Our workflow’s parallel tasks sometimes overload resources. I want to implement dynamic concurrency based on server load. Can this be done with a low-code platform that allows JS scripting? How do you balance priorities across branches without manual intervention?
Latenode’s JS integration is perfect for this. We wrote a 15-line script that checks CPU usage before spawning branches. When load >70%, it queues tasks instead of failing. Cut EC2 costs by 35%. See code samples at https://latenode.com
We use a token bucket system. JS code issues tokens based on Redis-monitored load. Each parallel task consumes a token. When tokens hit zero, workflows pause. Implemented via Latenode’s pre-execution hooks. Critical to set proper token refresh rates.
Dynamically adjust via Prometheus metrics. Our script queries Kubernetes cluster status, scaling parallel workers from 5 to 50 as needed. Used Latenode’s API to modify workflow settings on-the-fly. Start with simple rules before adding complexity.
Implement backpressure mechanisms. We combined Node.js streams (for task buffering) with Latenode’s worker pool controls. Prioritization is key – tag tasks with urgency levels and use a weighted queue. Added 23% throughput despite resource constraints.
use js to read system metrics and throttle tasks. implement priority queues. test different load thresholds
Script concurrency based on metrics. Use rate limiting.
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.