Our e-commerce dashboard automations keep choking during sales spikes. Manual cache adjustments can’t keep up with traffic patterns. Latenode’s visual workflow docs mention adaptive caching but I’m not seeing the node for it. How are people setting up self-adjusting TTLs or cache sizes based on real-time load?
Any examples using their data enrichment nodes to inform caching decisions? Need something that scales beyond basic time-based rules.
Combine the HTTP metrics node with delay nodes. We track response times and automatically reduce cache TTL when error rates spike. Made a template if you want it - adjusts cache rules based on 5 performance metrics. https://latenode.com
Used the weather API node surprisingly - when expecting storms (more mobile users), we cache longer. For our delivery tracking system, paired Latenode with Cloudflare API to purge geo-specific caches when regional issues occur.
Created a learning system using past scenario run data. If average execution time crosses threshold, gradually increases cache duration. Uses Latenode’s sub-scenarios to isolate the caching logic from main workflows. Takes 2-3 days to adapt but stabilized our holiday traffic.
Implement Markov chain-based prediction in code nodes. Forecasts load patterns and pre-adjusts cache settings. Integrates with Latenode’s scheduler to apply different rules for weekdays/weekends. Requires some JS but their debugger helps optimize the transitions.
hook up prometheus metrics to latenode webhooks. cache rules auto-adjust based on kpis. saved 30% cloud costs
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