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From Jean-Baptiste Onofré>
Subject Re: [VOTE] Accept Coral into the Apache Incubator
Date Fri, 02 Feb 2018 06:54:29 GMT
+1 (binding)


On 02/01/2018 03:07 PM, Byung-Gon Chun wrote:
> Hi all,
> I would like to start a VOTE to propose the Coral project as a podling into
> the Apache Incubator.
> The ASF voting rules are described at
> voting.html
> A vote for accepting a new Apache Incubator podling is a majority vote for
> which only Incubator PMC member votes are binding.
> This vote will run for at least 72 hours. Please VOTE as follows.
> [] +1 Accept Coral into the Apache Incubator
> [] +0 Abstain
> [] -1 Do not accept Coral into the Apache Incubator because ...
> The proposal is listed below, but you can also access it on the wiki:
> = CoralProposal =
> == Abstract ==
> Coral is a data processing system for flexible employment with
> different execution scenarios for various deployment characteristics
> on clusters.
> == Proposal ==
> Today, there is a wide variety of data processing systems with
> different designs for better performance and datacenter efficiency.
> They include processing data on specific resource environments and
> running jobs with specific attributes. Although each system
> successfully solves the problems it targets, most systems are designed
> in the way that runtime behaviors are built tightly inside the system
> core to hide the complexity of distributed computing. This makes it
> hard for a single system to support different deployment
> characteristics with different runtime behaviors without substantial
> effort.
> Coral is a data processing system that aims to flexibly control the
> runtime behaviors of a job to adapt to varying deployment
> characteristics. Moreover, it provides a means of extending the
> system’s capabilities and incorporating the extensions to the flexible
> job execution.
> In order to be able to easily modify runtime behaviors to adapt to
> varying deployment characteristics, Coral exposes runtime behaviors to
> be flexibly configured and modified at both compile-time and runtime
> through a set of high-level graph pass interfaces.
> We hope to contribute to the big data processing community by enabling
> more flexibility and extensibility in job executions. Furthermore, we
> can benefit more together as a community when we work together as a
> community to mature the system with more use cases and understanding
> of diverse deployment characteristics. The Apache Software Foundation
> is the perfect place to achieve these aspirations.
> == Background ==
> Many data processing systems have distinctive runtime behaviors
> optimized and configured for specific deployment characteristics like
> different resource environments and for handling special job
> attributes.
> For example, much research have been conducted to overcome the
> challenge of running data processing jobs on cheap, unreliable
> transient resources. Likewise, techniques for disaggregating different
> types of resources, like memory, CPU and GPU, are being actively
> developed to use datacenter resources more efficiently. Many
> researchers are also working to run data processing jobs in even more
> diverse environments, such as across distant datacenters. Similarly,
> for special job attributes, many works take different approaches, such
> as runtime optimization, to solve problems like data skew, and to
> optimize systems for data processing jobs with small-scale input data.
> Although each of the systems performs well with the jobs and in the
> environments they target, they perform poorly with unconsidered cases,
> and do not consider supporting multiple deployment characteristics on
> a single system in their designs.
> For an application writer to optimize an application to perform well
> on a certain system engraved with its underlying behaviors, it
> requires a deep understanding of the system itself, which is an
> overhead that often requires a lot of time and effort. Moreover, for a
> developer to modify such system behaviors, it requires modifications
> of the system core, which requires an even deeper understanding of the
> system itself.
> With this background, Coral is designed to represent all of its jobs
> as an Intermediate Representation (IR) DAG. In the Coral compiler,
> user applications from various programming models (ex. Apache Beam)
> are submitted, transformed to an IR DAG, and optimized/customized for
> the deployment characteristics. In the IR DAG optimization phase, the
> DAG is modified through a series of compiler “passes” which reshape or
> annotate the DAG with an expression of the underlying runtime
> behaviors. The IR DAG is then submitted as an execution plan for the
> Coral runtime. The runtime includes the unmodified parts of data
> processing in the backbone which is transparently integrated with
> configurable components exposed for further extension.
> == Rationale ==
> Coral’s vision lies in providing means for flexibly supporting a wide
> variety of job execution scenarios for users while facilitating system
> developers to extend the execution framework with various
> functionalities at the same time. The capabilities of the system can
> be extended as it grows to meet a more variety of execution scenarios.
> We require inputs from users and developers from diverse domains in
> order to make it a more thriving and useful project. The Apache
> Software Foundation provides the best tools and community to support
> this vision.
> == Initial Goals ==
> Initial goals will be to move the existing codebase to Apache and
> integrate with the Apache development process. We further plan to
> develop our system to meet the needs for more execution scenarios for
> a more variety of deployment characteristics.
> == Current Status ==
> Coral codebase is currently hosted in a repository at The
> current version has been developed by system developers at Seoul
> National University, Viva Republica, Samsung, and LG.
> == Meritocracy ==
> We plan to strongly support meritocracy. We will discuss the
> requirements in an open forum, and those that continuously contribute
> to Coral with the passion to strengthen the system will be invited as
> committers. Contributors that enrich Coral by providing various use
> cases, various implementations of the configurable components
> including ideas for optimization techniques will be especially
> welcome. Committers with a deep understanding of the system’s
> technical aspects as a whole and its philosophy will definitely be
> voted as the PMC. We will monitor community participation so that
> privileges can be extended to those that contribute.
> == Community ==
> We hope to expand our contribution community by becoming an Apache
> incubator project. The contributions will come from both users and
> system developers interested in flexibility and extensibility of job
> executions that Coral can support. We expect users to mainly
> contribute to diversify the use cases and deployment characteristics,
> and developers to  contribute to implement them.
> == Alignment ==
> Apache Spark is one of many popular data processing frameworks. The
> system is designed towards optimizing jobs using RDDs in memory and
> many other optimizations built tightly within the framework. In
> contrast to Spark, Coral aims to provide more flexibility for job
> execution in an easy manner.
> Apache Tez enables developers to build complex task DAGs with control
> over the control plane of job execution. In Coral, a high-level
> programming layer (ex. Apache Beam) is automatically converted to a
> basic IR DAG and can be converted to any IR DAG through a series of
> easy user writable passes, that can both reshape and modify the
> annotation (of execution properties) of the DAG. Moreover, Coral
> leaves more parts of the job execution configurable, such as the
> scheduler and the data plane. As opposed to providing a set of
> properties for solid optimization, Coral’s configurable parts can be
> easily extended and explored by implementing the pre-defined
> interfaces. For example, an arbitrary intermediate data store can be
> added.
> Coral currently supports Apache Beam programs and we are working on
> supporting Apache Spark programs as well. Coral also utilizes Apache
> REEF for container management, which allows Coral to run in Apache
> YARN and Apache Mesos clusters. If necessary, we plan to contribute to
> and collaborate with these other Apache projects for the benefit of
> all. We plan to extend such integrations with more Apache softwares.
> Apache software foundation already hosts many major big-data systems,
> and we expect to help further growth of the big-data community by
> having Coral within the Apache foundation.
> == Known Risks ==
> === Orphaned Products ===
> The risk of the Coral project being orphaned is minimal. There is
> already plenty of work that arduously support different deployment
> characteristics, and we propose a general way to implement them with
> flexible and extensible configuration knobs. The domain of data
> processing is already of high interest, and this domain is expected to
> evolve continuously with various other purposes, such as resource
> disaggregation and using transient resources for better datacenter
> resource utilization.
> === Inexperience with Open Source ===
> The initial committers include PMC members and committers of other
> Apache projects. They have experience with open source projects,
> starting from their incubation to the top-level. They have been
> involved in the open source development process, and are familiar with
> releasing code under an open source license.
> === Homogeneous Developers ===
> The initial set of committers is from a limited set of organizations,
> but we expect to attract new contributors from diverse organizations
> and will thus grow organically once approved for incubation. Our prior
> experience with other open source projects will help various
> contributors to actively participate in our project.
> === Reliance on Salaried Developers ===
> Many developers are from Seoul National University. This is not applicable.
> === Relationships with Other Apache Products ===
> Coral positions itself among multiple Apache products. It runs on
> Apache REEF for container management. It also utilizes many useful
> development tools including Apache Maven, Apache Log4J, and multiple
> Apache Commons components. Coral supports the Apache Beam programming
> model for user applications. We are currently working on supporting
> the Apache Spark programming APIs as well.
> === An Excessive Fascination with the Apache Brand ===
> We hope to make Coral a powerful system for data processing, meeting
> various needs for different deployment characteristics, under a more
> variety of environments. We see the limitations of simply putting code
> on GitHub, and we believe the Apache community will help the growth of
> Coral for the project to become a positively impactful and innovative
> open source software. We believe Coral is a great fit for the Apache
> Software Foundation due to the collaboration it aims to achieve from
> the big data processing community.
> == Documentation ==
> The current documentation for Coral is at
> == Initial Source ==
> The Coral codebase is currently hosted at
> == External Dependencies ==
> To the best of our knowledge, all Coral dependencies are distributed
> under Apache compatible licenses. Upon acceptance to the incubator, we
> would begin a thorough analysis of all transitive dependencies to
> verify this fact and further introduce license checking into the build
> and release process.
> == Cryptography ==
> Not applicable.
> == Required Resources ==
> === Mailing Lists ===
> We will operate two mailing lists as follows:
>    * Coral PMC discussions:
>    * Coral developers:
> === Git Repositories ===
> Upon incubation:
> After the incubation, we would like to move the existing repo
> to the Apache infrastructure
> === Issue Tracking ===
> Coral currently tracks its issues using the Github issue tracker:
> We plan to migrate to Apache
> == Initial Committers ==
>   * Byung-Gon Chun
>   * Jeongyoon Eo
>   * Geon-Woo Kim
>   * Joo Yeon Kim
>   * Gyewon Lee
>   * Jung-Gil Lee
>   * Sanha Lee
>   * Wooyeon Lee
>   * Yunseong Lee
>   * JangHo Seo
>   * Won Wook Song
>   * Taegeon Um
>   * Youngseok Yang
> == Affiliations ==
>   * SNU (Seoul National University)
>     * Byung-Gon Chun
>     * Jeongyoon Eo
>     * Geon-Woo Kim
>     * Gyewon Lee
>     * Sanha Lee
>     * Wooyeon Lee
>     * Yunseong Lee
>     * JangHo Seo
>     * Won Wook Song
>     * Taegeon Um
>     * Youngseok Yang
>   * LG
>     * Jung-Gil Lee
>   * Samsung
>     * Joo Yeon Kim
>   * Viva Republica
>     * Geon-Woo Kim
> == Sponsors ==
> === Champions ===
> Byung-Gon Chun
> === Mentors ===
>   * Hyunsik Choi
>   * Byung-Gon Chun
>   * Jean-Baptiste Onofré
>   * Markus Weimer
>   * Reynold Xin
> === Sponsoring Entity ===
> The Apache Incubator
> Thanks!
> Byung-Gon Chun

Jean-Baptiste Onofré
Talend -

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