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(...).
Explain & Profile
db.explain() and db.profile() are first-class methods alongside
db.execute(). They are intentionally separate calls — neither
routes through execute() — so plan inspection and runtime metrics
must be requested explicitly.
plan = db.explain(
"MATCH (p:Person) WHERE p.name = $name RETURN p",
{"name": "Alice"},
)
print(plan["shape"]) # "readOnly"
print(plan["tree"]["operator"]) # top-most operator label
profile = db.profile(
"MATCH (p:Person) WHERE p.name = $name RETURN p",
{"name": "Alice"},
)
print(profile["metrics"]["total_elapsed_ns"])
print(profile["metrics"]["per_operator"]) # per-step inclusive timing
explain() never invokes the executor — calling it on a mutating
query (CREATE, MERGE, SET, DELETE, REMOVE) leaves the graph
untouched.
profile()executes the query for real. Mutating queries produce the same side effects asexecute(). Useexplain()to inspect a mutating plan without running it.
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.
Concurrency, transactions and streams
Writers serialize on the database's writer lock; readers never wait for
it. Every call that may wait for the lock (execute, transaction,
clear, load_snapshot, opening a stream) waits with the GIL released,
so the thread holding the lock always keeps running.
execute()andtransaction([...])take the lock, run, commit and release it within one call on one thread. OnAsyncDatabasethey run on the loop's default executor; a small or saturated executor delays writes but cannot deadlock them, since the writer holding the lock never needs an executor thread to finish.- A mutating
stream()(one whose query writes) holds the writer lock from open until it is exhausted (commit) or closed early (rollback). It runs on a native thread of its own, so a stream can be pulled, closed or garbage-collected on any thread, and may outlive theDatabaseit came from.AsyncDatabase.streamnever waits for the lock on the event loop: a stream that does not get it at once finishes opening on a thread of its own. It pulls one row per iteration step. - While your own code holds a mutating stream open, do not wait on
another write to the same database from that same thread or coroutine:
that write waits for the lock your stream holds. On
AsyncDatabase, avoid awaiting other database calls inside the loop body of a mutating stream when many writers may be queued on the executor; collect the rows first, or usetransaction([...]).
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 ignoresheight— see functions reference for the full spatial reference and known limitations.
Errors
LoraError— base classLoraQueryError— parse / analyze / execute failureInvalidParamsError— 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
1 maintainer
Metadata
1 maintainer
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