Managing large-scale recursive backend workflows in Bubble.io without hitting capacity or timing limits requires careful design. Bubble has execution time limits per workflow (typically around 5 minutes) and caps on how many things can be processed per step, especially in recursive loops.
To optimize performance:
Chunk your data: Instead of processing all items at once, break them into smaller batches (e.g., 50–100 items) per workflow run. This reduces memory load and runtime per execution.
Use delay or scheduling: Use Schedule API Workflow on a list or recursive Schedule API Workflow with a slight delay (e.g., 5–10 seconds between calls) to spread processing over time and avoid rate limits.
Track progress: Store a “last processed ID” or timestamp to continue processing from where the last batch ended. This avoids duplicate or skipped entries.
Leverage backend triggers: Trigger workflows only when needed, using conditions to skip unnecessary operations.
Monitor capacity: Regularly check your app’s logs and capacity usage. Consider upgrading your Bubble plan if you frequently approach limits.
In short, efficient batching, scheduling, and state tracking are key to scaling recursive workflows in Bubble without hitting performance walls.
Thanks
Dalip