namidb 0.1.0

Cloud-native graph database on object storage — Python bindings

namidb (Python bindings)

Python wrapper for the NamiDB storage + query engine. Backed by Rust via pyo3 and built with maturin.

Install

pip install namidb              # released wheel — Python ≥ 3.9, abi3
pip install 'namidb[pandas]'    # + DataFrame interop
pip install 'namidb[polars]'    # + polars interop

Wheels are published for Linux (x86_64, aarch64), macOS (arm64), and Windows (x86_64) via the python-wheels.yml workflow on every py-v* tag — pyarrow >= 14 is a hard transitive dependency. Intel macOS users fall back to the sdist (slower install, same runtime behaviour).

Build from source

pip install maturin
cd crates/namidb-py
maturin develop --release --extras test

Once maturin develop finishes, the namidb module is importable from any Python ≥ 3.9 environment:

import uuid
import namidb as tg

client = tg.Client("memory://acme")

alice = str(uuid.uuid7())
bob = str(uuid.uuid7())

client.upsert_node("Person", alice, {"name": "Alice", "age": 30})
client.upsert_node("Person", bob, {"name": "Bob"})
client.upsert_edge("KNOWS", alice, bob, {"since": 2020})
client.commit()
client.flush()

print(client.lookup_node("Person", alice))
# {'id': '...', 'label': 'Person', 'lsn': 1, 'schema_version': 0,
#  'properties': {'name': 'Alice', 'age': 30}}

print(client.scan_label("Person"))
print(client.out_edges("KNOWS", alice))
print(client.cache_stats())

Cypher queries

Client.cypher(query, params=None) runs a Cypher query against the current namespace and returns a QueryResult:

client.cypher("CREATE (a:Person {name: 'Alice', age: 30})")
client.cypher("CREATE (a:Person {name: 'Bob',   age: 25})")
client.commit()

result = client.cypher(
    "MATCH (p:Person) WHERE p.age > $min RETURN p.name AS name, p.age AS age",
    params={"min": 26},
)

print(result.columns)   # ['name', 'age']
print(len(result))      # 1
print(result.first())   # {'name': 'Alice', 'age': 30}
for row in result.rows():
    print(row)          # {'name': 'Alice', 'age': 30}

Cypher writes (CREATE / SET / DELETE / MERGE / REMOVE) are durably committed (WAL append + manifest CAS) before cypher() returns — the executor calls commit_batch() internally at the end of every write plan. Call client.flush() periodically to push the memtable into L0 SSTs. This is different from the upsert_node / upsert_edge / tombstone_* API, which stages mutations and requires an explicit client.commit().

Async API

The same surface is available as a Python coroutine via Client.acypher for asyncio / FastAPI / aiohttp integration:

import asyncio
import namidb as tg


async def main() -> None:
    client = tg.Client("memory://acme")
    await client.acypher("CREATE (p:Person {name: 'Alice'})")
    client.commit()
    result = await client.acypher(
        "MATCH (p:Person {name: $name}) RETURN p.name AS name",
        params={"name": "Alice"},
    )
    print(result.rows())


asyncio.run(main())

acypher is driven by the pyo3-async-runtimes tokio bridge — every call runs on the same multi-threaded tokio runtime that backs the synchronous API, so mixing the two from the same Client is safe.

Type mapping (Cypher ↔ Python)

Both cypher parameters and QueryResult.rows() follow the same mapping:

Cypher RuntimeValue	Python type
Null	None
Bool	bool
Integer	int
Float	float
String	str
Bytes	bytes
Vector(Vec<f32>)	list[float]
List	list
Map	dict[str, ...]
Date	datetime.date
DateTime (UTC µs)	datetime.datetime UTC
Node(NodeValue)	{"_kind": "node", "id", "label", "properties"}
Rel(RelValue)	{"_kind": "rel", "edge_type", "src", "dst", "properties"}
Path	list[Node|Rel] alternating

bool is intentionally checked before int so that Python True / False do not silently round-trip as Integer(1) / Integer(0).

Bulk inserts

Client.merge_nodes and Client.merge_edges batch many writes under a single tokio-runtime + mutex round-trip. They are the right ingestion path when you have thousands of rows (Cypher CREATE parses + plans + executes per call):

import uuid
import namidb as tg

client = tg.Client("memory://acme")

# Bulk insert: each row needs an "id" UUID string + arbitrary properties.
client.merge_nodes(
    "Person",
    [{"id": str(uuid.uuid4()), "name": f"p{i}", "age": 20 + i} for i in range(10_000)],
)
# Edges: each row needs "src" + "dst" UUIDs.
client.merge_edges(
    "KNOWS",
    [
        {"src": "uuid-a", "dst": "uuid-b", "since": 2020},
        {"src": "uuid-b", "dst": "uuid-c", "since": 2021},
    ],
)
client.commit()        # WAL + manifest CAS
client.flush()         # memtable -> L0 SSTs

merge_nodes / merge_edges stage into the current batch (same lifecycle as upsert_*) — call client.commit() to make the mutations durable.

Arrow / pandas / polars output

pyarrow >= 14 ships as a hard dependency. Every QueryResult can materialise as a pyarrow.Table; pandas / polars conversions delegate to it.

result = client.cypher(
    "MATCH (p:Person) RETURN p.name AS name, p.age AS age ORDER BY p.age DESC"
)

table = result.to_arrow()              # pyarrow.Table
df = result.to_pandas()                # pandas.DataFrame  (needs pandas)
pl_df = result.to_polars()             # polars.DataFrame  (needs polars)

Column order follows the RETURN projection from the parsed plan (not the runtime row's BTreeMap ordering), so RETURN p.name AS name, p.age AS age always yields columns ["name", "age"] even when zero rows match.

Pandas and Polars are optional extras:

pip install 'namidb[pandas]'
pip install 'namidb[polars]'

Calling to_polars() without the polars extra raises a clear ImportError pointing at the install command.

For label-wide scans you can skip the Cypher round-trip entirely:

table = client.scan_label_arrow("Person")
# Columns: id, label, lsn, schema_version, then the union of property
# keys across the scanned views (missing keys filled with None).

Storage backends

URI scheme	Backend	Status
memory://<ns>	object_store::memory::InMemory	Stable. Ephemeral, single-process.
s3://<bucket>[/<prefix>]?ns=<ns>...	object_store::aws::AmazonS3	Stable. Works against any S3-compatible service.
file://...	object_store::local::LocalFileSystem	Not supported in v0. Upstream lacks PutMode::Update, required by manifest CAS.
gs://...	object_store::gcp	Planned.
az://...	object_store::azure	Planned.

AWS S3

import namidb as tg

client = tg.Client(
    "s3://my-bucket/data?ns=prod"
    "&region=us-west-2"
)

Credentials are read from the standard AWS environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN, AWS_DEFAULT_REGION). Query-string region=... overrides the env.

LocalStack (local persistent storage)

docker run -p 4566:4566 -e SERVICES=s3 localstack/localstack
aws --endpoint-url=http://localhost:4566 s3 mb s3://namidb-dev
export AWS_ACCESS_KEY_ID=test
export AWS_SECRET_ACCESS_KEY=test

client = tg.Client(
    "s3://namidb-dev?ns=local"
    "&endpoint=http://localhost:4566"
    "&allow_http=true"
    "&region=us-east-1"
)

The allow_http=true flag is required because LocalStack does not serve TLS by default.

Scope (v0)

• memory:// for single-process testing, s3:// for persistent storage (any S3-compatible service; LocalStack works for local persistence). file:// is intentionally unsupported — see the backends table above.
• Synchronous Python API + async coroutine API (acypher). Under the hood every call drives a tokio runtime owned by the Client; the first call you make per process pays the bootstrap cost.
• The same SST + bloom cache used by the Rust read path (SstCache) is exposed via client.cache_stats() so application-level dashboards can graph hit rate.
• Cypher coverage matches the Rust engine: LDBC SNB Interactive IC01 through IC12, factorized execution toggleable via NAMIDB_FACTORIZE=1. See the project README for the engine's surface and the RFCs in docs/rfc/ for design details.

Running the integration test (optional)

The pytest suite under tests/ ships a LocalStack round-trip test that is @pytest.mark.skipif-guarded on the NAMIDB_TEST_LOCALSTACK_BUCKET env var. To enable it:

docker run -p 4566:4566 -e SERVICES=s3 localstack/localstack &
aws --endpoint-url=http://localhost:4566 s3 mb s3://namidb-it
export NAMIDB_TEST_LOCALSTACK_BUCKET=namidb-it
export AWS_ACCESS_KEY_ID=test AWS_SECRET_ACCESS_KEY=test
.venv/bin/pytest tests/test_uri.py::test_s3_localstack_round_trip -v

Releasing to PyPI

1. Bump version in crates/namidb-py/pyproject.toml and crates/namidb-py/Cargo.toml (they must match).
2. Update CHANGELOG.md (or this README's release notes section).
3. Commit, then tag and push:

   git tag py-v0.1.0
   git push origin py-v0.1.0
   

4. python-wheels.yml builds 4 wheels (Linux x86_64/aarch64, macOS arm64, Windows x86_64) + sdist, smoke-tests one wheel on Python 3.9 and 3.13, then publishes to PyPI via OIDC trusted publishing (configured once per account at https://pypi.org/manage/account/publishing/).

py-v* tag prefix keeps Python releases separate from any future v* tags that mark engine / crate releases.