input-output-build 0.1.1
Utilities for building and testing input/output pipelines.
Requires Python: >=3.9
Keywords: input, output, build, pipeline, automation

# input-output-build

`input-output-build` is a modular framework designed for constructing,
validating, and testing high‑performance input/output (I/O) pipelines
across diverse environments. Whether you're building data ingestion
systems, streaming architectures, ETL workflows, or interactive
command-line tooling, this package provides a structured ecosystem for
orchestrating I/O operations with robustness, extensibility, and
composability in mind.

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## ✨ Features

### 🔧 Modular I/O Pipeline Architecture

-   Build pipelines from **independent, reusable blocks**
-   Native support for **parallelism**, **batch processing**, and
    **streaming**
-   Strong typing philosophy ensures that **every transformation is
    traceable**

### 🧪 Built-In Test Harness

-   Automatic test scaffolding for I/O-heavy applications\
-   Golden‑file comparison utilities\
-   Randomized fuzzing for stress-testing your I/O flows

### 📊 Rich Logging & Introspection

-   Pipeline visualization via ASCII graphs\
-   Event tracing with timestamps\
-   Execution statistics (latencies, throughput, failure snapshots)

### 🚀 Extensible Plugin System

-   Load pipeline components dynamically
-   Register new input/output types at runtime
-   Optional sandboxing mode for experimental blocks

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## 📦 Installation

``` bash
pip install input-output-build
```

or for development:

``` bash
git clone https://github.com/youruser/input-output-build
cd input-output-build
pip install -e .
```

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## 📁 Project Structure

    input-output-build/
    ├── docs/
    │   ├── architecture.md
    │   ├── pipeline-examples.md
    │   └── plugin-development.md
    ├── examples/
    │   ├── stream_processor.py
    │   ├── batch_to_stream.py
    │   └── io_benchmark.py
    ├── src/
    │   └── input_output_build/
    │       ├── core/
    │       │   ├── base.py
    │       │   ├── pipeline.py
    │       │   └── executor.py
    │       ├── io/
    │       │   ├── file_reader.py
    │       │   ├── file_writer.py
    │       │   ├── stream_input.py
    │       │   └── http_source.py
    │       ├── utils/
    │       │   ├── logger.py
    │       │   ├── benchmarking.py
    │       │   └── graph.py
    │       └── cli.py
    └── tests/
        ├── test_pipeline.py
        ├── test_io_handlers.py
        └── test_benchmarks.py

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## 🏗️ Example: Building a Pipeline

``` python
from input_output_build.core.pipeline import Pipeline
from input_output_build.io.file_reader import FileReader
from input_output_build.io.file_writer import FileWriter
from input_output_build.utils.logger import Logger

pipeline = (
    Pipeline("demo")
    .add(FileReader("input.txt"))
    .add(lambda data: data.upper())
    .add(FileWriter("output.txt"))
)

Logger.info("Running pipeline...")
pipeline.run()
```

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## 🔌 Creating Your Own Block

``` python
from input_output_build.core.base import Block

class ReverseText(Block):
    def process(self, data):
        return data[::-1]
```

Use it inside a pipeline:

``` python
pipeline.add(ReverseText())
```

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## 📈 Benchmarking Pipelines

``` python
from input_output_build.utils.benchmarking import benchmark

result = benchmark(pipeline.run, iterations=100)
print(result)
```

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## 🧩 Plugin Development

Pipeline components can be loaded dynamically at runtime.

Create a plugin:

``` python
# myplugin.py
from input_output_build.core.base import Block

class Trim(Block):
    def process(self, data):
        return data.strip()
```

Register it:

``` python
pipeline.load_plugin("myplugin.Trim")
```

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## 🗺️ Architecture Overview

### Core Concepts

-   **Block** -- atomic processing unit\
-   **Pipeline** -- sequential chain of blocks\
-   **Executor** -- runtime responsible for scheduling\
-   **Inspector** -- provides real-time stats and debugging hooks

### Execution Model

    Input → Block A → Block B → Block C → Output

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## 🧵 Concurrency Model

The framework supports:

-   multiprocessing pipelines\
-   multithreaded workers\
-   async/await streaming execution

```{=html}
<!-- -->
```
                ┌─────────┐
    Input ───►  │ Executor│ ───► Worker Pool ───► Blocks
                └─────────┘

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## 📚 Documentation

See:

-   `docs/architecture.md`\
-   `docs/pipeline-examples.md`\
-   `docs/plugin-development.md`

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## 🛠️ Development

Run the test suite:

``` bash
pytest -q
```

Format and lint:

``` bash
ruff check .
black .
```

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## 🤝 Contributing

Pull requests are welcome!\
Please follow the contribution guidelines and coding standards.

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## 📝 License

MIT License © 2025 Your Name

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## ⭐ Acknowledgements

Special thanks to contributors and the open-source community for
inspiring the modular pipeline design philosophy.

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