Performance notes

Performance depends on storage backend, indexing choices, payload size, and query patterns.

Storage backend

AutoCRUD uses a storage factory to create a per-model storage instance:

• Memory storage: suitable for development / small datasets; fastest, but not persistent.
• Disk storage (or other persistent backends): suitable for production; performance depends on IO.

If you store blobs, blob store behavior may also depend on the chosen storage factory.

Indexing

Use add_model(indexed_fields=...) to index frequently queried fields.

Trade-offs: - Indexing improves query speed and filtering capability. - Indexing increases write/update cost and storage size.

Listing & pagination

For large datasets: - use paginated list endpoints - avoid returning huge payloads in one request - prefer querying fields that are indexed

Backup / restore

dump() and load() are streaming-based (msgpack archive). For large backups: - run as offline maintenance jobs - expect throughput to be limited by storage IO and payload size