Introduction

Version: 2.0.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Welcome

Welcome to the Adaptive Pipeline documentation. This project is a high-performance, educational file processing pipeline built in Rust, demonstrating advanced software architecture patterns and modern Rust idioms.

What is This Project?

The Adaptive Pipeline is:

• A File Processing System - Transform files through configurable stages (compression, encryption, integrity checking)
• An Educational Resource - Learn advanced Rust patterns, DDD, Clean Architecture, and Hexagonal Architecture
• Open Source - Licensed under BSD-3-Clause, contributions welcome

Who is This For?

• Rust Developers learning advanced patterns
• Contributors wanting to extend the pipeline
• Students exploring software architecture
• Engineers seeking secure file processing solutions

Documentation Structure

This documentation is organized for progressive learning:

1. Getting Started - Quick start, installation, first pipeline
2. Architecture - High-level system design
3. Reference - Glossary and API links

For detailed technical documentation, see the Pipeline Developer Guide.

Quick Links

• Quick Start - Get running in 5 minutes
• Pipeline Developer Guide - Detailed technical documentation
• API Documentation - Generated API docs
• GitHub Repository

Getting Help

• Issues: Report bugs on GitHub Issues
• Discussions: Ask questions in GitHub Discussions
• Documentation: Browse this guide and the Pipeline Developer Guide

Let's get started!

Quick Start

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Get up and running with the Adaptive Pipeline in 5 minutes.

Prerequisites

• Rust 1.70 or later
• SQLite 3.35 or later

Quick Installation

git clone https://github.com/abitofhelp/optimized_adaptive_pipeline_rs.git
cd optimized_adaptive_pipeline_rs
make build

Your First Pipeline

TODO: Add quick start example

Installation

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Detailed installation instructions for all platforms.

System Requirements

TODO: Add system requirements

Building from Source

TODO: Add build instructions

Configuration

TODO: Add configuration instructions

Your First Pipeline

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Step-by-step tutorial for creating and running your first pipeline.

Creating a Pipeline

TODO: Add pipeline creation tutorial

Running the Pipeline

TODO: Add execution instructions

Understanding the Output

TODO: Add output explanation

Architecture Overview

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

High-level overview of the pipeline architecture.

Architectural Patterns

TODO: Extract from pipeline/src/lib.rs

Layers

TODO: Describe Domain, Application, Infrastructure layers

Design Goals

TODO: Add design goals

Design Principles

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Core design principles guiding the pipeline architecture.

Domain-Driven Design

TODO: Add DDD principles

Clean Architecture

TODO: Add clean architecture principles

SOLID Principles

TODO: Add SOLID application

Project Structure

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Organization of the codebase and key directories.

Directory Layout

TODO: Add directory structure

Workspace Organization

TODO: Add workspace explanation

Module Organization

TODO: Add module structure

Software Requirements Specification (SRS)

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

1. Introduction

1.1 Purpose

This Software Requirements Specification (SRS) defines the functional and non-functional requirements for the Adaptive Pipeline system, a high-performance file processing pipeline implemented in Rust using Domain-Driven Design, Clean Architecture, and Hexagonal Architecture patterns.

Intended Audience:

• Software developers implementing pipeline features
• Quality assurance engineers designing test plans
• System architects evaluating design decisions
• Project stakeholders reviewing system capabilities

1.2 Scope

System Name: Adaptive Pipeline

System Purpose: Provide a configurable, extensible pipeline for processing files through multiple stages including compression, encryption, integrity verification, and custom transformations.

Key Capabilities:

• Multi-stage file processing with configurable pipelines
• Built-in stage types: Compression (Brotli, Gzip, Zstd, LZ4), Encryption (AES-256-GCM, ChaCha20-Poly1305), Integrity verification (SHA-256, SHA-512, BLAKE3)
• Custom stage extensibility: Create domain-specific stages (sanitization, transformation, validation, enrichment, watermarking) through trait-based extension system
• Binary format (.adapipe) for processed files with embedded metadata
• Asynchronous, concurrent processing with resource management
• Plugin-ready architecture for loading external stage implementations
• Comprehensive metrics and observability with Prometheus integration

Out of Scope:

• Distributed processing across multiple machines
• Real-time streaming protocols
• Network-based file transfer
• GUI/Web interface
• Cloud service integration

1.3 Definitions, Acronyms, and Abbreviations

Domain Terms:

• Pipeline: Ordered sequence of processing stages
• Stage: Individual processing operation (compression, encryption, etc.)
• Chunk: Fixed-size portion of file data for streaming processing
• Context: Shared state and metrics during pipeline execution
• Aggregate: Domain entity representing complete pipeline configuration

1.4 References

• Domain-Driven Design: Eric Evans, 2003
• Clean Architecture: Robert C. Martin, 2017
• Rust Programming Language: https://www.rust-lang.org/
• Tokio Asynchronous Runtime: https://tokio.rs/
• Criterion Benchmarking: https://github.com/bheisler/criterion.rs

1.5 Overview

This SRS is organized as follows:

• Section 2: Overall system description and context
• Section 3: Functional requirements organized by feature
• Section 4: Non-functional requirements (performance, security, etc.)
• Section 5: System interfaces and integration points
• Section 6: Requirements traceability matrix

2. Overall Description

2.1 Product Perspective

The Adaptive Pipeline is a standalone library and CLI application for file processing. It operates as:

Architectural Context:

┌─────────────────────────────────────────────────┐
│         CLI Application Layer                   │
│  (Command parsing, progress display, output)    │
├─────────────────────────────────────────────────┤
│         Application Layer                        │
│  (Use cases, orchestration, workflow control)   │
├─────────────────────────────────────────────────┤
│         Domain Layer                             │
│  (Business logic, entities, domain services)    │
├─────────────────────────────────────────────────┤
│         Infrastructure Layer                     │
│  (I/O, persistence, metrics, adapters)          │
└─────────────────────────────────────────────────┘
         ▼                    ▼                ▼
    File System         SQLite DB       System Resources

System Interfaces:

• Input: File system files (any binary format)
• Output: Processed files (.adapipe format) or restored original files
• Configuration: TOML configuration files or command-line arguments
• Persistence: SQLite database for pipeline metadata
• Monitoring: Prometheus metrics endpoint (HTTP)

2.2 Product Functions

Primary Functions:

1. Pipeline Configuration

   • Define multi-stage processing workflows
   • Configure compression, encryption, and custom stages
   • Persist and retrieve pipeline configurations

2. File Processing

   • Read files in configurable chunks
   • Apply compression algorithms with configurable levels
   • Encrypt data with authenticated encryption
   • Calculate and verify integrity checksums
   • Write processed data in .adapipe binary format

3. File Restoration

   • Read .adapipe formatted files
   • Extract metadata and processing steps
   • Reverse processing stages (decrypt, decompress)
   • Restore original files with integrity verification

4. Resource Management

   • Control CPU utilization with token-based concurrency
   • Limit memory usage with configurable thresholds
   • Manage I/O operations with adaptive throttling
   • Track and report resource consumption

5. Observability

   • Collect processing metrics (throughput, latency, errors)
   • Export metrics in Prometheus format
   • Provide structured logging with tracing
   • Generate performance reports

2.3 User Classes and Characteristics

2.4 Operating Environment

Supported Platforms:

• Linux (x86_64, aarch64)
• macOS (x86_64, Apple Silicon)
• Windows (x86_64) - Best effort support

Runtime Requirements:

• Rust 1.75 or later
• Tokio asynchronous runtime
• SQLite 3.35 or later (for persistence)
• Minimum 512 MB RAM
• Disk space proportional to processing needs

Build Requirements:

• Rust toolchain (rustc, cargo)
• C compiler (for SQLite, compression libraries)
• pkg-config (Linux/macOS)

2.5 Design and Implementation Constraints

Architectural Constraints:

• Must follow Domain-Driven Design principles
• Must maintain layer separation (domain, application, infrastructure)
• Domain layer must have no external dependencies
• Must use Dependency Inversion Principle throughout

Technical Constraints:

• Implemented in Rust (no other programming languages)
• Asynchronous operations must use Tokio runtime
• CPU-bound operations must use Rayon thread pool
• Database operations must use SQLx with compile-time query verification
• All public APIs must be documented with rustdoc

Security Constraints:

• Encryption keys must be zeroized on drop
• No sensitive data in logs or error messages
• All encryption must use authenticated encryption (AEAD)
• File permissions must be preserved and validated

2.6 Assumptions and Dependencies

Assumptions:

• Files being processed fit available disk space when chunked
• File system supports atomic file operations
• System clock is synchronized (for timestamps)
• SQLite database file has appropriate permissions

Dependencies:

• tokio: Asynchronous runtime (MIT/Apache-2.0)
• serde: Serialization framework (MIT/Apache-2.0)
• sqlx: SQL toolkit with compile-time checking (MIT/Apache-2.0)
• prometheus: Metrics collection (Apache-2.0)
• tracing: Structured logging (MIT)
• rayon: Data parallelism library (MIT/Apache-2.0)

3. Functional Requirements

3.1 Pipeline Configuration (FR-CONFIG)

FR-CONFIG-001: Create Pipeline

Priority: High Description: System shall allow users to create a new pipeline configuration with a unique name and ordered sequence of stages.

Inputs:

• Pipeline name (string, 1-100 characters)
• List of pipeline stages with configuration

Processing:

• Validate pipeline name uniqueness
• Validate stage ordering and compatibility
• Automatically add input/output checksum stages
• Assign unique pipeline ID

Outputs:

• Created Pipeline entity with ID
• Success/failure status

Error Conditions:

• Duplicate pipeline name
• Invalid stage configuration
• Empty stage list

FR-CONFIG-002: Configure Pipeline Stage

Priority: High Description: System shall allow configuration of individual pipeline stages with type-specific parameters.

Inputs:

• Stage type (compression, encryption, transform, checksum)
• Stage name (string)
• Algorithm/method identifier
• Configuration parameters (key-value map)
• Parallel processing flag

Processing:

• Validate stage type and algorithm compatibility
• Validate configuration parameters for algorithm
• Set default values for optional parameters

Outputs:

• Configured PipelineStage entity
• Validation results

Error Conditions:

• Unsupported algorithm for stage type
• Invalid configuration parameters
• Missing required parameters

FR-CONFIG-003: Persist Pipeline Configuration

Priority: Medium Description: System shall persist pipeline configurations to SQLite database for retrieval and reuse.

Inputs:

• Pipeline entity with stages

Processing:

• Serialize pipeline configuration to database schema
• Store pipeline metadata (name, description, timestamps)
• Store stages with ordering and configuration
• Commit transaction atomically

Outputs:

• Persisted pipeline ID
• Timestamp of persistence

Error Conditions:

• Database connection failure
• Disk space exhaustion
• Transaction rollback

FR-CONFIG-004: Retrieve Pipeline Configuration

Priority: Medium Description: System shall retrieve persisted pipeline configurations by ID or name.

Inputs:

• Pipeline ID or name

Processing:

• Query database for pipeline record
• Retrieve associated stages in order
• Reconstruct Pipeline entity from database data

Outputs:

• Pipeline entity with all stages
• Metadata (creation time, last modified)

Error Conditions:

• Pipeline not found
• Database corruption
• Deserialization failure

3.2 Compression Processing (FR-COMPRESS)

FR-COMPRESS-001: Compress Data

Priority: High Description: System shall compress file chunks using configurable compression algorithms and levels.

Inputs:

• Input data chunk (FileChunk)
• Compression algorithm (Brotli, Gzip, Zstd, LZ4)
• Compression level (1-11, algorithm-dependent)
• Processing context

Processing:

• Select compression algorithm implementation
• Apply compression to chunk data
• Update processing metrics (bytes in/out, compression ratio)
• Preserve chunk metadata (sequence number, offset)

Outputs:

• Compressed FileChunk
• Updated processing context with metrics

Error Conditions:

• Compression algorithm failure
• Memory allocation failure
• Invalid compression level

Performance Requirements:

• LZ4: ≥500 MB/s throughput
• Zstd: ≥200 MB/s throughput
• Brotli: ≥100 MB/s throughput

FR-COMPRESS-002: Decompress Data

Priority: High Description: System shall decompress previously compressed file chunks for restoration.

Inputs:

• Compressed data chunk (FileChunk)
• Compression algorithm identifier
• Processing context

Processing:

• Select decompression algorithm implementation
• Apply decompression to chunk data
• Verify decompressed size matches expectations
• Update processing metrics

Outputs:

• Decompressed FileChunk with original data
• Updated processing context

Error Conditions:

• Decompression algorithm mismatch
• Corrupted compressed data
• Decompression algorithm failure

FR-COMPRESS-003: Benchmark Compression

Priority: Low Description: System shall provide benchmarking capability for compression algorithms to select optimal algorithm.

Inputs:

• Sample data
• List of algorithms to benchmark
• Benchmark duration or iteration count

Processing:

• Run compression/decompression for each algorithm
• Measure throughput, compression ratio, memory usage
• Calculate statistics (mean, std dev, percentiles)

Outputs:

• Benchmark results per algorithm
• Recommendation based on criteria

Error Conditions:

• Insufficient sample data
• Benchmark timeout

3.3 Encryption Processing (FR-ENCRYPT)

FR-ENCRYPT-001: Encrypt Data

Priority: High Description: System shall encrypt file chunks using authenticated encryption algorithms with secure key management.

Inputs:

• Input data chunk (FileChunk)
• Encryption algorithm (AES-256-GCM, ChaCha20-Poly1305, XChaCha20-Poly1305)
• Encryption key or key derivation parameters
• Security context

Processing:

• Derive encryption key if password-based (Argon2, Scrypt, PBKDF2)
• Generate random nonce for AEAD
• Encrypt chunk data with authentication tag
• Prepend nonce to ciphertext
• Update processing metrics

Outputs:

• Encrypted FileChunk (nonce + ciphertext + auth tag)
• Updated processing context

Error Conditions:

• Key derivation failure
• Encryption algorithm failure
• Insufficient entropy for nonce

Security Requirements:

• Keys must be zeroized after use
• Nonces must never repeat for same key
• Authentication tags must be verified on decryption

FR-ENCRYPT-002: Decrypt Data

Priority: High Description: System shall decrypt and authenticate previously encrypted file chunks.

Inputs:

• Encrypted data chunk (FileChunk with nonce + ciphertext)
• Encryption algorithm identifier
• Decryption key or derivation parameters
• Security context

Processing:

• Extract nonce from chunk data
• Derive decryption key if password-based
• Decrypt and verify authentication tag
• Update processing metrics

Outputs:

• Decrypted FileChunk with plaintext
• Authentication verification result

Error Conditions:

• Authentication failure (data tampered)
• Decryption algorithm mismatch
• Invalid decryption key
• Corrupted nonce or ciphertext

FR-ENCRYPT-003: Key Derivation

Priority: High Description: System shall derive encryption keys from passwords using memory-hard key derivation functions.

Inputs:

• Password or passphrase
• KDF algorithm (Argon2, Scrypt, PBKDF2)
• Salt (random or provided)
• KDF parameters (iterations, memory, parallelism)

Processing:

• Generate cryptographic random salt if not provided
• Apply KDF with specified parameters
• Produce key material of required length
• Zeroize password from memory

Outputs:

• Derived encryption key
• Salt used (for storage/retrieval)

Error Conditions:

• Insufficient memory for KDF
• Invalid KDF parameters
• Weak password (if validation enabled)

3.4 Integrity Verification (FR-INTEGRITY)

FR-INTEGRITY-001: Calculate Checksum

Priority: High Description: System shall calculate cryptographic checksums for file chunks and complete files.

Inputs:

• Input data (FileChunk or complete file)
• Checksum algorithm (SHA-256, SHA-512, BLAKE3, MD5)
• Processing context

Processing:

• Initialize checksum algorithm state
• Process data through hash function
• Finalize and produce checksum digest
• Update processing metrics

Outputs:

• Checksum digest (hex string or bytes)
• Updated processing context

Error Conditions:

• Unsupported checksum algorithm
• Hash calculation failure

Performance Requirements:

• SHA-256: ≥400 MB/s throughput
• BLAKE3: ≥3 GB/s throughput (with SIMD)

FR-INTEGRITY-002: Verify Checksum

Priority: High Description: System shall verify data integrity by comparing calculated checksums against expected values.

Inputs:

• Data to verify
• Expected checksum
• Checksum algorithm

Processing:

• Calculate checksum of provided data
• Compare calculated vs. expected (constant-time)
• Record verification result

Outputs:

• Verification success/failure
• Calculated checksum (for diagnostics)

Error Conditions:

• Checksum mismatch (integrity failure)
• Algorithm mismatch
• Malformed expected checksum

FR-INTEGRITY-003: Automatic Checksum Stages

Priority: High Description: System shall automatically add input and output checksum stages to all pipelines.

Inputs:

• User-defined pipeline stages

Processing:

• Insert input checksum stage at position 0
• Append output checksum stage at final position
• Reorder user stages to positions 1..n

Outputs:

• Pipeline with automatic checksum stages
• Updated stage ordering

Error Conditions:

• None (always succeeds)

3.5 Binary Format (FR-FORMAT)

FR-FORMAT-001: Write .adapipe File

Priority: High Description: System shall write processed data to .adapipe binary format with embedded metadata.

Inputs:

• Processed file chunks
• File header metadata (original name, size, checksum, processing steps)
• Output file path

Processing:

• Write chunks to file sequentially or in parallel
• Serialize metadata header to JSON
• Calculate header length and format version
• Write footer with magic bytes, version, header length
• Structure: [CHUNKS][JSON_HEADER][HEADER_LENGTH][VERSION][MAGIC]

Outputs:

• .adapipe format file
• Total bytes written

Error Conditions:

• Disk space exhaustion
• Permission denied
• I/O error during write

Format Requirements:

• Magic bytes: "ADAPIPE\0" (8 bytes)
• Format version: 2 bytes (little-endian)
• Header length: 4 bytes (little-endian)
• JSON header: UTF-8 encoded

FR-FORMAT-002: Read .adapipe File

Priority: High Description: System shall read .adapipe format files and extract metadata and processed data.

Inputs:

• .adapipe file path

Processing:

• Read and validate magic bytes from file end
• Read format version and header length
• Read and parse JSON header
• Verify header structure and required fields
• Stream chunk data from file

Outputs:

• File header metadata
• Chunk data reader for streaming

Error Conditions:

• Invalid magic bytes (not .adapipe format)
• Unsupported format version
• Corrupted header
• Malformed JSON

FR-FORMAT-003: Validate .adapipe File

Priority: Medium Description: System shall validate .adapipe file structure and integrity without full restoration.

Inputs:

• .adapipe file path

Processing:

• Verify magic bytes and format version
• Parse and validate header structure
• Verify checksum in metadata
• Check chunk count matches header

Outputs:

• Validation result (valid/invalid)
• Validation errors if invalid
• File metadata summary

Error Conditions:

• File format errors
• Checksum mismatch
• Missing required metadata fields

3.6 Resource Management (FR-RESOURCE)

FR-RESOURCE-001: CPU Token Management

Priority: High Description: System shall limit concurrent CPU-bound operations using token-based semaphore system.

Inputs:

• Maximum CPU tokens (default: number of CPU cores)
• Operation requiring CPU token

Processing:

• Acquire CPU token before CPU-bound operation
• Block if no tokens available
• Release token after operation completes
• Track token usage metrics

Outputs:

• Token acquisition success
• Operation execution

Error Conditions:

• Token acquisition timeout
• Semaphore errors

Performance Requirements:

• Token acquisition overhead: <1µs
• Fair token distribution (no starvation)

FR-RESOURCE-002: I/O Token Management

Priority: High Description: System shall limit concurrent I/O operations to prevent resource exhaustion.

Inputs:

• Maximum I/O tokens (configurable)
• I/O operation requiring token

Processing:

• Acquire I/O token before I/O operation
• Block if no tokens available
• Release token after I/O completes
• Track I/O operation metrics

Outputs:

• Token acquisition success
• I/O operation execution

Error Conditions:

• Token acquisition timeout
• I/O operation failure

FR-RESOURCE-003: Memory Tracking

Priority: Medium Description: System shall track memory usage and enforce configurable memory limits.

Inputs:

• Maximum memory threshold
• Memory allocation operation

Processing:

• Track current memory usage with atomic counter
• Check against threshold before allocation
• Increment counter on allocation
• Decrement counter on deallocation (via RAII guard)

Outputs:

• Allocation success/failure
• Current memory usage

Error Conditions:

• Memory limit exceeded
• Memory tracking overflow

3.7 Metrics and Observability (FR-METRICS)

FR-METRICS-001: Collect Processing Metrics

Priority: Medium Description: System shall collect detailed metrics during pipeline processing operations.

Metrics Collected:

• Bytes processed (input/output)
• Processing duration (total, per stage)
• Throughput (MB/s)
• Compression ratio
• Error count and types
• Active operations count
• Queue depth

Outputs:

• ProcessingMetrics entity
• Real-time metric updates

Error Conditions:

• Metric overflow
• Invalid metric values

FR-METRICS-002: Export Prometheus Metrics

Priority: Medium Description: System shall export metrics in Prometheus format via HTTP endpoint.

Inputs:

• HTTP GET request to /metrics endpoint

Processing:

• Collect current metric values from all collectors
• Format metrics in Prometheus text format
• Include metric type, help text, labels

Outputs:

• HTTP 200 response with Prometheus metrics
• Content-Type: text/plain; version=0.0.4

Error Conditions:

• Metrics collection failure
• HTTP server error

Metrics Exported:

pipelines_processed_total{status="success|error"}
pipeline_processing_duration_seconds{quantile="0.5|0.9|0.99"}
pipeline_bytes_processed_total
pipeline_chunks_processed_total
throughput_mbps
compression_ratio

FR-METRICS-003: Structured Logging

Priority: Medium Description: System shall provide structured logging with configurable log levels and tracing integration.

Inputs:

• Log events from application code
• Log level filter (error, warn, info, debug, trace)

Processing:

• Format log events with structured fields
• Include span context for distributed tracing
• Route to configured log outputs (stdout, file, etc.)
• Filter based on log level configuration

Outputs:

• Structured log messages with JSON or key-value format
• Trace spans for operation context

Error Conditions:

• Log output failure (disk full, etc.)
• Invalid log configuration

3.8 Custom Stage Extensibility (FR-CUSTOM)

FR-CUSTOM-001: Define Custom Stage Types

Priority: High Description: System shall allow developers to define custom stage types by extending the StageType enum and implementing stage-specific logic.

Inputs:

• New StageType enum variant
• Stage-specific configuration parameters
• Stage metadata (name, description)

Processing:

• Register custom stage type in system
• Validate stage type uniqueness
• Associate configuration schema with stage type

Outputs:

• Registered custom stage type
• Stage type available for pipeline configuration

Error Conditions:

• Duplicate stage type name
• Invalid stage type identifier
• Configuration schema validation failure

Extension Points:

• StageType enum in domain layer
• Display and FromStr trait implementations
• Stage configuration validation

FR-CUSTOM-002: Implement Custom Stage Logic

Priority: High Description: System shall provide extension points for implementing custom stage processing logic through domain service traits.

Inputs:

• Domain service trait definition
• Infrastructure adapter implementation
• Processing algorithm for file chunks

Processing:

• Define domain service trait (e.g., SanitizationService, TransformationService)
• Implement infrastructure adapter with concrete algorithm
• Register adapter with dependency injection container
• Integrate with StageExecutor for execution

Outputs:

• Functional custom stage implementation
• Stage available for use in pipelines

Error Conditions:

• Trait method signature mismatch
• Missing required trait bounds (Send + Sync)
• Registration failure
• Incompatible chunk processing logic

Implementation Requirements:

• Must be async-compatible (async_trait)
• Must support parallel chunk processing
• Must update ProcessingContext metrics
• Must handle errors through Result types

FR-CUSTOM-003: Register Custom Stages

Priority: High Description: System shall provide registration mechanism for custom stages with the StageExecutor.

Inputs:

• Custom stage type identifier
• StageExecutor implementation
• Supported algorithm identifiers

Processing:

• Validate stage executor implementation
• Register executor with system registry
• Associate stage type with executor
• Enable stage in pipeline configuration

Outputs:

• Registered stage executor
• Stage type available in CLI and API

Error Conditions:

• Executor registration conflict
• Invalid stage type reference
• Missing required executor methods

Registration Methods:

• Compile-time registration (preferred)
• Runtime registration via plugin interface
• Configuration-based registration

FR-CUSTOM-004: Validate Custom Stage Configuration

Priority: Medium Description: System shall validate custom stage configurations against stage-specific schemas.

Inputs:

• Custom stage configuration
• Configuration schema definition
• Stage-specific validation rules

Processing:

• Parse configuration parameters
• Validate against schema (types, ranges, formats)
• Check required vs optional parameters
• Validate parameter compatibility

Outputs:

• Validated configuration
• Configuration errors if invalid

Error Conditions:

• Missing required parameters
• Invalid parameter types
• Parameter value out of range
• Incompatible parameter combinations

FR-CUSTOM-005: Custom Stage Lifecycle Management

Priority: Medium Description: System shall support initialization and cleanup for custom stages through lifecycle hooks.

Inputs:

• Stage initialization parameters
• Processing context
• Cleanup triggers (success, failure, always)

Processing:

• Call prepare_stage() before first execution
• Allocate stage-specific resources
• Execute stage processing
• Call cleanup_stage() after completion or failure
• Release resources and finalize state

Outputs:

• Initialized stage ready for processing
• Clean resource cleanup after execution

Error Conditions:

• Initialization failure
• Resource allocation failure
• Cleanup failure (logged but not propagated)

Lifecycle Methods:

• prepare_stage(): Initialize stage resources
• cleanup_stage(): Release resources and cleanup
• Resource management through RAII patterns

FR-CUSTOM-006: Custom Stage Examples

Priority: Low Description: System shall provide comprehensive examples of custom stage implementations for common use cases.

Example Use Cases:

• Data Sanitization: Remove PII, redact sensitive fields
• Data Transformation: Convert XML to JSON, restructure data
• Data Validation: Schema validation, format checking
• Data Enrichment: Add timestamps, inject metadata
• Watermarking: Add digital watermarks to content
• Deduplication: Remove duplicate data blocks

Deliverables:

• Example implementations in examples/custom-stages/
• Documentation in pipeline/docs/src/advanced/custom-stages.md
• Integration tests demonstrating usage
• Performance benchmarks for common patterns

Error Conditions:

• None (documentation and examples)

4. Non-Functional Requirements

4.1 Performance Requirements (NFR-PERF)

NFR-PERF-001: Processing Throughput

Requirement: System shall achieve minimum throughput of 100 MB/s for file processing on standard hardware.

Measurement:

• Hardware: 4-core CPU, 8 GB RAM, SSD storage
• File size: 100 MB
• Configuration: Zstd compression (level 6), no encryption

Acceptance Criteria:

• Average throughput ≥ 100 MB/s over 10 runs
• P95 throughput ≥ 80 MB/s

NFR-PERF-002: Processing Latency

Requirement: System shall complete small file processing (1 MB) in under 50ms.

Measurement:

• File size: 1 MB
• Configuration: LZ4 compression (fastest), no encryption
• End-to-end latency from read to write

Acceptance Criteria:

• P50 latency < 30ms
• P95 latency < 50ms
• P99 latency < 100ms

NFR-PERF-003: Resource Efficiency

Requirement: System shall process files with memory usage proportional to chunk size, not file size.

Measurement:

• Process 1 GB file with 64 KB chunks
• Monitor peak memory usage

Acceptance Criteria:

• Peak memory < 100 MB for any file size
• Memory scales with concurrency, not file size

NFR-PERF-004: Concurrent Processing

Requirement: System shall support concurrent processing of multiple files up to CPU core count.

Measurement:

• Number of concurrent file processing operations
• CPU utilization and throughput

Acceptance Criteria:

• Support N concurrent operations where N = CPU core count
• Linear throughput scaling up to N operations
• CPU utilization > 80% during concurrent processing

4.2 Security Requirements (NFR-SEC)

NFR-SEC-001: Encryption Strength

Requirement: System shall use only authenticated encryption algorithms with minimum 128-bit security level.

Compliance:

• AES-256-GCM (256-bit key, 128-bit security level)
• ChaCha20-Poly1305 (256-bit key, 256-bit security level)
• XChaCha20-Poly1305 (256-bit key, 256-bit security level)

Acceptance Criteria:

• No unauthenticated encryption algorithms
• All encryption provides integrity verification
• Key sizes meet NIST recommendations

NFR-SEC-002: Key Management

Requirement: System shall securely handle encryption keys with automatic zeroization.

Implementation:

• Keys zeroized on drop (using zeroize crate)
• Keys never written to logs or error messages
• Keys stored in protected memory when possible

Acceptance Criteria:

• Memory analysis shows key zeroization
• No keys in log files or error output
• Key derivation uses memory-hard functions

NFR-SEC-003: Authentication Verification

Requirement: System shall verify authentication tags and reject tampered data.

Implementation:

• AEAD authentication tags verified before decryption
• Constant-time comparison to prevent timing attacks
• Immediate rejection of invalid authentication

Acceptance Criteria:

• Tampered ciphertext always rejected
• Authentication failure detectable
• No partial decryption of unauthenticated data

NFR-SEC-004: Input Validation

Requirement: System shall validate all external inputs to prevent injection and path traversal attacks.

Implementation:

• File paths validated and sanitized
• Configuration parameters validated against schemas
• Database queries use parameterized statements
• No direct execution of user-provided code

Acceptance Criteria:

• Path traversal attacks blocked
• SQL injection not possible
• Invalid configurations rejected

4.3 Reliability Requirements (NFR-REL)

NFR-REL-001: Error Handling

Requirement: System shall handle errors gracefully without data loss or corruption.

Implementation:

• All errors propagated through Result types
• No panics in library code (CLI may panic on fatal errors)
• Partial results discarded on pipeline failure
• Database transactions rolled back on error

Acceptance Criteria:

• No silent failures
• Error messages include context
• No data corruption on error paths
• Recovery possible from transient errors

NFR-REL-002: Data Integrity

Requirement: System shall detect data corruption through checksums and reject corrupted data.

Implementation:

• Input checksum calculated before processing
• Output checksum calculated after processing
• Checksum verification on restoration
• Authentication tags on encrypted data

Acceptance Criteria:

• Bit flip detection rate: 100%
• No false positives in integrity checks
• Corrupted data always rejected

NFR-REL-003: Atomic Operations

Requirement: System shall perform file operations atomically to prevent partial writes.

Implementation:

• Write to temporary files, then atomic rename
• Database transactions for metadata updates
• Rollback on failure

Acceptance Criteria:

• No partially written output files
• Database consistency maintained
• Recovery possible from interrupted operations

4.4 Maintainability Requirements (NFR-MAINT)

NFR-MAINT-001: Code Documentation

Requirement: All public APIs shall have rustdoc documentation with examples.

Coverage:

• Public functions, structs, enums documented
• Example code for complex APIs
• Error conditions documented
• Panic conditions documented (if any)

Acceptance Criteria:

• cargo doc succeeds without warnings
• Documentation coverage > 90%
• Examples compile and run

NFR-MAINT-002: Architectural Compliance

Requirement: System shall maintain strict layer separation per Clean Architecture.

Rules:

• Domain layer: No dependencies on infrastructure
• Application layer: Depends on domain only
• Infrastructure layer: Implements domain interfaces

Acceptance Criteria:

• Architecture tests pass
• Dependency graph validated
• No circular dependencies

NFR-MAINT-003: Test Coverage

Requirement: System shall maintain comprehensive test coverage.

Coverage Targets:

• Line coverage: > 80%
• Branch coverage: > 70%
• Critical paths (encryption, integrity): 100%

Test Types:

• Unit tests for all modules
• Integration tests for layer interaction
• E2E tests for complete workflows

Acceptance Criteria:

• All tests pass in CI
• Coverage reports generated
• Critical functionality fully tested

4.5 Portability Requirements (NFR-PORT)

NFR-PORT-001: Platform Support

Requirement: System shall compile and run on Linux, macOS, and Windows.

Platforms:

• Linux: Ubuntu 20.04+, RHEL 8+
• macOS: 10.15+ (Intel and Apple Silicon)
• Windows: 10+ (x86_64)

Acceptance Criteria:

• CI tests pass on all platforms
• Platform-specific code isolated
• Feature parity across platforms

NFR-PORT-002: Rust Version Compatibility

Requirement: System shall support stable Rust toolchain.

Rust Version:

• Minimum: Rust 1.75
• Recommended: Latest stable

Acceptance Criteria:

• Compiles on minimum Rust version
• No nightly-only features
• MSRV documented in README

4.6 Usability Requirements (NFR-USE)

NFR-USE-001: CLI Usability

Requirement: CLI shall provide clear help text, progress indication, and error messages.

Features:

• --help displays usage information
• Progress bar for long operations
• Colored output for errors and warnings
• Verbose mode for debugging

Acceptance Criteria:

• Help text covers all commands
• Progress updates at least every second
• Error messages actionable

NFR-USE-002: API Ergonomics

Requirement: Library API shall follow Rust conventions and idioms.

Conventions:

• Builder pattern for complex types
• Method chaining where appropriate
• Descriptive error types
• No unnecessary lifetimes or generics

Acceptance Criteria:

• API lint (clippy) warnings addressed
• API documentation clear
• Example code idiomatic

4.7 Extensibility Requirements (NFR-EXT)

NFR-EXT-001: Custom Stage Support

Requirement: System architecture shall support custom stage implementations without modifying core library code.

Extension Mechanisms:

• Trait-Based Extension: Define custom stages via trait implementation
• Type System Extension: Add StageType enum variants
• Registration System: Register custom stages at compile-time or runtime
• Configuration Extension: Support stage-specific configuration schemas

Acceptance Criteria:

• Custom stages implementable without forking core library
• No breaking changes required in domain layer
• Registration mechanism well-documented
• Examples provided for common patterns

NFR-EXT-002: Plugin Architecture

Requirement: System shall support plugin-style custom stages with dynamic loading capabilities (future).

Design Goals:

• Isolated custom stage code from core library
• Safe loading of external stage implementations
• Version compatibility checking
• Dependency management for plugins

Acceptance Criteria:

• Plugin interface defined and documented
• Safe sandboxing of plugin code
• Graceful handling of plugin failures
• Plugin compatibility matrix maintained

NFR-EXT-003: Extension Documentation

Requirement: System shall provide comprehensive documentation for creating custom stages.

Documentation Requirements:

• Step-by-step implementation guide
• Complete working examples
• API reference for extension points
• Best practices and patterns
• Performance tuning guidelines
• Testing strategies for custom stages

Acceptance Criteria:

• Developer can implement custom stage in < 2 hours
• All extension points documented with examples
• Common pitfalls documented with solutions
• Documentation tested by external developers

NFR-EXT-004: Backward Compatibility

Requirement: System shall maintain backward compatibility for custom stage implementations across minor version updates.

Compatibility Guarantees:

• Trait signatures stable across minor versions
• Deprecation warnings for breaking changes
• Migration guides for major version updates
• Semantic versioning strictly followed

Acceptance Criteria:

• Custom stages compile across patch versions
• Breaking changes only in major versions
• Deprecation period: minimum 2 minor versions
• Migration guides published before major releases

NFR-EXT-005: Extension Performance

Requirement: Custom stage infrastructure shall impose minimal performance overhead (<5%) compared to built-in stages.

Performance Goals:

• Trait dispatch overhead: <1% of processing time
• No unnecessary allocations in hot paths
• Zero-cost abstractions where possible
• Efficient resource sharing

Acceptance Criteria:

• Benchmarks show <5% overhead
• Custom stage throughput ≥ 95% of built-in stages
• Memory overhead minimal (documented per-stage)

5. System Interfaces

5.1 File System Interface

Description: System interacts with file system for reading input files and writing output files.

Operations:

• Read files (sequential, chunked, memory-mapped)
• Write files (buffered, with fsync option)
• List directories
• Query file metadata (size, permissions, timestamps)
• Create temporary files

Error Handling:

• File not found → PipelineError::IOError
• Permission denied → PipelineError::IOError
• Disk full → PipelineError::IOError

5.2 Database Interface

Description: System uses SQLite for persisting pipeline configurations and metadata.

Schema:

CREATE TABLE pipelines (
    id TEXT PRIMARY KEY,
    name TEXT UNIQUE NOT NULL,
    description TEXT,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL
);

CREATE TABLE pipeline_stages (
    id TEXT PRIMARY KEY,
    pipeline_id TEXT NOT NULL,
    stage_type TEXT NOT NULL,
    stage_name TEXT NOT NULL,
    order_index INTEGER NOT NULL,
    configuration TEXT NOT NULL,
    FOREIGN KEY (pipeline_id) REFERENCES pipelines(id)
);

Operations:

• Insert pipeline configuration
• Query pipeline by ID or name
• Update pipeline metadata
• Delete pipeline and stages

Error Handling:

• Connection failure → PipelineError::DatabaseError
• Constraint violation → PipelineError::DatabaseError
• Query error → PipelineError::DatabaseError

5.3 Metrics Interface (HTTP)

Description: System exposes Prometheus metrics via HTTP endpoint.

Endpoint: GET /metrics

Response Format:

# HELP pipeline_bytes_processed_total Total bytes processed
# TYPE pipeline_bytes_processed_total counter
pipeline_bytes_processed_total 1048576

# HELP pipeline_processing_duration_seconds Processing duration
# TYPE pipeline_processing_duration_seconds histogram
pipeline_processing_duration_seconds_bucket{le="0.1"} 10
pipeline_processing_duration_seconds_bucket{le="0.5"} 25
...

Error Handling:

• Server error → HTTP 500
• Not found → HTTP 404

5.4 Configuration Interface

Description: System reads configuration from TOML files and command-line arguments.

Configuration Files:

[pipeline]
default_chunk_size = 65536
max_memory_mb = 1024

[compression]
default_algorithm = "zstd"
default_level = 6

[encryption]
default_algorithm = "aes256gcm"
key_derivation = "argon2"

[database]
path = "./pipeline.db"

Command-Line Arguments:

pipeline process --input file.txt --output file.adapipe --compress zstd --encrypt aes256gcm

6. Requirements Traceability Matrix

7. Appendices

Appendix A: Algorithm Support Matrix

Appendix B: Error Code Reference

Appendix C: Custom Stage Use Cases

This appendix provides real-world examples of custom stage implementations to demonstrate the extensibility of the pipeline system.

C.1 Data Sanitization Stage

Purpose: Remove or redact Personally Identifiable Information (PII) from documents before archival or sharing.

Use Cases:

• Healthcare: Redact patient names, SSN, medical record numbers from documents
• Financial: Remove account numbers, credit card data from statements
• Legal: Redact confidential information in discovery documents
• HR: Anonymize employee data in reports

Implementation:

• StageType: Sanitization
• Algorithm examples: regex_redaction, named_entity_recognition, pattern_matching
• Configuration: Rules for what to redact, replacement patterns, allowed/blocked terms

Benefits:

• GDPR/HIPAA compliance
• Safe data sharing
• Privacy protection
• Audit trail of sanitization

C.2 Data Transformation Stage

Purpose: Convert data between formats or restructure content.

Use Cases:

• XML to JSON conversion for API modernization
• CSV to Parquet for data lake ingestion
• Document format conversion (DOCX → PDF)
• Image format optimization (PNG → WebP)

Implementation:

• StageType: Transform
• Algorithm examples: xml_to_json, csv_to_parquet, image_optimize
• Configuration: Source/target formats, transformation rules, quality settings

Benefits:

• Automated format conversion
• Data lake preparation
• Storage optimization
• API compatibility

C.3 Schema Validation Stage

Purpose: Validate data against schemas before processing or storage.

Use Cases:

• JSON Schema validation for API payloads
• XML Schema (XSD) validation for B2B integrations
• Protobuf validation for microservices
• Database schema compliance checking

Implementation:

• StageType: Validation
• Algorithm examples: json_schema, xml_schema, protobuf_validate
• Configuration: Schema file path, validation strictness, error handling

Benefits:

• Data quality assurance
• Early error detection
• Contract enforcement
• Compliance verification

C.4 Data Enrichment Stage

Purpose: Add metadata, annotations, or derived fields to data.

Use Cases:

• Add geolocation data based on IP addresses
• Inject timestamps and processing metadata
• Add classification tags based on content
• Append audit trail information

Implementation:

• StageType: Enrichment
• Algorithm examples: geo_lookup, metadata_injection, content_classification
• Configuration: Enrichment sources, field mappings, lookup tables

Benefits:

• Enhanced analytics
• Better searchability
• Compliance tracking
• Data lineage

C.5 Digital Watermarking Stage

Purpose: Embed imperceptible watermarks in content for provenance tracking.

Use Cases:

• Document watermarking with user ID and timestamp
• Image watermarking for copyright protection
• PDF watermarking for leak detection
• Video watermarking for piracy prevention

Implementation:

• StageType: Watermark
• Algorithm examples: steganography, visible_watermark, digital_signature
• Configuration: Watermark content, embedding strength, detection keys

Benefits:

• Copyright protection
• Leak detection
• Provenance tracking
• Authenticity verification

C.6 Deduplication Stage

Purpose: Identify and remove duplicate data blocks for storage efficiency.

Use Cases:

• Deduplicate file chunks in backups
• Remove duplicate records in data sets
• Content-addressed storage optimization
• Incremental backup efficiency

Implementation:

• StageType: Deduplication
• Algorithm examples: fixed_block, variable_block, content_hash
• Configuration: Block size, hash algorithm, dedup database

Benefits:

• Storage savings (50-90% typical)
• Network bandwidth reduction
• Faster backup/restore
• Cost optimization

C.7 Custom Stage Development Effort

Prerequisites:

• Rust programming experience
• Understanding of pipeline architecture
• Domain knowledge for stage-specific logic

Document Status: Draft Last Updated: 2025-01-04 Next Review: TBD Approver: TBD

Glossary

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Definitions of key terms and concepts.

A

TODO: Add glossary terms

P

Pipeline - TODO

Pipeline Stage - TODO

S

Stage - TODO

API Documentation

Version: 0.1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner Status: Draft

Links to generated API documentation.

Generated API Docs

The complete API documentation is generated from source code documentation:

• Pipeline API Documentation

Building API Docs

make docs

This generates documentation at target/doc/pipeline/index.html.

Key Modules

TODO: Add key module links and descriptions