
TheoVale TheoVale
Sure thing, let’s break it down step by step. First, treat each manuscript as a record with a unique ID, then split its metadata into two layers: the core fields you’ll always need—title, author, date, place, language, and a short description—and a flexible set of tags for every other detail like script type, illumination style, parchment condition, and any unique annotations. Put the core fields in a relational table for quick joins, and store the tags in a key‑value store or a full‑text index so you can search by any attribute. For the actual text, use a full‑text search engine like Elasticsearch; index each page as a document with its page number and a tokenized text blob. That way you can run quick phrase queries or fuzzy matches without pulling in the whole manuscript. Finally, keep a separate audit log of edits or translations in a lightweight NoSQL collection so you never lose the provenance. That should keep retrieval times low while preserving every quirk of the originals.
Redis Redis
That’s a solid skeleton, but I’d still be wary of the split‑layer approach. If you keep the core in SQL and the tags in a KV store, you’ll pay the price in cross‑store joins for composite queries. A single document store that supports structured fields and full‑text on those fields might shave some latency, even if it means a slightly more complex query engine. Also, make sure the audit log doesn’t bloat the KV store—perhaps a separate, immutable log with pointers back to the main record. All in all, you’re on the right track, just tighten the glue between layers.
TheoVale TheoVale
I hear you—keeping everything in one shop does cut out the join headache. If you pick a document store that lets you index both the structured fields and run full‑text, you can still keep the audit trail separate with a simple pointer. Just make sure the audit logs are immutable blobs, not another key‑value wall, or you’ll end up chasing ghosts in the data. So, tweak the glue, keep the layers tight, and you’ll have a clean, fast system that still honors every quirk.
Redis Redis
Sounds good, Theo—just remember that even a single document store can get messy if you keep over‑indexing. Keep the audit blobs truly immutable, lock them down, and maybe give them a little timestamp tag. That way, when you finally run a query that hits every field, you’ll get the fastest hit without chasing cross‑store ghosts. Happy indexing.
TheoVale TheoVale
Glad you’re on board—keep it tight, don’t over‑index, lock those audit blobs, and tag them with timestamps so you can pull the latest in a snap. Happy indexing, and if the ghosts ever return, we’ll just haunt them with a clean query.
Redis Redis
Thanks, Theo. I’ll keep the schema lean, the audit logs immutable, and the timestamps tight. If those ghosts ever surface, a precise query will settle them once and for all.
TheoVale TheoVale
Sounds like a plan—just keep the schema lean, the audit logs solid, and the timestamps in order. If those ghosts ever pop up, a tight query will keep them in check. Good luck!