+1 (binding)
Good luck!

On Tue, May 24, 2022 at 8:49 PM Ye Xianjin <advance...@gmail.com> wrote:

> +1 (non-binding).
>
> Sent from my iPhone
>
> > On May 25, 2022, at 9:59 AM, Goson zhang <gosonzh...@apache.org> wrote:
> >
> > +1 (non-binding)
> >
> > Good luck!
> >
> > Daniel Widdis <wid...@gmail.com> 于2022年5月25日周三 09:53写道:
> >
> >> +1 (non-binding) from me!  Good luck!
> >>
> >> On 5/24/22, 9:05 AM, "Jerry Shao" <js...@apache.org> wrote:
> >>
> >>    Hi all,
> >>
> >>    Due to the name issue in thread (
> >>    https://lists.apache.org/thread/y07xjkqzvpchncym9zr1hgm3c4l4ql0f),
> we
> >>    figured out a new project name "Uniffle" and created a new Thread.
> >> Please
> >>    help to discuss.
> >>
> >>    We would like to propose Uniffle[1] as a new Apache incubator
> project,
> >> you
> >>    can find the proposal here [2] for more details.
> >>
> >>    Uniffle is a high performance, general purpose Remote Shuffle Service
> >> for
> >>    distributed compute engines like Apache Spark
> >>    <https://spark.apache.org/>, Apache
> >>    Hadoop MapReduce <https://hadoop.apache.org/>, Apache Flink
> >>    <https://flink.apache.org/> and so on. We are aiming to make
> >> Firestorm a
> >>    universal shuffle service for distributed compute engines.
> >>
> >>    Shuffle is the key part for a distributed compute engine to exchange
> >> the
> >>    data between distributed tasks, the performance and stability of
> >> shuffle
> >>    will directly affect the whole job. Current “local file pull-like
> >> shuffle
> >>    style” has several limitations:
> >>
> >>       1. Current shuffle is hard to support super large workloads,
> >> especially
> >>       in a high load environment, the major problem is IO problem
> (random
> >> disk IO
> >>       issue, network congestion and timeout).
> >>       2. Current shuffle is hard to deploy on the disaggregated compute
> >>       storage environment, as disk capacity is quite limited on compute
> >> nodes.
> >>       3. The constraint of storing shuffle data locally makes it hard to
> >> scale
> >>       elastically.
> >>
> >>    Remote Shuffle Service is the key technology for enterprises to build
> >> big
> >>    data platforms, to expand big data applications to disaggregated,
> >>    online-offline hybrid environments, and to solve above problems.
> >>
> >>    The implementation of Remote Shuffle Service -  “Uniffle”  - is
> heavily
> >>    adopted in Tencent, and shows its advantages in production. Other
> >>    enterprises also adopted or prepared to adopt Firestorm in their
> >>    environments.
> >>
> >>    Uniffle's key idea is brought from Salfish shuffle
> >>    <
> >>
> https://www.researchgate.net/publication/262241541_Sailfish_a_framework_for_large_scale_data_processing
> >>> ,
> >>    it has several key design goals:
> >>
> >>       1. High performance. Firestorm’s performance is close enough to
> >> local
> >>       file based shuffle style for small workloads. For large workloads,
> >> it is
> >>       far better than the current shuffle style.
> >>       2. Fault tolerance. Firestorm provides high availability for
> >> Coordinated
> >>       nodes, and failover for Shuffle nodes.
> >>       3. Pluggable. Firestorm is highly pluggable, which could be suited
> >> to
> >>       different compute engines, different backend storages, and
> different
> >>       wire-protocols.
> >>
> >>    We believe that Uniffle project will provide the great value for the
> >>    community if it is accepted by the Apache incubator.
> >>
> >>    I will help this project as champion and many thanks to the 3
> mentors:
> >>
> >>       -
> >>
> >>       Felix Cheung (felixche...@apache.org)
> >>       - Junping du (junping...@apache.org)
> >>       - Weiwei Yang (w...@apache.org)
> >>       - Xun liu (liu...@apache.org)
> >>       - Zhankun Tang (zt...@apache.org)
> >>
> >>
> >>    [1] https://github.com/Tencent/Firestorm
> >>    [2]
> >> https://cwiki.apache.org/confluence/display/INCUBATOR/UniffleProposal
> >>
> >>    Best regards,
> >>    Jerry
> >>
> >>
> >>
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> >>
> >>
>
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