INDEX // ARCHITECTURE_TOPOLOGIES
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SYSTEMS_ARCHITECTURE #02 // PRODUCTION CASE STUDY

Distributed Real-Time Log & Trace Telemetry Pipeline

Multi-region observability aggregator handling 40,000 trace spans/second with Grafana Tempo integration.

Performance TuningKubernetesOpenTelemetryApache Kafka
Verified Production & Scale Metrics
Ingested 40k spans/sec; cut production MTTR by 65% across 22 microservices.
01 // Problem Statement & Architectural Context

Engineering teams lacked end-to-end trace correlation across polyglot microservices (Java, Go, and Node.js), causing mean time to resolution (MTTR) for critical production incidents to exceed 4 hours.

02 // Systems Topology & Architecture MDX Specification

Architecture Blueprint

  • Collection Layer: OpenTelemetry Collector DaemonSets deployed on Kubernetes nodes scraping stdout logs and receiving OTLP spans over gRPC.
  • Buffering & Rate Limiting: High-throughput Kafka queue absorbing downstream APM write spikes during service outages.
  • Storage & Indexing: Prometheus for metric time-series, Grafana Loki for log aggregation, and Jaeger for trace graph visualization.
  • Service Mesh: Envoy proxy injecting W3C Trace Context headers (traceparent) across all ingress and inter-service HTTP calls.
03 // Failure Modes, Root Cause Analysis & Mitigations

Addressed high cardinality trace volume causing collector memory spikes by introducing tail-based sampling rules (100% of HTTP 5xx and slow spans >500ms captured; 5% of healthy 200 OK spans sampled).

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