Platform Engineering

Platform Engineering

Consolidates Observability and Performance Engineering to build high-performance, resilient, and observable systems.

When to Use This Skill

• Observability: Setting up Prometheus/Grafana, OpenTelemetry, logging pipelines.
• Performance: Debugging latency, optimizing DB queries, caching strategies.
• Reliability: Defining SLIs/SLOs, Load Testing, Chaos Engineering.
• Infrastructure: Incident response and post-mortems.

Core Disciplines

1. Observability (The "Three Pillars")

Logs (Events)

• Purpose: Debugging specific events. "What happened?"
• Best Practice: Structured JSON logging. Application-context only (no noise).

Metrics (Aggregates)

• Purpose: Trending and Alerting. "Is it healthy?"
• The RED Method:
  1. Rate: Request throughput (req/sec).
  2. Errors: Error throughput (errors/sec).
  3. Duration: Latency (p50, p99).

Traces (Context)

• Purpose: Distributed transactions. "Where is the latency?"
• Standard: OpenTelemetry (OTel). Ensure context propagation (traceparent header) across all microservices.

2. Performance Engineering

Optimization Workflow

1. Baseline: Measure current state (RPS, Latency).
2. Profile: Flame graphs for CPU/RAM. Traces for I/O.
3. Optimize:
   • Frontend: Core Web Vitals (LCP, CLS, INP).
   • Backend: Fix N+1 queries, add DB indexes.
   • Caching: Memoization (L1) -> Redis (L2) -> CDN (L3).
4. Verify: Load test the fix.

Load Testing (Saturation)

• Smoke Test: Minimal load to verify logic.
• Stress Test: Find the breaking point (Saturation).
• Soak Test: Long duration to find memory leaks.

3. Reliability (SRE)

SLI / SLO / SLA

• SLI: The metric (Indicator). "P95 Latency".
• SLO: The goal (Objective). "99.9% of requests < 200ms".
• Error Budget: The allowed failure rate (1 - SLO). Burn this to ship faster.

Resources

• OpenTelemetry Best Practices
• Google SRE Book
• RED Method