frappe-ops-performance

To tune MariaDB for slow queries in a Frappe ERPNext deployment, you need to analyze the slow query log and apply database tuning configurations. This optimization resolves memory bottlenecks and stabilizes production performance.

Frappe experiences high memory usage and cache misses in production due to misconfigured Redis memory management. Adjusting Redis caching settings and application server sizing directly addresses these memory bottlenecks.

The best way to size Gunicorn workers for high concurrency in ERPNext is to tune the application server configurations based on your deployment's resource limits. Proper Gunicorn worker tuning handles high concurrency and prevents timeouts.

Yes, this Frappe performance tuning guidance applies to both on-prem and cloud deployments running Frappe v14 through v16. It provides production-grade deployment guidance for reliability and observability across different environments.

Yes, you can use Python profiling workflows to fix Frappe application server timeouts by identifying slow processing logic. Combining profiling with Gunicorn worker tuning and CDN integration optimizes static asset delivery.

A limitation when applying CDN integration to Frappe static assets is that misconfigured cache and app server settings can still cause unstable performance. You must verify measurable improvements in a staging environment before production deployment.