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
| 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}")
|
OutputPathname: bunchstore.data
bs.A: 123
File contents: {'A': 123, 'B': array([4, 5, 6]), 'C': 789}