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v6d-io / v6d

v6d

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

Data & DatabasesSystems & InfrastructureDevOps & Cloud
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2Distinct people
8Mentions
0Explicit recommenders
968GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

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

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:01:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month8 dated mentions

Observed mentions: 2021-01-19 12:30:11 UTC to 2022-08-25 07:04:02 UTC.

01232021-01-01 00:00:00 UTC to 2021-02-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsJan 20212021-02-01 00:00:00 UTC to 2021-03-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2021-03-01 00:00:00 UTC to 2021-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-04-01 00:00:00 UTC to 2021-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-05-01 00:00:00 UTC to 2021-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-06-01 00:00:00 UTC to 2021-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-07-01 00:00:00 UTC to 2021-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-08-01 00:00:00 UTC to 2021-09-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2021-09-01 00:00:00 UTC to 2021-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-10-01 00:00:00 UTC to 2021-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 20212021-11-01 00:00:00 UTC to 2021-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-12-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2022-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-02-01 00:00:00 UTC to 2022-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-03-01 00:00:00 UTC to 2022-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-04-01 00:00:00 UTC to 2022-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-05-01 00:00:00 UTC to 2022-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-06-01 00:00:00 UTC to 2022-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-07-01 00:00:00 UTC to 2022-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-08-01 00:00:00 UTC to 2022-09-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsAug 2022

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jan 20212021-01-01 00:00:00 UTC2021-02-01 00:00:00 UTC211
Feb 20212021-02-01 00:00:00 UTC2021-03-01 00:00:00 UTC303
Mar 20212021-03-01 00:00:00 UTC2021-04-01 00:00:00 UTC000
Apr 20212021-04-01 00:00:00 UTC2021-05-01 00:00:00 UTC000
May 20212021-05-01 00:00:00 UTC2021-06-01 00:00:00 UTC000
Jun 20212021-06-01 00:00:00 UTC2021-07-01 00:00:00 UTC000
Jul 20212021-07-01 00:00:00 UTC2021-08-01 00:00:00 UTC000
Aug 20212021-08-01 00:00:00 UTC2021-09-01 00:00:00 UTC110
Sep 20212021-09-01 00:00:00 UTC2021-10-01 00:00:00 UTC000
Oct 20212021-10-01 00:00:00 UTC2021-11-01 00:00:00 UTC000
Nov 20212021-11-01 00:00:00 UTC2021-12-01 00:00:00 UTC000
Dec 20212021-12-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
Jan 20222022-01-01 00:00:00 UTC2022-02-01 00:00:00 UTC000
Feb 20222022-02-01 00:00:00 UTC2022-03-01 00:00:00 UTC000
Mar 20222022-03-01 00:00:00 UTC2022-04-01 00:00:00 UTC000
Apr 20222022-04-01 00:00:00 UTC2022-05-01 00:00:00 UTC000
May 20222022-05-01 00:00:00 UTC2022-06-01 00:00:00 UTC000
Jun 20222022-06-01 00:00:00 UTC2022-07-01 00:00:00 UTC000
Jul 20222022-07-01 00:00:00 UTC2022-08-01 00:00:00 UTC000
Aug 20222022-08-01 00:00:00 UTC2022-09-01 00:00:00 UTC211

What Hacker News says

3 sources
sighingnow·Feb 4, 2021
Thanks for you interests on GraphScope! We do have a concrete plan for k8s-less deployment and we already have an issue [1] to track that. That will be available before the end of March 2021. To simplify the environment setup process we will release a docker image for end-users, but without docker will be ok as well (requires building from sources). GraphScope use vineyard [2] as the storage layer for im-memory graph data structures. And current the graph type (aka. ArrowPropertyFragment in GraphScope) uses a set of arrow tables and arrays under the hood. GraphScope supports a `to_vineyard_dataframe` method on the computation context [3]. We also has a plan for integration between vineyard and dask (may could be delivered in March as well). At that time the interop between dask would be straightforward. [1]: https://github.com/alibaba/Gra
Read the rest
phScope/discussions/113 [2]: https://github.com/alibaba/libvineyard [3]: https://graphscope.io/docs/reference/context.html#graphscope...
In GraphScope: A One-Stop Large-Scale Graph Computing System
wenyuanyu·Feb 3, 2021
alibaba/euler is a lightweight library built specifically for GNN sampling only. While GraphScope is built for many kinds of graph tasks such as gremlin, graph analytics and GNN sampling. You can check this example to get an idea what GraphScope can do. https://nbviewer.jupyter.org/github/alibaba/GraphScope/blob/... The graphs on GraphScope is backed by vineyard (https://github.com/alibaba/libvineyard). And that enables GraphScope to have multiple specifically optimized runtimes (written in C++, rust and Python) for different tasks shares the distributed graph data in memory efficiently.
In GraphScope: A One-Stop Large-Scale Graph Computing System
wenyuanyu·Feb 3, 2021
> if you have a small graph would it unreasonable to use graphscope to analyze it - what if it was a small graph that you expected to grow big later? You are right. GraphScope is built for processing very large graphs, but for small graphs, it should work just as well. If you expect your graphs to grow too big to be handled on a single machine in the future, I think it is a good idea to start with GraphScope even when it is small. Stay tuned, we got plans to make GraphScope nicer to use for smaller graphs without a k8s cluster. > What if you have a graph inside a graph db - I guess GraphScope gives you extra performance by allowing you to distribute processing for tasks that would make sense - any other benefits like that? Besides extra performance, there could a few other benefits depending on your scenario. 1. Many graph analytical / GN
Read the rest
N tasks takes a lot of computing resources. A single task can take hours to run even with 1000 CPU cores. It makes sense to run such tasks in other machines/systems without adding too much burden to a graph db to avoid affect its quality of service. 2. Fully integration with Python makes it more flexible to do data analytics. For example, you can leverage the ability provided by numpy, pandas and mars (https://github.com/mars-project/mars) along GraphScope with zero-copy thanks to our storage engine vineyard (https://github.com/alibaba/libvineyard) 3. Besides distributed processing, extra performance can also come from the efficient graph layout in memory, and other optimizations on the compiler and runtime-level. GraphScope is ~100x faster on Gremlin, and even more on graph analytical algorithms like PageRank, compared with graph dbs like JanusGraph.
In GraphScope: A One-Stop Large-Scale Graph Computing System

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