genestore 0.3.0

Python bindings for genegraph-storage. Store your numpy data in Lance format

pygenestore

Store your numpy arrays at scale using the Lance format. Handles millions of rows as far as the memory goes.

Usage

It is possible to create multiple storages by passing different directories to create_storage.

It is possible to store different arrays in the same storage, just set different names.

Default API (blocking)

import numpy as np
import genestore

# Configure storage
builder = genestore.store_array("./lance_data")
builder.with_max_rows_per_file(500_000)
builder.with_compression("zstd")

# Build storage instance
storage = builder.build()

# Create data (2D float64 numpy array)
np.random.seed(42)
x = np.random.randn(1000, 128).astype(np.float64)

# Store (blocking)
path = storage.store(x, "my_dataset")
print("Stored at:", path)

# Load (blocking)
y = storage.load("my_dataset")
print("Loaded shape:", y.shape)

# Verify roundtrip
assert np.allclose(x, y)
assert np.array_equal(x, y)
print("✓ Data verification passed")

Async API

import numpy as np
import genestore
import asyncio

async def main():
    # Create a storage builder and configure it
    builder = genestore.create_storage(f"./lance_data")
    builder.with_max_rows_per_file(500000)
    builder.with_compression("zstd")

    # Build the storage instance
    storage = builder.build()

    # Create a numpy array (dense matrix)
    np.random.seed(42)  # For reproducibility
    data = np.random.randn(1000, 128).astype(np.float64)

    # Store the array (await the async call)
    path = await storage.aio.store(data, "my_dataset")
    print(f"Storage at: {path}")

    # Load the array back using the NAME (not path)
    loaded_data = await storage.aio.load("my_dataset")
    print(f"Loaded shape: {loaded_data.shape}")

    # Verify the data
    assert np.allclose(data, loaded_data)
    print("✓ Data verification passed!")

if __name__ == "__main__":
    asyncio.run(main())

Tests

pip install -r requirements-dev.txt
pytest tests/