performance

Indexes dramatically improve query performance. I add indexes via Meta.indexes or db_index=True on frequently-filtered fields. Compound indexes help queries filtering on multiple fields. For text search, I use GinIndex on PostgreSQL. I index foreign k

Updating last_seen_at on every request creates hot rows and write amplification. Instead, track last seen in Redis and periodically flush to DB, or only write when the value meaningfully changes.

Database indexes dramatically speed up queries. I add indexes to frequently-filtered fields via db_index=True or Meta.indexes. Compound indexes help queries filtering on multiple fields together. For text search on PostgreSQL, I use GinIndex with Sear

Memoization is useful, but it should be scoped. Memoize within the instance/request, never globally. This is a simple way to avoid repeated expensive DB reads inside a view render.

Set a rough query budget per request in dev/test and alert when exceeded. This is a pragmatic way to keep performance regressions visible without requiring a full APM setup.

For Postgres search beyond trivial ILIKE, maintain a tsvector column and a GIN index. Update it via trigger or application logic. This keeps search fast and predictable even as your dataset grows.

Row-by-row inserts are painfully slow for big ingests. Postgres COPY is a great bulk-ingestion tool, and in Node you can stream into COPY using libraries like pg-copy-streams. The important part is validating before you stream, because once you’re in

I enable strict loading when I want N+1 bugs to fail loudly during development instead of quietly shipping to production. In Enable strict_loading on associations, I mark the relationships that routinely get iterated (has_many :line_items and belongs_