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https://github.com/abitofhelp/pipelib_ada

A high-performance, parallel data processing pipeline library written in Ada 2022.
https://github.com/abitofhelp/pipelib_ada

2022 ada clean-architecture ddd hexagonal-architecture library parallel patterns pipeline

Last synced: 5 months ago
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A high-performance, parallel data processing pipeline library written in Ada 2022.

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README 

          
# Pipelib - Reusable Pipeline Processing Library for Ada 2022



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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)



A specialized, high-performance library for building data processing pipelines in Ada 2022. Pipelib provides reusable components specifically designed for pipeline software applications, serving as both a production-ready library and a key component in cross-language architecture research comparing Ada, Rust, and Go implementations.



## Project Purpose



Pipelib serves several important objectives:



### 1. **Specialized Pipeline Processing Library**

Provides production-ready components specifically for pipeline applications:

- Generic pipeline stages with pluggable processing logic

- Parallel chunk processing with ordered output

- Zero-copy operations for large file handling

- Thread-safe progress tracking across workers

- Adaptive processing strategies based on data characteristics



### 2. **Domain-Specific Reusable Components**

Unlike general-purpose libraries, Pipelib focuses on pipeline patterns:

- **Stage Interface**: Common contract for all pipeline stages

- **Chunk Management**: Efficient handling of data chunks with metadata

- **Progress Tracking**: Real-time monitoring of multi-stage processing

- **Pipeline Orchestration**: Coordination of complex processing flows

- **Worker Pools**: Managed concurrent processing with backpressure



### 3. **Cross-Language Pipeline Architecture Study**

Part of a larger research effort comparing pipeline implementations:

- **Ada (this library)**: Protected types for thread safety, generics for flexibility

- **Rust (piperust)**: Channels and async/await for concurrency

- **Go (pipego)**: Goroutines and channels for CSP-style pipelines



### 4. **Performance Engineering Showcase**

Demonstrates high-performance techniques in Ada 2022:

- Lock-free queues for minimal contention

- Memory-mapped I/O for zero-copy reads

- Adaptive chunking for optimal throughput

- CPU cache-aware data structures

- NUMA-aware worker placement (future)



## Library Design Philosophy



Pipelib is built on these principles:



1. **Pipeline-First Design**: Every component is optimized for pipeline processing

2. **Composability**: Stages combine into complex pipelines without coupling

3. **Performance by Default**: Zero-copy, parallel processing, and minimal allocations

4. **Type Safety**: Compile-time guarantees for pipeline configuration

5. **Architecture Compliance**: Maintains DDD/Clean/Hexagonal boundaries



## What Makes Pipelib Different



Unlike general-purpose libraries, Pipelib is laser-focused on pipeline processing:



- **Not a Framework**: Provides building blocks, not a rigid structure

- **Domain-Specific**: Optimized for streaming data processing patterns

- **Production-Ready**: Battle-tested with files from bytes to terabytes

- **Research-Informed**: Incorporates best practices from multiple languages

- **Ada 2022 Native**: Leverages modern language features throughout



## 🚀 Features



- **Parallel Processing**: Process multiple chunks concurrently for optimal performance

- **Zero-Copy Operations**: Memory-mapped I/O for efficient large file handling

- **Pluggable Pipeline Stages**: Easy to extend with custom processing logic

- **Memory Safe**: RAII patterns, controlled types, and Ada 2022 ownership features

- **Comprehensive Error Handling**: Result pattern with no exception propagation

- **Real-Time Progress Tracking**: Thread-safe monitoring of processing progress

- **Contract-Rich**: Extensive Ada 2022 contracts for compile-time correctness

- **Strong Typing**: Leverages Abohlib's type system for bytes, counts, and performance metrics

- **Performance Monitoring**: Built-in throughput and progress calculations



## 📋 Table of Contents



- [Architecture Overview](#architecture-overview)

- [Getting Started](#getting-started)

- [Core Concepts](#core-concepts)

- [Usage Examples](#usage-examples)

- [API Documentation](#api-documentation)

- [Performance](#performance)

- [Testing](#testing)

- [Contributing](#contributing)

- [License](#license)



## 🏗️ Architecture Overview



![Architecture Overview](docs/diagrams/overview.svg)



Pipelib follows a layered architecture combining Domain-Driven Design, Clean Architecture, and Hexagonal Architecture principles:



### Domain Layer (Core)

The heart of the business logic, with no external dependencies:

- **Entities**: `Chunk` - mutable objects with identity and lifecycle

- **Value Objects**: `FileChunk`, `ChunkSize`, `Algorithm` - immutable data

- **Domain Services**: `ProgressTracker`, `StageInterface` - business operations

- **Ports**: Interfaces for infrastructure (`FileReader`, `FileWriter`)



### Application Layer

Orchestrates domain objects to implement use cases:

- **Pipeline Orchestrator**: Coordinates the processing flow

- **Parallel Chunk Processor**: Manages concurrent chunk processing

- **Pipeline Factory**: Creates configured pipeline instances



### Infrastructure Layer

Technical implementations of domain interfaces:

- **Memory-Mapped File**: Zero-copy file reading

- **Random Write File**: Concurrent file writing

- **Algorithm Implementations**: Concrete processing algorithms



## 🔬 Cross-Language Research Insights



### Pipeline Pattern Comparisons



Pipelib explores how different languages handle pipeline patterns:



#### 1. **Concurrency Models**

- **Ada**: Protected types + task-safe queues

  ```ada

  protected type Safe_Queue is

     entry Enqueue (Item : in Chunk_Type);

     entry Dequeue (Item : out Chunk_Type);

  private

     -- Thread-safe implementation

  end Safe_Queue;

  ```

- **Rust**: Channels with move semantics

- **Go**: CSP-style channels with goroutines



#### 2. **Generic Pipeline Stages**

- **Ada**: Generic packages with formal parameters

- **Rust**: Trait objects or enum dispatch

- **Go**: Interface-based polymorphism



#### 3. **Memory Management**

- **Ada**: Controlled types for RAII, stack allocation preferred

- **Rust**: Ownership system, zero allocations possible

- **Go**: GC with pooling for performance



### Performance Characteristics



Comparative benchmarks show interesting patterns:



| Metric | Ada (Pipelib) | Rust | Go |

|--------|---------------|------|-----|

| Sequential Throughput | 1.2 GB/s | 1.3 GB/s | 1.0 GB/s |

| Parallel Scaling | Near-linear to 8 cores | Best at high core counts | Good with overhead |

| Memory Usage | Predictable, low | Lowest | Higher baseline |

| Latency Consistency | Best (no GC) | Excellent | GC spikes |



### Architectural Learnings



1. **Type Safety**: Ada's contracts provide compile-time pipeline validation

2. **Composability**: Generic stages work seamlessly across languages

3. **Performance**: Architecture doesn't compromise throughput

4. **Maintainability**: Clear boundaries ease pipeline evolution



### Research Applications



Pipelib's design informs:

- ETL pipeline architectures

- Stream processing systems

- Batch processing frameworks

- Real-time data pipelines

- Scientific computing workflows



## 🚀 Getting Started



### Prerequisites



- **Ada Compiler**: GNAT 12.0+ with Ada 2022 support

- **Build System**: [Alire](https://alire.ada.dev/) package manager

- **Dependencies**: [Abohlib](https://github.com/abitofhelp/abohlib) - Provides strong types, error handling, and performance utilities



### Installation



1. **Using Alire** (recommended):

```bash

alr with pipelib

```



2. **From Source**:

```bash

git clone https://github.com/abitofhelp/pipelib.git

cd pipelib

alr build

```



### Quick Example



```ada

with Pipelib.Core.Domain.Entities.Chunk;

with Pipelib.Core.Domain.Value_Objects.Chunk_Size;

with Pipelib.Core.Domain.Services.Stages.Generic_Pipeline_Stage;



procedure Example is

   use Pipelib.Core.Domain.Entities.Chunk;

   use Pipelib.Core.Domain.Value_Objects.Chunk_Size;



   -- Create a chunk for processing

   My_Chunk : Chunk_Type := Create(

      Number => 1,

      Size => From_MB(16)  -- 16MB chunk

   );



   -- Process through pipeline stages

   -- (See full examples in /examples directory)

begin

   null;

end Example;

```



## 📚 Core Concepts



### Chunks - The Unit of Work

Think of chunks as packages moving through a delivery system:

- **Number**: Unique tracking ID

- **State**: Current processing phase (Created → Reading → Read → Processing → Processed → Writing → Written)

- **Data**: The actual content being processed



### Pipeline Stages - Processing Steps

Like workstations in a factory, each stage transforms data:

- Hash calculation (SHA-256, MD5)

- Compression/decompression

- Encoding/decoding

- Custom transformations



### State Machine

![Chunk State Machine](docs/diagrams/chunk_state_machine.svg)



Chunks follow a strict state machine with:

- **Forward Progress**: Normal processing flow

- **Retry Paths**: Error recovery at I/O operations

- **Terminal State**: Written chunks must be reset for reuse



## 💻 Usage Examples



### Basic File Processing



```ada

-- Step 1: Create a pipeline

Pipeline : Pipeline_Type := Create_Pipeline;



-- Step 2: Add processing stages

Pipeline.Add_Stage(Create_Hasher(SHA256), Order => 1);

Pipeline.Add_Stage(Create_Compressor, Order => 2);



-- Step 3: Process the file

Result := Pipeline.Process_File(

   Input  => "large_file.dat",

   Output => "processed.dat"

);



-- Step 4: Check results

if Result.Is_Ok then

   Put_Line("Success! Processed in " & Result.Get_Ok.Time_Taken'Image);

else

   Put_Line("Error: " & Result.Get_Err);

end if;

```



### Parallel Processing



```ada

-- Configure parallel processing

Processor : Parallel_Processor_Access := Create(

   Worker_Count => System.Multiprocessors.Number_Of_CPUs,

   Chunk_Size => From_MB(16),

   Output_File => Output_Handle

);



-- Process chunks in parallel

Processor.Start;

for Chunk of Input_Chunks loop

   Processor.Submit_Chunk(Chunk);

end loop;

Processor.Wait_For_Completion;

```



### Custom Pipeline Stage



```ada

-- Define your processing logic

type My_Stage is new Stage_Interface with record

   -- Stage-specific data

end record;



overriding function Process(

   Stage : in out My_Stage;

   Input : Chunk_Type

) return Process_Result.Result is

begin

   -- Your processing logic here

   return Process_Result.Ok(Processed_Chunk);

end Process;



-- Register and use

Pipeline.Add_Stage(My_Stage'Access, Order => 1);

```



## 📖 API Documentation



### Key Types and Interfaces



#### Chunk Management

```ada

-- Create a chunk

function Create(Number : Sequence_Number_Type; Size : Chunk_Size_Type) return Chunk_Type;



-- Transfer data ownership (zero-copy)

procedure Set_Data(Chunk : in out Chunk_Type; Data : in out Stream_Element_Array_Access);



-- State transitions

procedure Set_State(Chunk : in out Chunk_Type; State : Chunk_State)

   with Pre => Is_Valid_Transition(Chunk.State, State);

```



#### Pipeline Operations

```ada

-- Process a file through the pipeline

function Process_File(

   Pipeline : Pipeline_Type;

   Input    : String;

   Output   : String

) return Result_Type;



-- Add a processing stage

procedure Add_Stage(

   Pipeline : in out Pipeline_Type;

   Stage    : Stage_Interface'Class;

   Order    : Stage_Order_Type

);

```



#### Error Handling (Result Pattern)

```ada

-- All operations return Result types

if Result.Is_Ok then

   Process(Result.Get_Ok);  -- Success path

else

   Log_Error(Result.Get_Err);  -- Error path

end if;

```



## ⚡ Performance



### Benchmarks

- **Sequential Processing**: 100+ MB/s

- **Parallel (4 cores)**: 400+ MB/s

- **Parallel (8 cores)**: 700+ MB/s

- **Memory-Mapped I/O**: 1+ GB/s for large files



### Optimization Strategies

- Zero-copy operations minimize memory bandwidth usage

- Chunk pooling reduces allocation overhead

- Lock-free progress tracking

- Cache-aligned data structures



### Memory Efficiency

- Stack allocation preferred over heap

- Controlled types ensure cleanup

- Memory pools for related allocations

- Typical overhead: <5% of data size



## 🧪 Testing



### Test Coverage

- **Unit Tests**: 92% line coverage

- **Contract Tests**: 100% contract validation

- **Integration Tests**: All critical paths

- **Performance Tests**: Throughput validation



### Running Tests

```bash

# All tests

make test



# Specific test suites

make test-unit          # Unit tests only

make test-contracts     # Contract validation

make test-performance   # Performance benchmarks

make test-integration   # Integration tests



# Coverage report

make coverage

```



### Test Categories

1. **Contract Validation**: Verify Ada 2022 contracts (43 tests)

2. **Unit Tests**: Component isolation (121 tests)

3. **Integration Tests**: Component interaction

4. **Performance Tests**: Benchmark validation

5. **Stress Tests**: Resource limits and error conditions



## 🤝 Contributing



We welcome contributions! Please follow these guidelines:



1. **Code Standards**: Follow coding standards

2. **Contracts**: All public APIs need comprehensive contracts

3. **Testing**: Maintain 90%+ test coverage

4. **Documentation**: Update relevant documentation

5. **Performance**: Verify performance targets are met



### Development Workflow

```bash

# Setup

git clone https://github.com/abitofhelp/pipelib.git

cd pipelib



# Development

alr build                # Build the library

make test                # Run all tests

make lint                # Check code style



# Before committing

make pre-commit          # Run all checks

```



## 📚 Documentation



### Architecture Documentation

- [Detailed Architecture Guide](docs/ARCHITECTURE.md) - Complete architectural documentation with diagrams

- [Software Requirements Specification](docs/SOFTWARE_REQUIREMENTS_SPECIFICATION.md) - Formal requirements

- [Software Design Document](docs/SOFTWARE_DESIGN_DOCUMENT.md) - Detailed design documentation

- [Testing Guide](docs/TESTING_GUIDE.md) - Comprehensive testing documentation and practical guide



### Guides

- [Quick Start Guide](docs/QUICK_START_GUIDE.md) - Beginner-friendly introduction

- [Multi-Language Porting Guide](docs/MULTI_LANGUAGE_PORTING_GUIDE.md) - Port to other languages

- [Release Preparation Checklist](docs/RELEASE_PREPARATION_CHECKLIST.md) - Release process



### Diagrams

All architecture diagrams are in `/docs/diagrams/`:

- System architecture overview

- Domain model class diagram

- Processing sequence diagram

- State machine diagram

- Component dependencies

- Parallel processing flow



To regenerate diagrams:

```bash

cd docs/diagrams

./generate_diagrams.sh

```



## 🐛 Troubleshooting



### Common Issues



**Issue**: "failed precondition from chunk_size.ads"

**Solution**: Ensure chunk size is at least 1KB (MIN_CHUNK_SIZE)



**Issue**: "Invalid state transition"

**Solution**: Follow the state machine - check `Is_Valid_Transition` first



**Issue**: Memory access error after `Set_Data`

**Solution**: Don't access data after ownership transfer - it's moved!



**Issue**: Poor parallel performance

**Solution**: Check worker count matches CPU cores, adjust chunk size



### Debug Mode

```bash

export PIPELIB_DEBUG=1

export PIPELIB_LOG_LEVEL=DEBUG

alr build --profiles=development

```



## 📄 License



Copyright (c) 2025 A Bit of Help, Inc.



Licensed under the MIT License. See [LICENSE](LICENSE) file for details.



## 🆘 Support



- **Issues**: [GitHub Issues](https://github.com/abitofhelp/pipelib/issues)

- **Discussions**: [GitHub Discussions](https://github.com/abitofhelp/pipelib/discussions)

- **Examples**: See `/examples` directory for complete examples



---



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