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Introducing BunchStore: A Bunch with Persistency#

The BunchStore class serves as a straightforward key-value store, mapping a Bunch object seamlessly across both memory and the filesystem. It is particularly useful for caching API responses, utilizing its dictionary-like interface for easy data access and storage. Its file-based persistence guarantees data continuity across sessions.

Tip

The DATAPAK storage format used for serialization supports a wide range of complex data types. You can store text, images, dictionaries, lists, sets, arrays, dataframes, and more.

BunchStore example

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import numpy as np

from mltraq.opts import options
from mltraq.storage.serialization import deserialize
from mltraq.utils.bunch import BunchStore
from mltraq.utils.fs import tmpdir_ctx

with tmpdir_ctx():

    # Default location of BunchStore on filesystem
    print("Pathname:", options().get("bunchstore.pathname"))

    # Initialize object, creating file
    bs = BunchStore()

    # Set two keys
    bs["A"] = 123
    bs.B = np.array([4, 5, 6])

    # Reinitialize object object, reloading file
    bs = BunchStore()
    bs["C"] = 789

    # Accessing previouslty stored valued
    print("bs.A:", bs.A)

    data = deserialize(
        open(options().get("bunchstore.pathname"), "rb").read()
    )
    print(f"File contents: {data}")
Output
Pathname: bunchstore.data
bs.A: 123
File contents: {'A': 123, 'B': array([4, 5, 6]), 'C': 789}