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CITATION.cff
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CITATION.cff
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cff-version: 1.2.0
message: >-
If you use this software, please cite our paper using the
metadata from this file.
title: 'Vineyard: Optimizing Data Sharing in Data-Intensive Analytics'
authors:
- given-names: Wenyuan
family-names: Yu
affiliation: Alibaba Group
- given-names: Tao
family-names: He
affiliation: Alibaba Group
- given-names: Lei
family-names: Wang
affiliation: Alibaba Group
- given-names: Ke
family-names: Meng
affiliation: Alibaba Group
- given-names: Ye
family-names: Cao
affiliation: Alibaba Group
- given-names: Diwen
family-names: Zhu
affiliation: Alibaba Group
- given-names: Sanhong
family-names: Li
affiliation: Alibaba Group
- given-names: Jingren
family-names: Zhou
affiliation: Alibaba Group
license: Apache-2.0
identifiers:
- type: doi
value: 10.1145/3589780
repository-code: 'https://github.com/v6d-io/v6d'
url: 'https://v6d.io'
abstract: >-
Modern data analytics and AI jobs become increasingly complex and involve
multiple tasks performed on specialized systems. Sharing of intermediate
data between different systems is often a significant bottleneck in such
jobs. When the intermediate data is large, it is mostly exchanged through
files in standard formats (e.g., CSV and ORC), causing high I/O and
(de)serialization overheads. To solve these problems, we develop Vineyard,
a high-performance, extensible, and cloud-native object store, trying to
provide an intuitive experience for users to share data across systems in
complex real-life workflows. Since different systems usually work on data
structures (e.g., dataframes, graphs, hashmaps) with similar interfaces,
and their computation logic is often loosely-coupled with how such interfaces
are implemented over specific memory layouts, it enables Vineyard to conduct
data sharing efficiently at a high level via memory mapping and method sharing.
Vineyard provides an IDL named VCDL to facilitate users to register their
own intermediate data types into Vineyard such that objects of the registered
types can then be efficiently shared across systems in a polyglot workflow.
As a cloud-native system, Vineyard is designed to work closely with Kubernetes,
as well as achieve fault-tolerance and high performance in production
environments. Evaluations on real-life datasets and data analytics jobs show
that the above optimizations of Vineyard can significantly improve the end-to-end
performance of data analytics jobs, by reducing their data-sharing time up
to 68.4x.
preferred-citation:
type: article
title: 'Vineyard: Optimizing Data Sharing in Data-Intensive Analytics'
authors:
- given-names: Wenyuan
family-names: Yu
affiliation: Alibaba Group
- given-names: Tao
family-names: He
affiliation: Alibaba Group
- given-names: Lei
family-names: Wang
affiliation: Alibaba Group
- given-names: Ke
family-names: Meng
affiliation: Alibaba Group
- given-names: Ye
family-names: Cao
affiliation: Alibaba Group
- given-names: Diwen
family-names: Zhu
affiliation: Alibaba Group
- given-names: Sanhong
family-names: Li
affiliation: Alibaba Group
- given-names: Jingren
family-names: Zhou
affiliation: Alibaba Group
year: 2023
journal: "Proc. ACM Manag. Data"
doi: 10.1145/3589780
month: 06
volume: 1
number: 2
publisher:
name: Association for Computing Machinery
keywords:
- data sharing
in-memory object store