Hi Looks a nice project, very interested in checking more detail.
How about the performance? Onyx+spark in comparison to directly using spark, whether reduce performance ,or not ? Regards Liang Byung-Gon Chun wrote > Dear Apache Incubator Community, > > Please accept the following proposal for presentation and discussion: > https://wiki.apache.org/incubator/OnyxProposal > > 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 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 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 https://snuspl.github.io/onyx/. > > == Initial Source == > The Onyx codebase is currently hosted at https://github.com/snuspl/onyx. > > == 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: > private@.apache > * Onyx developers: > dev@.apache > > === Git Repositories === > Upon incubation: https://github.com/apache/incubator-onyx. > After the incubation, we would like to move the existing repo > https://github.com/snuspl/onyx to the Apache infrastructure > > === Issue Tracking === > Onyx currently tracks its issues using the Github issue tracker: > https://github.com/snuspl/onyx/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 > * Markus Weimer > * Reynold Xin > > === Sponsoring Entity === > The Apache Incubator > > > > -- > Byung-Gon Chun -- Sent from: http://apache-incubator-general.996316.n3.nabble.com/ --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
