OC-201b · Module 2

Data Persistence & Storage Patterns

3 min read

Modules that fetch data and discard it are wasting money. Every API call costs tokens, time, and rate limit budget. When you fetch competitor pricing today and throw it away, tomorrow you pay again for the same data. The persistence pattern stores API responses in a local database with timestamps, so your module checks local storage before calling the API. Fresh data serves from cache. Stale data triggers a refresh. The API bill drops. The response time drops further.

The hybrid SQL plus vector storage pattern from OC-201a is the right foundation here. Structured fields (company name, price, date) go into SQL columns for exact queries. Unstructured content (product descriptions, news articles) goes into vector columns for semantic search. Every module that persists data uses the same hybrid pattern. This standardization means your knowledge base, your CRM, your analytics snapshots, and your competitor data all share the same query interface. One pattern, every data type.

Do This

  • Cache every API response with a timestamp and expiration rule
  • Use the hybrid SQL + vector storage pattern for all persistent data
  • Set different cache lifetimes per data type — real-time data expires fast, reference data expires slow
  • Build historical trend analysis on top of cached snapshots — the data is already there

Avoid This

  • Call the API every time when yesterday's data would serve the same purpose
  • Store structured and unstructured data in separate systems with different query interfaces
  • Cache without timestamps — you cannot manage freshness if you do not know when data arrived
  • Delete cached data after use — historical snapshots become exponentially more valuable over time