Metadata-Version: 2.4
Name: aaiclick
Version: 0.0.10
Summary: A Python framework that translates Python code into ClickHouse operations for big data computing
Author: aaiclick Contributors
License: MIT
Project-URL: Homepage, https://github.com/kolodkin/aaiclick
Project-URL: Repository, https://github.com/kolodkin/aaiclick
Project-URL: Issues, https://github.com/kolodkin/aaiclick/issues
Project-URL: Documentation, https://aaiclick.readthedocs.io
Keywords: clickhouse,big data,database,analytics,data processing
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Database
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pyarrow>=23.0.0
Requires-Dist: python-dotenv>=1.2.0
Requires-Dist: pyyaml>=6.0.3
Requires-Dist: pydantic>=2.12.0
Requires-Dist: sqlmodel>=0.0.38
Requires-Dist: alembic>=1.18.0
Requires-Dist: chdb>=4.1.0
Requires-Dist: aiosqlite>=0.22.0
Requires-Dist: greenlet>=3.0.0
Requires-Dist: croniter>=6.0.0
Provides-Extra: distributed
Requires-Dist: clickhouse-connect>=0.15.0; extra == "distributed"
Requires-Dist: asyncpg>=0.31.0; extra == "distributed"
Requires-Dist: psycopg2-binary>=2.9.11; extra == "distributed"
Provides-Extra: ai
Requires-Dist: litellm>=1.83.0; extra == "ai"
Requires-Dist: ollama>=0.6.0; extra == "ai"
Provides-Extra: all
Requires-Dist: aaiclick[ai,distributed]; extra == "all"
Provides-Extra: test
Requires-Dist: pytest>=9.0.0; extra == "test"
Requires-Dist: pytest-asyncio>=1.3.0; extra == "test"
Requires-Dist: pytest-cov>=7.1.0; extra == "test"
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Provides-Extra: docs
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Requires-Dist: mkdocs-material>=9.7.0; extra == "docs"
Requires-Dist: mkdocstrings[python]>=1.0.0; extra == "docs"
Provides-Extra: dev
Requires-Dist: aaiclick[all,test]; extra == "dev"
Dynamic: license-file

# aaiclick

A Python framework that translates Python code into ClickHouse operations for big data computing.

## Overview

aaiclick is a data orchestration framework built to make distributed computing easy, with three principles in mind:

1. **Simplicity** — Python-native syntax and dynamic task execution.
2. **Performance** — Utilizes ClickHouse's powerful distributed engine. Data lives in ClickHouse as columnar tables; Python code orchestrates operations — arithmetic, filtering, aggregation, joins — that execute as ClickHouse queries.
3. **AI Lineage Superpower** — Query your data flow. How did this value get here? Why don't we see that value there? Trace lineage across operations and debug pipelines with AI-powered agents.

Local (in-process, zero setup) and distributed (Docker Compose provided) deployments.
Runs locally with embedded chdb + SQLite, or scales out with remote ClickHouse + PostgreSQL.

**Early stage — looking for early adopters to join the ride and provide feedback.**

## Installation

The base install includes embedded [chdb](https://clickhouse.com/docs/chdb) and SQLite — no external servers needed:

```bash
pip install aaiclick
python -m aaiclick setup
```

For a distributed deployment (remote ClickHouse server + PostgreSQL):

```bash
pip install "aaiclick[distributed]"
```

For AI features (lineage tracing, debug agents):

```bash
pip install "aaiclick[ai]"
# or all extras:
pip install "aaiclick[all]"
```

## Orchestration

Define tasks and jobs with decorators — all data operations execute as ClickHouse queries:

```python
from aaiclick import create_object_from_value
from aaiclick.orchestration import job, task

@task
async def load_sales():
    return await create_object_from_value({
        "region": ["US", "EU", "US", "EU", "US"],
        "amount": [500, 300, 150, 200, 80],
    })

@task
async def analyze(sales):
    # GROUP BY + SUM — runs as a single ClickHouse query
    by_region = await sales.group_by("region").sum("amount")
    print(await by_region.data())  # → {'region': ['EU', 'US'], 'amount': [500, 730]}

    # append rows without leaving ClickHouse
    await sales.insert({"region": ["JP"], "amount": [400]})

@job("sales_pipeline")
def sales_pipeline():
    sales = load_sales()
    # dependencies resolved from arguments
    result = analyze(sales=sales)
    return result

if __name__ == "__main__":
    from aaiclick.orchestration import job_test
    job_test(sales_pipeline)  # runs all tasks locally for debugging
```

```bash
python sales_pipeline.py
```

## Data Operation Only Mode

Use `data_context()` directly for interactive work without orchestration:

```python
import asyncio
from aaiclick import create_object_from_value
from aaiclick.data.data_context import data_context

async def main():
    async with data_context():
        prices = await create_object_from_value([10.0, 20.0, 30.0])

        total = await (prices + prices * 0.1)  # scalars broadcast automatically
        print(await total.data())  # → [11.0, 22.0, 33.0]
        print(await (await total.mean()).data())  # → 22.0

asyncio.run(main())
```

## Documentation

[aaiclick.readthedocs.io](https://aaiclick.readthedocs.io/en/latest/)

## License

MIT License - see [LICENSE](LICENSE) for details.
