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From Jean-Baptiste Onofré>
Subject Re: [PROPOSAL] Onyx - proposal for Apache Incubation
Date Tue, 30 Jan 2018 08:52:31 GMT

sorry to be a little bit late on this.

It's a very interesting proposal. It sounds pretty close to the portability
layer we want to add in Apache Beam. I would love to see interaction between the
two communities.

I have two minor questions:

1. about the name: Onyx sounds very generic and the name is used in other
technologies. Maybe another unique name would be more accurate.
2. the Onyx code is on github right now, under the Apache 2.0 license. Does this
code has any affiliation with companies ? Meaning that we would need a SGA for
the code donation.

If you need any help for the incubation, I would be more than happy to help !


On 01/26/2018 12:28 AM, Byung-Gon Chun wrote:
> Dear Apache Incubator Community,
> Please accept the following proposal for presentation and discussion:
> Onyx is a data processing system that aims to flexibly control the runtime
> behaviors of a job to adapt to varying deployment characteristics (e.g.,
> harnessing transient resources in datacenters, cross-datacenter deployment,
> changing runtime based on job characteristics, etc.). Onyx provides ways to
> extend the system’s capabilities and incorporate the extensions to the
> flexible job execution.
> Onyx translates a user program (e.g., Apache Beam, Apache Spark) into an
> Intermediate Representation (IR) DAG, which Onyx optimizes and deploys
> based on a deployment policy.
> I've attached the proposal below.
> Best regards,
> Byung-Gon Chun
> = OnyxProposal =
> == Abstract ==
> Onyx 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.
> Onyx 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, Onyx 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, Onyx is designed to represent all of its jobs as
> an Intermediate Representation (IR) DAG. In the Onyx 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
> Onyx 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 ==
> Onyx’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 ==
> Onyx 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 Onyx with the passion to strengthen the system will be invited as
> committers. Contributors that enrich Onyx 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 Onyx 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, Onyx 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 Onyx, 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, Onyx 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, Onyx’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.
> Onyx currently supports Apache Beam programs and we are working on
> supporting Apache Spark programs as well. Onyx also utilizes Apache
> REEF for container management, which allows Onyx 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 Onyx
> within the Apache foundation.
> == Known Risks ==
> === Orphaned Products ===
> The risk of the Onyx 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 ===
> Onyx 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. Onyx 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 Onyx 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
> Onyx for the project to become a positively impactful and innovative
> open source software. We believe Onyx 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 Onyx is at
> == Initial Source ==
> The Onyx codebase is currently hosted at
> == External Dependencies ==
> To the best of our knowledge, all Onyx 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:
>    * Onyx PMC discussions:
>    * Onyx developers:
> === Git Repositories ===
> Upon incubation:
> After the incubation, we would like to move the existing repo
> to the Apache infrastructure
> === Issue Tracking ===
> Onyx 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
>   * Markus Weimer
>   * Reynold Xin
> === Sponsoring Entity ===
> The Apache Incubator

Jean-Baptiste Onofré
Talend -

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