+1 (binding) Also, happy to help, mentor, or be a connection with the Beam PMC, as appropriate.
On Thu, Feb 1, 2018 at 9:54 AM, Kevin A. McGrail <kmcgr...@apache.org> wrote: > +1 Binding > > > On 2/1/2018 9:07 AM, 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 https://www.apache.org/foundation/ >> 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: >> https://wiki.apache.org/incubator/CoralProposal >> >> = 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 github.com. 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 https://snuspl.github.io/coral/ >> . >> >> == Initial Source == >> The Coral codebase is currently hosted at https://github.com/snuspl/coral >> . >> >> == 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: priv...@coral.incubator.apache.org >> * Coral developers: d...@coral.incubator.apache.org >> >> === Git Repositories === >> Upon incubation: https://github.com/apache/incubator-coral. >> After the incubation, we would like to move the existing repo >> https://github.com/snuspl/coral to the Apache infrastructure >> >> === Issue Tracking === >> Coral currently tracks its issues using the Github issue tracker: >> https://github.com/snuspl/coral/issues. We plan to migrate to Apache >> JIRA. >> >> == 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 >> >> > -- > Kevin A. McGrail > Asst. Treasurer & VP Fundraising, Apache Software Foundation > Chair Emeritus Apache SpamAssassin Project > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org > For additional commands, e-mail: general-h...@incubator.apache.org > >
