performance-tuning

To improve AMS Dispatcher cache efficiency, apply systematic tuning guided by baseline capture, runtime verification, and evidence-driven optimization. This process uses MCP tools to inspect cache behavior, monitor metrics, and validate changes against AMS-specific constraints.

The best way to measure AEM Dispatcher performance is by capturing a baseline assessment using runtime checks like trace_request, inspect_cache, and monitor_metrics. This establishes measurable thresholds for cache hit ratios and latency before applying configuration patches.

Yes, this optimization approach is explicitly designed for AMS deployments running AEM 6.5. It applies AMS-specific constraints and safe-change protocols to ensure dispatcher configuration modifications are validated within that environment.

To safely apply patches to dispatcher farms and vhosts, follow the safe-change protocol using static checks like validate, lint, and sdk alongside runtime verification. This produces before/after evidence and rollback guidance to prevent downtime.

Dispatcher configuration may fail validation after tuning if changes violate AMS-specific constraints or static checks. Running validate, lint, and sdk tools identifies configuration blocks causing errors, ensuring patches meet required deployment standards.

To monitor AMS Dispatcher metrics and tail logs, use built-in runtime verification tools like monitor_metrics and tail_logs. These tools provide real-time data on throughput and latency, enabling evidence-driven optimization and continuous performance tracking.