vineyard
An in-memory immutable data manager
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Vineyard server instance and an IPC socket path; not a standalone library for local-only use.
- 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 · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 (permissive) allows commercial and private use with minimal restrictions, requiring only license and copyright notice preservation.
last release 2024-08-29 (715 days) · last repo commit 2026-01-22 · 962 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 104,367 downloads/mo, #12,751 on PyPI
Alternatives
Verify before relying
pip install vineyard
import vineyard
client = vineyard.connect(vineyard_ipc_socket)
dataset = client.get(vineyard_object_id)- 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).
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 on this page. Verify before relying on it.
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
vineyard on PyPI
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.
Requires a running Vineyard server instance and an IPC socket path; not a standalone library for local-only use.
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)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 | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 16 packagesargcompleteclicketcd-distrolazy-importmakefunnumpypsutilpyarrowpyyamlsetuptoolssortedcontainerstreelibvineyard-bdistpandaspickle5shared-memory38 |
| Maintenance | Aging 715 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 104,367 / month, #12,751 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: POSIXProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: LibrariesTopic :: System :: Distributed Computing |
Evidence: vineyard-0.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; vineyard-0.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; vineyard-0.24.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; vineyard-0.24.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; vineyard-0.24.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; vineyard-0.24.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; vineyard-0.24.2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; vineyard-0.24.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; vineyard-0.24.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; vineyard-0.24.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; vineyard-0.24.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; vineyard-0.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; vineyard-0.24.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; vineyard-0.24.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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See also vineyard-bdist · mooncake-transfer-engine-cuda13 · dask · mooncake-transfer-engine · apache-flink-libraries · onnx-ir · apache-tvm-ffi · litdata · nvidia-nvshmem-cu12 · pyspark-pandas