lotdb 1.1.0

Persistent tree database for hierarchical metadata and file-backed datasets

LOTDB

Persistent tree database for hierarchical metadata and file-backed datasets.

LOTDB stands for Light Object Tree DB.

What it is

LOTDB stores data in StorageTree nodes:

• each node can contain child nodes
• each node can store arbitrary attributes
• the full tree can be persisted with ZODB

This is especially useful for dataset pipelines where folder structure, file references, and metadata all belong together.

Installation

Local editable install:

pip install -e .

With optional IO helpers:

pip install -e .[io]

Basic usage

from lotdb import StorageTree

tree = StorageTree(key="dataset")
node = tree.get_node_path(["speaker_01", "session_a", "clip_001"])
node.set_attribute("label", "hello")

print(node.get_attribute("label"))

Persistent database usage

from lotdb import StorageTreeDatabase

db = StorageTreeDatabase(path="./data", name="treedb.fs", new=True)
tree = db.open_connection()

tree.get_node_path(["speaker_01", "clip_001"]).set_attribute("duration", 1.23)
db.commit()
db.close_connection()
db.close()

Backward compatibility

The old short method names still work:

• ga()
• gn()
• gns()
• gna()

Readable wrappers were added so the API is easier to maintain and easier for weaker coding models to follow.

Import compatibility is also preserved for now:

• new import: import lotdb
• legacy imports still work: import TreeDB, import StorageTree

Development

• source packages live in src/lotdb and src/TreeDB
• tests live in tests/
• API notes live in docs/API.md

Publishing

PyPI publishing is configured with GitHub Actions via trusted publishing.

Recommended setup:

• create a PyPI project named lotdb
• add a trusted publisher on PyPI for this GitHub repository
• publish by creating a GitHub release

TestPyPI is also configured.

Recommended rollout:

• first connect this repo to TestPyPI trusted publishing
• trigger the Publish to TestPyPI workflow manually or with a tag like test-v1.1.0
• verify install from TestPyPI
• then connect the same repo/workflow to real PyPI
• publish a GitHub release for the real upload

Example test install:

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple lotdb