Skip to content

Performance

Overview

This guide describes the performance principles, benchmarking methodology, and optimization techniques used throughout the CoreSystem ecosystem. It also provides recommendations for building high-performance applications while maintaining reliability and observability.

Performance Principles

CoreSystem libraries are designed around the following principles:

  • Minimize memory allocations.
  • Avoid unnecessary object creation.
  • Prefer asynchronous APIs for I/O operations.
  • Reduce lock contention.
  • Optimize hot execution paths.
  • Keep APIs lightweight and predictable.
  • Design for scalability under high concurrency.

Benchmark Methodology

Benchmark results should be generated using BenchmarkDotNet under reproducible conditions.

Each benchmark should document:

  • Hardware specifications.
  • Operating system.
  • .NET runtime version.
  • BenchmarkDotNet version.
  • Test configuration.
  • Number of iterations.
  • Warmup configuration.

Benchmarks

This section contains benchmark results for CoreSystem packages.

Examples include:

  • Cache performance
  • Memory provider
  • Redis provider
  • Serialization performance
  • HTTP pipeline
  • Retry policies
  • Circuit Breaker overhead

Optimization Guidelines

Memory

  • Reuse objects whenever possible.
  • Avoid unnecessary allocations.
  • Use spans and memory-efficient APIs where appropriate.

Async

  • Avoid blocking asynchronous operations.
  • Prefer async/await for I/O.
  • Do not mix synchronous and asynchronous APIs.

Caching

  • Cache frequently accessed data.
  • Configure sensible expiration policies.
  • Use distributed caching only when necessary.

Serialization

  • Minimize payload size.
  • Avoid unnecessary serialization.
  • Reuse serializer instances whenever possible.

Resilience

  • Configure retries carefully.
  • Avoid excessive retry attempts.
  • Combine retries with timeouts and circuit breakers.

Performance Trade-offs

Every optimization has trade-offs.

Examples include:

  • Memory usage vs. CPU utilization
  • Throughput vs. latency
  • Retry count vs. response time
  • Cache freshness vs. cache hit ratio

Understanding these trade-offs helps select the appropriate configuration for each application.

Best Practices

  • Measure before optimizing.
  • Benchmark realistic workloads.
  • Monitor applications in production.
  • Use OpenTelemetry metrics to identify bottlenecks.
  • Continuously review performance after new releases.

Future Improvements

Potential future improvements include:

  • Additional BenchmarkDotNet suites.
  • Performance regression testing.
  • Automated benchmark reporting.
  • Cross-platform benchmark comparisons.
  • Native AOT performance analysis. ```