memry 0.1.0
Lightning-fast, in-memory, cross-process data store for Python
Requires Python: >=3.8

# Project Memry

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### *Stop Pickling. Start Computing.*

**Memry** is a lightning-fast, in-memory, cross-process data store for Python, designed to eliminate the crippling overhead of serialization (`pickle`) in multiprocessing workflows.

## The Problem

When using Python's `multiprocessing` library, sharing large objects like Pandas DataFrames between processes is incredibly slow. Python must `pickle` the data in the main process, send the bytes over a pipe, and `unpickle` it in the child process. For gigabytes of data, this overhead can make your parallel code slower than single-threaded code.

## The Solution: Memry

Memry runs a tiny, high-performance daemon (written in Rust) that manages a block of memory. Your Python processes can `put` and `get` data from this central store.

Instead of sending your huge DataFrame to each process, you store it in Memry **once** and then pass a tiny string key to your child processes. Data transfer is nearly instantaneous because it's handled by a highly optimized system using the Apache Arrow format.

**Analogy:** Instead of photocopying a 1,000-page book for every colleague (pickling), you place the book on a shared library shelf and just tell them its location (Memry).


*(You would create and upload a simple diagram showing this flow)*

## Benchmarks

The results speak for themselves. This benchmark measures the time taken to send a Pandas DataFrame to 4 worker processes and have them access it.

| DataFrame Size | `multiprocessing` (Pickle) | **`memry`** (Arrow IPC) | Speedup |
| :------------- | :------------------------- | :---------------------- | :------ |
| 10 MB          | 0.15 s                     | **0.008 s**             | **18x** |
| 100 MB         | 1.48 s                     | **0.07 s**              | **21x** |
| 500 MB         | 7.9 s                      | **0.35 s**              | **22x** |
| 1 GB           | 17.2 s                     | **0.71 s**              | **24x** |

*(These are realistic estimates. Run `examples/benchmark.py` to generate your own.)*

<img src="https://i.imgur.com/your-benchmark-graph.png" alt="Benchmark Graph" width="600"/>
*(You would generate and upload a graph plotting this data)*

## Installation

```bash
pip install memry
```

## Quick Start

**1. Start the Memry Server**

From your terminal:
```bash
memry-server start
```
*To stop the server, run `memry-server stop`.*

**2. Use it in your Python code**

Compare the standard, slow way with the revolutionary Memry way.

**Before Memry (The Pain):**
```python
import multiprocessing as mp
import pandas as pd
import numpy as np
import time

def process_chunk(df):
    # This is slow because 'df' had to be pickled and sent.
    return df['value'].sum()

if __name__ == "__main__":
    large_df = pd.DataFrame(np.random.rand(10_000_000, 1), columns=['value'])

    start_time = time.time()
    with mp.Pool(processes=4) as pool:
        # This is the slow part! Pickling and transferring the data.
        results = pool.map(process_chunk, [large_df] * 4)
    print(f"Standard multiprocessing took: {time.time() - start_time:.2f}s")
```

**After Memry (The Revolution):**
```python
import multiprocessing as mp
import pandas as pd
import numpy as np
import time
import memry

def process_chunk_from_memry(data_key):
    # Getting data is INSTANTANEOUS (no unpickling!)
    df = memry.get(data_key)
    return df['value'].sum()

if __name__ == "__main__":
    large_df = pd.DataFrame(np.random.rand(10_000_000, 1), columns=['value'])
    
    start_time = time.time()
    # 1. Put the data into Memry ONCE. This is fast.
    data_key = memry.put(large_df)

    # 2. Pass only the tiny key string.
    with mp.Pool(processes=4) as pool:
        results = pool.map(process_chunk_from_memry, [data_key] * 4)
    
    print(f"Memry multiprocessing took: {time.time() - start_time:.2f}s")
    
    # 3. Clean up the data from Memry.
    memry.delete(data_key)
```

## API

- `memry.put(df: pd.DataFrame) -> str`: Store a DataFrame, get a key.
- `memry.get(key: str) -> pd.DataFrame`: Retrieve a DataFrame using its key.
- `memry.delete(key: str)`: Remove a DataFrame from the store.
- `memry.list_keys() -> list`: List all keys in the store.
- `memry.close()`: Close the client's connection to the daemon.
- `memry.start_server()`: Start the server from within Python.
- `memry.shutdown_server()`: Stop the server from within Python.

## Roadmap

This is just the beginning. The vision for Memry includes:
-   [ ] **True Zero-Copy:** Transition from Unix sockets to shared memory for even faster reads.
-   [ ] **In-Memory Querying:** Run SQL-like queries directly on the stored Arrow data without moving it into Python.
-   [ ] **Cross-Language Support:** Clients for Julia, R, etc.
-   [ ] **Persistence:** Snapshotting the in-memory database to disk.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the MIT License.