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Performance Engineering

Master plan for system-level performance tuning and profiling. Covers application profiling, resource optimization, caching strategies, load testing, and distributed system performance patterns for data-intensive workloads.

Key Topics​

  • Application Profiling: CPU profiling, memory profiling, flame graphs, and bottleneck identification
  • Caching Strategies: L1/L2/L3 caching, cache invalidation patterns, and CDN optimization
  • Resource Optimization: CPU, memory, disk I/O, and network tuning for data pipelines
  • Concurrency Patterns: Thread pools, async I/O, parallel processing, and backpressure handling
  • Load Testing: Stress testing, spike testing, soak testing, and capacity planning
  • Distributed Tracing: OpenTelemetry, Jaeger, Zipkin for request flow analysis
  • Database Connection Pooling: Pool sizing, connection lifecycle, and connection leaks
  • Batch vs. Stream Processing: Processing pattern selection based on latency and throughput requirements
  • Memory Management: Garbage collection tuning, memory leak detection, and heap analysis
  • Network Optimization: TCP tuning, compression, protocol selection (HTTP/2, gRPC)

Primary Tools & Technologies​

Profiling Tools:

  • Python: py-spy, memory_profiler, cProfile, line_profiler
  • Java: JProfiler, YourKit, VisualVM, Async Profiler
  • Go: pprof, trace
  • Node.js: clinic.js, 0x

Caching Solutions:

  • Redis (in-memory cache, distributed caching)
  • Memcached (simple key-value caching)
  • Varnish (HTTP caching, CDN)
  • Application-level caching (Caffeine, Guava, lru-cache)

Load Testing:

  • Apache JMeter (general-purpose load testing)
  • Locust (Python-based, distributed load testing)
  • k6 (Go-based, developer-friendly)
  • Gatling (Scala-based, real-time metrics)

Observability & Tracing:

  • OpenTelemetry (vendor-neutral instrumentation)
  • Jaeger, Zipkin (distributed tracing)
  • Datadog APM, New Relic, Dynatrace (commercial APM)
  • Prometheus + Grafana (metrics and visualization)

Resource Monitoring:

  • htop, iotop, nethogs (system-level monitoring)
  • cAdvisor (container metrics)
  • Kubernetes metrics-server, VPA (Vertical Pod Autoscaler)

Integration Points​

Upstream Dependencies:

  • Data Architecture: Schema design impacting query and application performance
  • Streaming Data: Throughput optimization for high-volume event processing
  • Data Transformation: Pipeline resource tuning and parallelization

Downstream Consumers:

  • SQL Optimization: Database-level tuning complementing application optimization
  • API Performance: Backend service optimization for API response times
  • Data Visualization: Optimizing dashboard load times and query performance

Cross-Functional:

  • Infrastructure: Cloud resource sizing, auto-scaling policies, and cost optimization
  • Monitoring & Alerting: Performance SLI/SLO tracking and anomaly detection
  • Capacity Planning: Growth forecasting and resource provisioning

Status​

Master Plan Available - Comprehensive guidance for performance engineering, covering profiling, caching, load testing, and distributed system optimization for data-intensive applications.

Part of the Data Engineering skill collection focused on maximizing system performance and resource efficiency.