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https://github.com/alibaba/v6d

vineyard (v6d): an in-memory immutable data manager. (Project under CNCF, TAG-Storage)
https://github.com/alibaba/v6d

big-data-analytics cloud-native cncf distributed distributed-comp distributed-systems graph-analytics in-memory-storage shared-memory sig-storage tag-storage

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vineyard (v6d): an in-memory immutable data manager. (Project under CNCF, TAG-Storage)

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        vineyard

    


    


        an in-memory immutable data manager

    



|Vineyard CI| |Coverage| |Docs| |FAQ| |Discussion| |Slack| |License| |CII Best Practices| |FOSSA|



|PyPI| |crates.io| |Docker HUB| |Artifact HUB| |ACM DL|



Vineyard (v6d) is an innovative in-memory immutable data manager that offers **out-of-the-box

high-level** abstractions and **zero-copy in-memory** sharing for distributed data in various

big data tasks, such as graph analytics (e.g., `GraphScope`_), numerical computing

(e.g., `Mars`_), and machine learning.



.. image:: https://v6d.io/_static/cncf-color.svg

  :width: 400

  :alt: Vineyard is a CNCF sandbox project



Vineyard is a `CNCF sandbox project`_ and indeed made successful by its community.



Table of Contents

-----------------



* `Overview <#what-is-vineyard>`_

* `Features of vineyard <#features>`_



  * `Efficient sharing for in-memory immutable data <#in-memory-immutable-data-sharing>`_

  * `Out-of-the-box high level data structures <#out-of-the-box-high-level-data-abstraction>`_

  * `Pipelining using stream <#stream-pipelining>`_

  * `I/O Drivers <#drivers>`_



* `Getting started with Vineyard <#try-vineyard>`_

* `Deploying on Kubernetes <#deploying-on-kubernetes>`_

* `Frequently asked questions <#faq>`_

* `Getting involved in our community <#getting-involved>`_

* `Third-party dependencies <#acknowledgements>`_



What is vineyard

----------------



Vineyard is specifically designed to facilitate zero-copy data sharing among big data systems. To

illustrate this, let's consider a typical machine learning task of `time series prediction with LSTM`_.

This task can be broken down into several steps:



- First, we read the data from the file system as a ``pandas.DataFrame``.

- Next, we apply various preprocessing tasks, such as eliminating null values, to the dataframe.

- Once the data is preprocessed, we define the model and train it on the processed dataframe using PyTorch.

- Finally, we evaluate the performance of the model.



In a single-machine environment, pandas and PyTorch, despite being two distinct systems designed for

different tasks, can efficiently share data with minimal overhead. This is achieved through an

end-to-end process within a single Python script.



.. image:: https://v6d.io/_static/vineyard_compare.png

   :alt: Comparing the workflow with and without vineyard



What if the input data is too large to be processed on a single machine?



   As depicted on the left side of the figure, a common approach is to store the data as tables in

   a distributed file system (e.g., HDFS) and replace ``pandas`` with ETL processes using SQL over a

   big data system such as Hive and Spark. To share the data with PyTorch, the intermediate results are

   typically saved back as tables on HDFS. However, this can introduce challenges for developers.



1. For the same task, users must program for multiple systems (SQL & Python).



2. Data can be polymorphic. Non-relational data, such as tensors, dataframes, and graphs/networks

   (in `GraphScope`_) are becoming increasingly common. Tables and SQL may not be the most efficient

   way to store, exchange, or process them. Transforming the data from/to "tables" between different

   systems can result in significant overhead.



3. Saving/loading the data to/from external storage

   incurs substantial memory-copies and IO costs.



Vineyard addresses these issues by providing:



1. **In-memory** distributed data sharing in a **zero-copy** fashion to avoid

   introducing additional I/O costs by leveraging a shared memory manager derived from plasma.



2. Built-in **out-of-the-box high-level** abstractions to share distributed

   data with complex structures (e.g., distributed graphs)

   with minimal extra development cost, while eliminating transformation costs.



As depicted on the right side of the above figure, we demonstrate how to integrate

vineyard to address the task in a big data context.



First, we utilize `Mars`_ (a tensor-based unified framework for large-scale data

computation that scales Numpy, Pandas, and Scikit-learn) to preprocess the raw data,

similar to the single-machine solution, and store the preprocessed dataframe in vineyard.



+-------------+-----------------------------------------------------------------------------+

|             | .. code-block:: python                                                      |

| single      |                                                                             |

|             |     data_csv = pd.read_csv('./data.csv', usecols=[1])                       |

+-------------+-----------------------------------------------------------------------------+

|             | .. code-block:: python                                                      |

|             |                                                                             |

|             |     import mars.dataframe as md                                             |

| distributed |     dataset = md.read_csv('hdfs://server/data_full', usecols=[1])           |

|             |     # after preprocessing, save the dataset to vineyard                     |

|             |     vineyard_distributed_tensor_id = dataset.to_vineyard()                  |

+-------------+-----------------------------------------------------------------------------+



Then, we modify the

training phase to get the preprocessed data from vineyard. Here vineyard makes

the sharing of distributed data between `Mars`_ and PyTorch just like a local

variable in the single machine solution.



+-------------+-----------------------------------------------------------------------------+

|             | .. code-block:: python                                                      |

| single      |                                                                             |

|             |     data_X, data_Y = create_dataset(dataset)                                |

+-------------+-----------------------------------------------------------------------------+

|             | .. code-block:: python                                                      |

|             |                                                                             |

|             |     client = vineyard.connect(vineyard_ipc_socket)                          |

| distributed |     dataset = client.get(vineyard_distributed_tensor_id).local_partition()  |

|             |     data_X, data_Y = create_dataset(dataset)                                |

+-------------+-----------------------------------------------------------------------------+



Finally, we execute the training phase in a distributed manner across the cluster.



From this example, it is evident that with vineyard, the task in the big data context can

be addressed with only minor adjustments to the single-machine solution. Compared to

existing approaches, vineyard effectively eliminates I/O and transformation overheads.



Features

--------



Efficient In-Memory Immutable Data Sharing

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



Vineyard serves as an in-memory immutable data manager, enabling efficient data

sharing across different systems via shared memory without additional overheads.

By eliminating serialization/deserialization and IO costs during data exchange

between systems, Vineyard significantly improves performance.



Out-of-the-Box High-Level Data Abstractions

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



Computation frameworks often have their own data abstractions for high-level concepts.

For example, tensors can be represented as `torch.tensor`, `tf.Tensor`, `mxnet.ndarray`, etc.

Moreover, every `graph processing engine `_

has its unique graph structure representation.



The diversity of data abstractions complicates data sharing. Vineyard addresses this

issue by providing out-of-the-box high-level data abstractions over in-memory blobs,

using hierarchical metadata to describe objects. Various computation systems can

leverage these built-in high-level data abstractions to exchange data with other systems

in a computation pipeline concisely and efficiently.



Stream Pipelining for Enhanced Performance

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



A computation doesn't need to wait for all preceding results to arrive before starting

its work. Vineyard provides a stream as a special kind of immutable data for pipelining

scenarios. The preceding job can write immutable data chunk by chunk to Vineyard while

maintaining data structure semantics. The successor job reads shared-memory chunks from

Vineyard's stream without extra copy costs and triggers its work. This overlapping

reduces the overall processing time and memory consumption.



Versatile Drivers for Common Tasks

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



Many big data analytical tasks involve numerous boilerplate routines that are unrelated

to the computation itself, such as various IO adapters, data partition strategies, and

migration jobs. Since data structure abstractions usually differ between systems, these

routines cannot be easily reused.



Vineyard provides common manipulation routines for immutable data as drivers.

In addition to sharing high-level data abstractions, Vineyard extends the capability

of data structures with drivers, enabling out-of-the-box reusable routines for the

boilerplate parts in computation jobs.



Try Vineyard

------------



Vineyard is available as a `python package`_ and can be effortlessly installed using ``pip``:



.. code:: shell



   pip3 install vineyard



For comprehensive and up-to-date documentation, please visit https://v6d.io.



If you wish to build vineyard from source, please consult the `Installation`_ guide. For

instructions on building and running unittests locally, refer to the `Contributing`_ section.



After installation, you can initiate a vineyard instance using the following command:



.. code:: shell



   python3 -m vineyard



For further details on connecting to a locally deployed vineyard instance, please

explore the `Getting Started`_ guide.



Deploying on Kubernetes

-----------------------



Vineyard is designed to efficiently share immutable data between different workloads,

making it a natural fit for cloud-native computing. By embracing cloud-native big data

processing and Kubernetes, Vineyard enables efficient distributed data sharing in

cloud-native environments while leveraging the scaling and scheduling capabilities

of Kubernetes.



To effectively manage all components of Vineyard within a Kubernetes cluster, we have

developed the Vineyard Operator. For more information, please refer to the `Vineyard

Operator`_ documentation.



FAQ

---



Vineyard shares many similarities with other open-source projects, yet it also has

distinct features. We often receive the following questions about Vineyard:



* Q: Can clients access the data while the stream is being filled?



  Sharing one piece of data among multiple clients is a target scenario for Vineyard,

  as the data stored in Vineyard is *immutable*. Multiple clients can safely consume

  the same piece of data through memory sharing, without incurring extra costs or

  additional memory usage from copying data back and forth.



* Q: How does Vineyard avoid serialization/deserialization between systems in different

  languages?



  Vineyard provides high-level data abstractions (e.g., ndarrays, dataframes) that can

  be naturally shared between different processes, eliminating the need for serialization

  and deserialization between systems in different languages.



* . . . . . .



For more detailed information, please refer to our `FAQ`_ page.



Get Involved

------------



- Join the `CNCF Slack`_ and participate in the ``#vineyard`` channel for discussions

  and collaboration.

- Familiarize yourself with our `contribution guide`_ to understand the process of

  contributing to vineyard.

- If you encounter any bugs or issues, please report them by submitting a `GitHub

  issue`_ or engage in a conversation on `Github discussion`_.

- We welcome and appreciate your contributions! Submit them using pull requests.



Thank you in advance for your valuable contributions to vineyard!



Publications

------------



- Wenyuan Yu, Tao He, Lei Wang, Ke Meng, Ye Cao, Diwen Zhu, Sanhong Li, Jingren Zhou.

  `Vineyard: Optimizing Data Sharing in Data-Intensive Analytics `_.

  ACM SIG Conference on Management of Data (SIGMOD), industry, 2023. |ACM DL|.



If you use this software, please cite our paper using the following metadata:



.. code:: bibtex



   @article{yu2023vineyard,

      author = {Yu, Wenyuan and He, Tao and Wang, Lei and Meng, Ke and Cao, Ye and Zhu, Diwen and Li, Sanhong and Zhou, Jingren},

      title = {Vineyard: Optimizing Data Sharing in Data-Intensive Analytics},

      year = {2023},

      issue_date = {June 2023},

      publisher = {Association for Computing Machinery},

      address = {New York, NY, USA},

      volume = {1},

      number = {2},

      url = {https://doi.org/10.1145/3589780},

      doi = {10.1145/3589780},

      journal = {Proc. ACM Manag. Data},

      month = {jun},

      articleno = {200},

      numpages = {27},

      keywords = {data sharing, in-memory object store}

   }



Acknowledgements

----------------



We thank the following excellent open-source projects:



- `apache-arrow `_, a cross-language development platform for in-memory analytics.

- `boost-leaf `_, a C++ lightweight error augmentation framework.

- `cityhash `_, CityHash, a family of hash functions for strings.

- `dlmalloc `_, Doug Lea's memory allocator.

- `etcd-cpp-apiv3 `_, a C++ API for etcd's v3 client API.

- `flat_hash_map `_, an efficient hashmap implementation.

- `gulrak/filesystem `_, an implementation of C++17 std::filesystem.

- `libcuckoo `_, libcuckoo, a high-performance, concurrent hash table.

- `mimalloc `_, a general purpose allocator with excellent performance characteristics.

- `nlohmann/json `_, a json library for modern c++.

- `pybind11 `_, a library for seamless operability between C++11 and Python.

- `s3fs `_, a library provide a convenient Python filesystem interface for S3.

- `skywalking-infra-e2e `_ A generation End-to-End Testing framework.

- `skywalking-swck `_ A kubernetes operator for the Apache Skywalking.

- `wyhash `_, C++ wrapper around wyhash and wyrand.

- `BBHash `_, a fast, minimal-memory perfect hash function.

- `rax `_, an ANSI C radix tree implementation.

- `MurmurHash3 `_, a fast non-cryptographic hash function.



License

-------



**Vineyard** is distributed under `Apache License 2.0`_. Please note that

third-party libraries may not have the same license as vineyard.



|FOSSA Status|



.. _Mars: https://github.com/mars-project/mars

.. _GraphScope: https://github.com/alibaba/GraphScope

.. _Installation: https://github.com/v6d-io/v6d/blob/main/docs/notes/developers/build-from-source.rst

.. _Contributing: https://github.com/v6d-io/v6d/blob/main/CONTRIBUTING.rst

.. _Getting Started: https://v6d.io/notes/getting-started.html

.. _Vineyard Operator: https://v6d.io/notes/cloud-native/vineyard-operator.html

.. _Apache License 2.0: https://github.com/v6d-io/v6d/blob/main/LICENSE

.. _contribution guide: https://github.com/v6d-io/v6d/blob/main/CONTRIBUTING.rst

.. _time series prediction with LSTM: https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch/blob/master/chapter5_RNN/time-series/lstm-time-series.ipynb

.. _python package: https://pypi.org/project/vineyard/

.. _CNCF Slack: https://slack.cncf.io/

.. _GitHub issue: https://github.com/v6d-io/v6d/issues/new

.. _Github discussion: https://github.com/v6d-io/v6d/discussions/new

.. _FAQ: https://v6d.io/notes/faq.html

.. _CNCF sandbox project: https://www.cncf.io/sandbox-projects/



.. |Vineyard CI| image:: https://github.com/v6d-io/v6d/actions/workflows/build-test.yml/badge.svg

   :target: https://github.com/v6d-io/v6d/actions/workflows/build-test.yml

.. |Coverage| image:: https://codecov.io/gh/v6d-io/v6d/branch/main/graph/badge.svg

   :target: https://codecov.io/gh/v6d-io/v6d

.. |Docs| image:: https://img.shields.io/badge/docs-latest-brightgreen.svg

   :target: https://v6d.io

.. |FAQ| image:: https://img.shields.io/badge/-FAQ-blue?logo=Read%20The%20Docs

   :target: https://v6d.io/notes/faq.html

.. |Discussion| image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue?logo=GitHub

   :target: https://github.com/v6d-io/v6d/discussions

.. |Slack| image:: https://img.shields.io/badge/Slack-Join%20%23vineyard-purple?logo=Slack

   :target: https://slack.cncf.io/

.. |PyPI| image:: https://img.shields.io/pypi/v/vineyard?color=blue

   :target: https://pypi.org/project/vineyard

.. |crates.io| image:: https://img.shields.io/crates/v/vineyard.svg

   :target: https://crates.io/crates/vineyard

.. |Docker HUB| image:: https://img.shields.io/badge/docker-ready-blue.svg

   :target: https://hub.docker.com/u/vineyardcloudnative

.. |Artifact HUB| image:: https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/vineyard

   :target: https://artifacthub.io/packages/helm/vineyard/vineyard

.. |CII Best Practices| image:: https://bestpractices.coreinfrastructure.org/projects/4902/badge

   :target: https://bestpractices.coreinfrastructure.org/projects/4902

.. |FOSSA| image:: https://app.fossa.com/api/projects/git%2Bgithub.com%2Fv6d-io%2Fv6d.svg?type=shield

   :target: https://app.fossa.com/projects/git%2Bgithub.com%2Fv6d-io%2Fv6d?ref=badge_shield

.. |FOSSA Status| image:: https://app.fossa.com/api/projects/git%2Bgithub.com%2Fv6d-io%2Fv6d.svg?type=large

   :target: https://app.fossa.com/projects/git%2Bgithub.com%2Fv6d-io%2Fv6d?ref=badge_large

.. |License| image:: https://img.shields.io/github/license/v6d-io/v6d

   :target: https://github.com/v6d-io/v6d/blob/main/LICENSE



.. |ACM DL| image:: https://img.shields.io/badge/ACM%20DL-10.1145%2F3589780-blue

   :target: https://dl.acm.org/doi/10.1145/3589780