---
id: vineyard
version: "0.24.2"
license: Apache License 2.0
license_treatment: permissive
maintenance: aging
---

# vineyard — An in-memory immutable data manager

License: permissive · Maintenance: aging · Downloads: 104.4K/mo

## What it is and what it does

Vineyard is an in-memory immutable data manager designed to solve the data-sharing bottleneck in distributed big data pipelines. It provides zero-copy sharing of complex data structures (dataframes, tensors, graphs) across different systems via shared memory, eliminating serialization, deserialization, and I/O costs that typically arise when exchanging data between frameworks. The package includes out-of-the-box high-level abstractions for common data types and supports stream pipelining to allow overlapping computation and data transfer.

Vineyard runs as a distributed service with a Python client library that connects to a shared-memory manager. It is a CNCF sandbox project and targets scenarios where data is too large for a single machine and multiple systems need to collaborate on preprocessing, analysis, or training tasks. The 16 runtime dependencies include numpy, pandas, pyarrow, and pyyaml, reflecting its role as a bridge between data processing and machine learning ecosystems.

Use it for:

- Preprocessing large datasets and training models without intermediate file I/O.
- Sharing distributed graphs and tensors across graph analytics engines.
- Building data pipelines where multiple frameworks need to exchange immutable data structures with minimal overhead.
- Reducing memory-copy and I/O costs in multi-stage big data workflows on distributed clusters.
- Integrating numerical computing with machine learning frameworks in a single logical pipeline.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Vineyard is an in-memory immutable data manager that enables zero-copy sharing of distributed data across big data systems like graph analytics, numerical computing, and machine learning frameworks.

Yes, if you are building distributed big data pipelines that require zero-copy data sharing across multiple frameworks. The permissive Apache License 2.0 and production-stable status support adoption. However, install only if you have a Vineyard server infrastructure in place and are working at scale; it is not a general-purpose data library for single-machine use. The aging maintenance status warrants monitoring for security updates.

## Install

pip install vineyard
uv add vineyard
poetry add vineyard

## Installing vineyard

Before you install: Medium install friction due to compiled wheels for multiple Python versions and architectures, but prebuilt binaries are available. Maintenance is aging—last release was 715 days ago, though the repository remains active with recent commits and a stable community.

License in practice: Apache License 2.0 (permissive) allows commercial and private use with minimal restrictions, requiring only license and copyright notice preservation.

Quickstart:

pip install vineyard

import vineyard
client = vineyard.connect(vineyard_ipc_socket)
dataset = client.get(vineyard_object_id)

Requires a running Vineyard server instance and an IPC socket path; not a standalone library for local-only use.

Verify before relying:

- Whether vineyard-bdist (a listed runtime dependency) is automatically installed or must be configured separately.
- Specific performance characteristics or memory overhead compared to direct shared-memory approaches.
- Compatibility with Windows systems (classifiers list MacOS and POSIX only).

## Package facts

- License: Apache License 2.0 (permissive)
- Python support: unspecified
- Install friction: medium
- Maintenance: aging
- Downloads: 104.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

in-memory data sharing, zero-copy distributed data, big data framework integration, immutable data manager, shared memory for analytics, cross-system data exchange, distributed data abstraction, distributed-data, zero-copy-sharing, big-data-pipeline

[View on SkillFed](https://skillfed.io/packages/vineyard) · [View on PyPI](https://pypi.org/project/vineyard/)