lora-python 0.7.0

Python bindings for the Lora in-memory graph database

lora-python

Python bindings for the Lora graph engine. Ships both a synchronous PyO3 Database class and an asyncio-compatible AsyncDatabase wrapper that never blocks the event loop.

Package: lora-python.

Install (local dev)

cd crates/bindings/lora-python
python3 -m venv .venv && source .venv/bin/activate
pip install -U pip maturin pytest pytest-asyncio
maturin develop         # builds the Rust extension into the venv
pytest                  # runs the sync + async smoke tests

maturin develop produces a lora_python/_native.<platform>.so inside the package and makes import lora_python work immediately.

Sync usage

from lora_python import Database, is_node

db = Database.create()
db.execute("CREATE (:Person {name: $n, age: $a})", {"n": "Alice", "a": 30})

res = db.execute("MATCH (n:Person) RETURN n")
for row in res["rows"]:
    n = row["n"]
    if is_node(n):
        print(n["properties"]["name"])

Initialization rule:

from lora_python import Database

scratch = Database.create()            # in-memory
persistent = Database.create("app", {"database_dir": "./data"})  # persistent: ./data/app.loradb

If you want persistence, pass a database name and database_dir to Database.create(...) or Database(...).

Async usage (non-blocking)

import asyncio
from lora_python import AsyncDatabase

async def main():
    db = await AsyncDatabase.create()
    await db.execute("CREATE (:Person {name: 'Alice'})")
    r = await db.execute("MATCH (n:Person) RETURN n.name AS name")
    print(r["rows"])

asyncio.run(main())

Async initialization follows the same rule:

db = await AsyncDatabase.create()            # in-memory
db = await AsyncDatabase.create("app", {"database_dir": "./data"})  # persistent: ./data/app.loradb

AsyncDatabase.execute dispatches the query onto the default asyncio thread pool via asyncio.to_thread. The PyO3 Database.execute releases the Python GIL for the duration of engine work, so other coroutines on the event loop can progress while a query runs. A dedicated test proves the event loop continues ticking during a 2 000-node MATCH.

Typed value model

Same conceptual contract as lora-node / lora-wasm:

Python shape	Lora value
None, bool, int, float, str	scalars
list, dict	collections
{"kind": "node", "id", "labels", "properties"}	node
{"kind": "relationship", "id", …}	relationship
{"kind": "path", "nodes": [...], "rels": [...]}	path
{"kind": "date", "iso": "YYYY-MM-DD"} (and time, …)	temporal
point dicts — see below	point

Points are returned as dicts keyed on their CRS:

SRID	Dict
7203	{"kind": "point", "srid": 7203, "crs": "cartesian", "x", "y"}
9157	{"kind": "point", "srid": 9157, "crs": "cartesian-3D", "x", "y", "z"}
4326	{"kind": "point", "srid": 4326, "crs": "WGS-84-2D", "x", "y", "longitude", "latitude"}
4979	{"kind": "point", "srid": 4979, "crs": "WGS-84-3D", "x", "y", "z", "longitude", "latitude", "height"}

Constructors and guards are exported from lora_python.types: date, time, localtime, datetime, localdatetime, duration, cartesian, cartesian_3d, wgs84, wgs84_3d, is_node, is_relationship, is_path, is_point, is_temporal.

distance() on WGS-84-3D points ignores height — see functions reference for the full spatial reference and known limitations.

Errors

• LoraError — base class
• LoraQueryError — parse / analyze / execute failure
• InvalidParamsError — a parameter value couldn't be mapped

All three are available as lora_python.LoraError, etc.

Persistence

Database.create("app", {"database_dir": "./data"}), Database("app", {"database_dir": "./data"}), and await AsyncDatabase.create("app", {"database_dir": "./data"}) open or create an archive-backed persistent database at ./data/app.loradb. Reopening the same path replays committed writes before returning the handle.

Call db.close() / await db.close() before reopening the same archive inside one process.

For explicit WAL directories with managed snapshots, use open_wal:

db = Database.open_wal(
    "./data/wal",
    {
        "snapshot_dir": "./data/snapshots",
        "snapshot_every_commits": 1000,
        "snapshot_keep_old": 2,
    },
)

snapshot_options accepts the same compression/encryption options as save_snapshot.

Snapshots accept the same broad shapes in sync and async APIs:

import io
from lora_python import Database

db = Database.create()
db.execute("CREATE (:Person {name: 'Alice'})")

meta = db.save_snapshot("./graph.lorasnap")   # path / PathLike
raw = db.save_snapshot("binary")              # bytes
text = db.save_snapshot("base64")             # base64 str
buf = io.BytesIO()
meta = db.save_snapshot(buf)                  # binary writer

db.load_snapshot("./graph.lorasnap")
db.load_snapshot(raw)
db.load_snapshot(io.BytesIO(buf.getvalue()))
db.load_snapshot(text, format="base64")

Architecture

lora-database (Rust)
   └── lora-python (crate, cdylib)             <- PyO3 bindings
          ├── Database (sync, releases the GIL)
          └── python/lora_python/
                 ├── _async.py  AsyncDatabase via asyncio.to_thread
                 └── types.py   typed dicts + constructors + guards