[Feat/Telemetry]: Build a Vectorized Non-Blocking Prometheus Aggregation Pipeline utilizing sync/atomic Flags

Problem

High-frequency logging setups and scraping metrics collectors under issue #6 perform dynamic allocation routines during rapid parallel tool loops. Locking structural counters during parallel scrape windows triggers tracking jitter and minor data leaks inside telemetry nodes.

Proposed Solution

Refactor the metric pipeline tracking layers in internal/telemetry/collector.go to run on top of an independent Vectorized Lock-Free Metrics Collector Layer.

• Store operational event counters using fixed arrays of primitive 64-bit integer values mapped explicitly to individual telemetry dimensions.
• Update tracking counters exclusively utilizing low-overhead atomic addition routines (atomic.AddInt64).
• Stream compiled tracking data segments down to Prometheus text scrapers utilizing optimized multi-buffer vector writers (unix.Writev) to maximize disk I/O throughput.

Alternatives

• Utilizing standard Prometheus telemetry libraries, which provide convenient abstraction APIs but introduce object allocation loops inside critical hot routing connection pipelines.

Use Case

Maintains flawless system-wide observability statistics visibility under intense stress workloads without introducing a single byte of memory heap allocation overhead.

Additional Notes

Expose these optimized telemetry output channels cleanly alongside standard dashboards definitions mapped within deployments configs.

Start by reading internal/telemetry/collector.go to understand the existing metric pipeline tracking layers and how deployment configurations expose dashboard definitions. Compare the current behavior with the requested atomic counters, vectorized output, and zero-allocation stress workload goals. Done means the optimized telemetry channels are exposed cleanly and the pipeline meets those performance requirements.