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Why NPipeline?

Why NPipeline?​

NPipeline is engineered to address the common challenges in building robust and efficient data processing systems. It offers a unique blend of flexibility, performance, and developer experience that sets it apart.

Use Cases​

NPipeline excels in scenarios requiring structured, high-throughput data processing. Common use cases include:

  • ETL (Extract, Transform, Load) Workflows: Building reliable data ingestion and transformation pipelines.
  • Real-time Data Processing: Handling streaming data from various sources with low latency.
  • Data Validation and Cleansing: Implementing complex validation rules and data quality checks.
  • Event-Driven Architectures: Processing events as they occur in a scalable manner.
  • Batch Processing: Efficiently processing large volumes of historical data.
  • Microservice Integration: Facilitating data exchange and transformation between microservices.

Design Philosophy​

NPipeline's design is rooted in several core principles:

  1. Simplicity and Clarity: A clear, intuitive API that makes pipeline construction straightforward and understandable.
  2. Performance First: Optimized for minimal memory allocations and high concurrency, crucial for demanding workloads.
  3. Testability: Designed from the ground up to be easily testable, promoting robust and reliable solutions.
  4. Extensibility: A modular architecture that allows developers to easily extend functionality and integrate with external systems.
  5. Modern .NET: Embracing the latest C# features and asynchronous programming patterns for efficient and idiomatic code.

Performance Characteristics​

Performance is a cornerstone of NPipeline. It is built to:

  • Minimize Allocations: Judicious use of memory to reduce garbage collection overhead, leading to smoother operation.
  • Maximize Throughput: Efficient internal mechanisms and asynchronous processing ensure high data processing rates.
  • Low Latency: Designed to process data items quickly, making it suitable for near real-time applications.
  • Scalability: The graph-based approach and efficient resource management allow pipelines to scale with increasing data volumes.
  • Zero-Allocation Fast Paths: Transform operations with synchronous execution paths (cache hits, simple calculations) eliminate heap allocations entirely. In high-cache-hit scenarios, this can drastically reduce garbage collection pressure, cutting out thousands of allocations per second in high-throughput pipelines.

By focusing on these aspects, NPipeline provides a powerful foundation for building data pipelines that are not only functional but also performant and maintainable.

Zero-Allocation Fast Paths: A Competitive Advantage​

One of NPipeline's most powerful features for high-performance scenarios is its support for zero-allocation fast paths in transform nodes.

The Problem: In typical async-first frameworks, even simple synchronous operations (like cache lookups or quick calculations) create heap-allocated Task<T> objects. Processing millions of items per second, where many transforms are synchronous or have high cache hit rates, this creates millions of tiny allocations per second—constant pressure on the garbage collector.

The Solution: Using ValueTask<T>, you can implement a two-path pattern:

  • Fast Path (Synchronous): Result available immediately → allocates on the stack, zero GC pressure
  • Slow Path (Asynchronous): Work required → transitions seamlessly to true async

The Impact:

  • For a pipeline processing 100,000 items/second with 90% cache hits, you eliminate 90,000 Task allocations per second
  • Measured reduction in garbage collection pressure: up to 90%
  • Particularly effective for: data validation, filtering, cached enrichment (everyday pipeline tasks)

Learn how to implement this: Synchronous Fast Paths and ValueTask Optimization

Next Steps​