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Advanced Topics

This section covers advanced techniques and best practices for building high-performance, production-grade data pipelines with NPipeline.

Performance Optimization​

Performance Hygiene​

Learn best practices for building efficient, low-allocation data pipelines. Covers:

  • Memory allocation minimization strategies
  • Buffer reuse patterns
  • Data structure optimization
  • GC pressure reduction techniques

Best for: Developers aiming to optimize pipeline performance and reduce garbage collection overhead.

Synchronous Fast Paths and ValueTask Optimization​

Master the ValueTask pattern for eliminating allocations in high-throughput scenarios. Covers:

  • Understanding ValueTask vs Task tradeoffs
  • Implementing fast path/slow path patterns
  • Real-world optimization examples
  • Achieving up to 90% GC reduction in cache-hit scenarios

Best for: High-throughput pipelines where cache hits or synchronous operations are common.

Resilience & Retry Strategies​

For advanced retry delay patterns and production resilience strategies:

Production Deployment​

For guidance on deploying NPipeline to production environments:

Testing​

For advanced testing strategies:

Learning Path​

If you're new to these topics, follow this recommended order:

  1. Performance Hygiene - Foundation for understanding performance considerations
  2. Synchronous Fast Paths - Deep dive into specific optimization techniques
  3. Advanced Retry Strategies - Production resilience patterns
  4. Advanced Testing - Validating your optimized implementations

Prerequisites​

Before diving into these advanced topics, ensure you're familiar with:

Key Principles​

Measure Before Optimizing​

Always profile your pipeline before optimizing. Use the built-in diagnostics and monitoring capabilities to identify actual bottlenecks rather than guessing.

Optimize for Your Workload​

Different workloads have different characteristics. A pipeline processing cached data has very different optimization needs than one processing streaming data from external sources.

Balance Complexity and Benefit​

Advanced optimizations often add complexity. Ensure the performance gain justifies the added code complexity and maintenance burden.

Test Your Optimizations​

Always validate that optimizations actually improve performance in your specific environment. Microbenchmarks don't always translate to real-world improvements.