Currently, cachier provides no built-in way to monitor cache performance in production.
Users cannot track cache hit/miss rates, measure cache effectiveness, monitor memory/disk
usage, or identify performance bottlenecks. For production systems with multiple cached
functions across different backends, understanding cache behavior is critical for
optimization and debugging.

Proposed Solution:
Implement a comprehensive analytics framework that collects metrics at the decorator level
and core level, including:

• Per-function cache hit/miss rates and ratios
• Cache operation latency (read/write/invalidation times)
• Cache size metrics (entry counts, storage size per backend)
• Stale cache access patterns and recalculation frequencies
• Thread contention and wait times (especially for wait_for_calc_timeout scenarios)
• Entry size distribution and entry_size_limit rejection counts

The framework should provide:

1. ACacheMetrics class accessible viacached_function.metrics
2. Pluggable exporters for Prometheus, StatsD, CloudWatch, and custom backends
3. Configurable sampling rates to minimize performance impact
4. Aggregation across multiple function instances
5. Time-windowed metrics (last minute, hour, day)

Example Usage:

fromcachierimportcachierfromcachier.metricsimportPrometheusExporter@cachier(backend='redis',enable_metrics=True)defexpensive_operation(x):returnx**2# Access metrics programmaticallystats=expensive_operation.metrics.get_stats()print(f"Hit rate:{stats.hit_rate}%, Avg latency:{stats.avg_latency_ms}ms")# Export to monitoring systemexporter=PrometheusExporter(port=9090)exporter.register_function(expensive_operation)

Technical Challenges:

• Minimizing performance overhead of metrics collection (use atomic operations, sampling)
• Thread-safe metrics aggregation across concurrent calls
• Backend-specific metrics (e.g., Redis connection pool stats, MongoDB query times)
• Handling metrics persistence across process restarts
• Supporting distributed aggregation for multi-instance deployments

Value:
Enables production observability, performance optimization, and data-driven cache tuning
decisions. Critical for systems with high cache utilization.