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High-performance embedded graph database for Python, written in Rust: ACID transactions, MVCC time-travel, a Cypher-like query language (NQL), vector search, and zero-copy Apache Arrow export for ML pipelines.

Runs in-process — no server to deploy. One pip install, one file on disk.

Install

pip install nopaldb

Prebuilt wheels for Linux (x86_64/aarch64), macOS (Intel/Apple Silicon) and Windows, for CPython 3.10+ (abi3: one wheel per platform covers all versions).

What the wheel contains

These wheels are built with the sled storage backend only. That is what Graph.open(...) uses, and it is the backend the project supports for production use.

The engine is selectable in the API — Graph.open_with_options(path, engine=...) — but engine="redb" raises ValueError on a wheel from PyPI, because that backend is not compiled in. redb is experimental: it exists behind a Cargo feature, is not enabled by default, and is still being qualified against the crash-recovery suite. To try it you must build from source:

pip install maturin
git clone https://github.com/sharop/nopaldb && cd nopaldb
maturin build --release --features python-full,storage-redb -m nopaldb/Cargo.toml

Treat such a build as an experiment, not as a supported configuration. The wheels on PyPI will ship redb once it clears its qualification gate.

Quickstart

import nopaldb

graph = nopaldb.Graph.open("./data.db")

# ACID transactions
tx = graph.begin_transaction()
alice = tx.add_node("Person", {"name": "Alice", "age": 30})
bob = tx.add_node("Person", {"name": "Bob", "age": 25})
tx.add_edge(alice, bob, "KNOWS")
tx.commit()

# Query with NQL
result = graph.execute_nql("""
    find p.name, p.age
    from (p:Person)
    where p.age > 25
""")
for row in result:
    print(f"{row['p.name']}: {row['p.age']}")

graph.close()

Isolation levels

tx = graph.begin_transaction(isolation="serializable")
# read_committed (default) | repeatable_read | serializable | read_uncommitted

Serializable transactions detect write conflicts and deadlocks; see the durability guarantees and isolation guide.

Arrow / ML pipelines

import pyarrow as pa

arrow_bytes = graph.to_arrow(label="Person")
batch = pa.ipc.open_stream(arrow_bytes).read_next_batch()
df = batch.to_pandas()   # zero-copy into Pandas / Polars / PyTorch pipelines

Good to know

  • One process per data directory (embedded file lock). Threads within the process run in parallel — the bindings release the GIL during database calls.
  • Use graph.bulk_loader(batch_size) for large ingestions.
  • Time-travel: MVCC version history is queryable; old versions are garbage collected on demand.

License: MPL-2.0 (the nopaldb library — this repository in its entirety). The companion ecosystem apps (MCP server, NDBStudio) are AGPL-3.0-only and live in their own repositories: https://github.com/Anxious-Mind-Group/nopaldb-mcp · https://github.com/Anxious-Mind-Group/ndbstudio. Releases ≤ 0.4.31 were AGPL-3.0-only. NopalDB™ is a trademark of Sergio Haro Pérez.

Metadata

Release files for nopaldb 0.5.22

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Source distribution for nopaldb 0.5.22
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nopaldb-0.5.22-cp310-abi3-win_amd64.whl CPython 3.10 abi3 Windows x86-64 Details
nopaldb-0.5.22-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.10 abi3 Linux glibc 2.17+ x86-64 Details
nopaldb-0.5.22-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.10 abi3 Linux glibc 2.17+ ARM64 Details
nopaldb-0.5.22-cp310-abi3-macosx_11_0_arm64.whl CPython 3.10 abi3 macOS 11.0+ ARM64 Details
nopaldb-0.5.22-cp310-abi3-macosx_10_12_x86_64.whl CPython 3.10 abi3 macOS 10.12+ x86-64 Details

Total release size: 23.8 MB

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