As a developer building a performance-critical application with FluentState, I want to optimize how state updates are processed and monitored, so that I can minimize performance bottlenecks, reduce unnecessary re-renders, and gain insights into my application's state management behavior.

Acceptance Criteria:

1. Batched Updates

   • Multiple state updates occurring within the configured time window are batched into a single update
   • The batchTimeWindow parameter controls how long to wait before processing batched updates
   • Batching can be enabled/disabled globally via the batchUpdates configuration option
   • Batched updates maintain the same final state as if updates were processed individually

2. Memoization Support

   • When enableMemoization is true, derived values from state are cached and only recalculated when dependencies change
   • Memoization works with both simple and complex state structures
   • Memoized values are properly invalidated when their dependencies change
   • Memory usage is optimized by clearing unused memoized values

3. Custom Equality Checking

   • Developers can provide a custom areEqual function to determine if state has actually changed
   • The custom equality function is used to prevent unnecessary updates when state is structurally equivalent
   • Default equality checking uses a shallow comparison for objects and arrays
   • Custom equality checking works with all state update methods

4. Performance Metrics

   • When metrics.enabled is true, the state manager collects performance data
   • Update frequency, duration, and count are tracked when measureUpdates is true
   • Memory usage statistics are collected when measureMemory is true
   • Computation timing for equality checks, memoization, and derivations is measured when measureComputations is true
   • Metrics are provided to the onMetrics callback for custom reporting or visualization
   • Metrics collection has minimal impact on overall performance