I’ve been thinking about ways to optimize database performance and came across the idea of handling data joins on the client side instead of in the database. The main benefit seems to be that this approach makes the data much easier to cache effectively.
Someone mentioned that frontend joins might not work well with large datasets, but I’m wondering if there are workarounds. One suggestion I saw was to use data duplication - basically storing some properties from related tables directly in the main table to avoid complex joins.
I’m not too concerned about storage efficiency right now. The focus is more on reducing database load and speeding up response times. Has anyone tried this approach? What are the trade-offs between database joins versus client-side data merging?
On a recent dashboard project, we employed client-side data joining to address performance challenges. The dashboard displayed user data, permissions, and department information, so we opted to cache each table separately with varying TTL values. Given that permissions change frequently while user details tend to remain static, this strategy reduced our database load by 70% during peak hours. However, be cautious with complex relationships, as they can quickly become cumbersome and consume significant memory with large datasets. Implementing effective cache invalidation is crucial to prevent data from becoming outdated. We utilized versioned cache keys linked to modification timestamps, which proved to be highly effective.
Interesting approach! I’m curious though - how do you handle complex client-side joins? What happens when you need to join 3-4 tables on the frontend? Does it turn into a maintenance nightmare or stay manageable?
tried this last year - worked awesome for our reporting system. pulled user data and orders separately, then merged in javascript. api response times dropped 40%, but memory usage spiked with big datasets. test your data volumes first!