Eager vs Lazy Loading: Best Data Fetching Strategies for Large-Scale Web Apps?
I'm building a large-scale web app with Django in Python (I might switch to Flask), and I'm trying to optimize how I fetch data. Specifically, I’m debating between eager loading (fetching all data upfront) and lazy loading (fetching data on-demand) for large datasets with complex relationships (e.g., nested data, foreign keys).
My main challenges are over-fetching (retrieving too much data upfront), N+1 query problem (multiple unnecessary queries), user perceived latency (delays in loading data). What are the trade-offs between these approaches, and how can I decide when to use one over the other? Any tips on optimizing data fetching for performance while handling large volumes and real-time updates?
TL;DR: How do you decide when to use eager vs lazy loading for large-scale apps with complex data and real-time updates?
QuerySets are lazy, which is useful to improve performance, but probably even more because you can further filter down a QuerySet, paginate it, etc.
N+1 query problem
You can fix this by using .prefetch_related(…) [Django-doc] which will, if you evaluate the QuerySet, also fetch the related data in one extra query. If the relations are more complex, you can even use a Prefetch object [Django-doc].
A lot of tooling also does not use the QuerySets to the full extent. For example one can use .only(…) [Django-doc] to only retrieve certain columns minimizing bandwidth between the database and the application.