+1
On Fri, Sep 11, 2015 at 9:11 PM, Skip Intro <al...@2kb.net> wrote: > +1 (non-binding) On Wed, Sep 09, 2015 at 07:37PM, Roman Shaposhnik wrote: > > > Following the discussion earlier: > http://s.apache.org/TE6 > > I would > like to call a VOTE for accepting > MADlib community as a new ASF incubator > > project. > > The proposal is available at: > > https://wiki.apache.org/incubator/MADlibProposal > and is also included at > the bottom of this email. > > Vote is open until at least Mon, 14 September > 2015, 23:59:00 PST > > [ ] +1 accept MADlib into the Apache Incubator > [ ] > ±0 > [ ] -1 because... > > Thanks, > Roman. > > == Abstract == > MADlib is > an open-source library (licensed under 2-clause BSD license) > for scalable > in-database analytics. It provides data-parallel > implementations of > mathematical, statistical and machine learning > methods for structured and > unstructured data. The MADlib mission is to > foster widespread development > of scalable analytic skills, by > harnessing efforts from commercial > practice, academic research, and > open source development. > > MADlib > occupies a unique niche in the realm of data science and > machine learning > libraries since its SQL APIs can allow it to work on > a wide range of data > stores and SQL engines. > > == Proposal == > The current open source > community behind MADlib feels that aligning > itself with HAWQ's community, > governance model, infrastructure and > roadmap will allow the project to > accelerate adoption and community > growth. Given HAWQ's trajectory of > entering Apache Software Foundation > family as an Incubating project, we > feel that the best course of > action for MADlib is to follow a similar > route. > > MADlib and HAWQ are complementary technologies in that MADlib > > in-database analytical functions can run within the HAWQ execution > > engine. (MADlib also runs on Greenplum Database and PostgreSQL today.) > It > is expected that contributors to MADlib will be cognizant of the > HAWQ ASF > project and may contribute to it as well. In short, > collaboration between > the two communities will make both projects more > vibrant and advance the > respective technologies in potentially novel > directions. > > Contributors > may also look at the HAWQ project as a starting port for > ports to other > parallel database engines. This proposal highly > encourages this type of > work as it would help to further realize the > original cross-platform goal > of MADlib as envisioned by its > originators. > > Thus, the goal of this > proposal is to bring the existing MADlib open > source community into ASF, > change the project's governance model to > the "Apache Way" and transition > the project's codebase and > infrastructure into ASF INFRA. The community > has agreed to transfer > the brand name "MADlib" to Apache Software > Foundation as well. > > Pivotal Inc. on behalf of the MADlib open source > community is > submitting this proposal to transition source code and > associated > artifacts (documentation, web site content, wiki, etc.) to the > Apache > Software Foundation Incubator under the Apache License, Version > 2.0 > and is asking Incubator PMC to established a MADlib incubating > > project. > > Currently MADlib uses a few category X licensed software tools > during > its build (mostly for generating documentation): > * doxypy 0.4.2 > (GPL) > * doxygen 1.8.4 (GPL) > * TikZ-UML > * bison 2.4 (GPL, with an > exception for generated output) > We feel that this usage is compatible > with an overall project licensed > under the ALv2 and don't anticipate any > changes. > Our usage of LGPL library cern_root-5.34 is expected to go away > since > the 2 cern modules used are being entirely re-written > in MADlib > > > Finally, MADlib inclusion of MPL licensed library (eigen 3.2.2) into > > its binary artifact seems to be consistent with > ASF recommendation for > managing "weak copyleft" dependencies. > > > == Background == > MADlib grew > out of discussions between database engine developers, > data scientists, > IT architects and academics interested in new > approaches to scalable, > sophisticated in-database analytics. These > discussions were written up in > a paper in VLDB 2009 that coined the > term “MAD Skills” for data analysis > > (http://dl.acm.org/citation.cfm?id=1687576). The MADlib software > > project began the following year as a collaboration between > researchers > at UC Berkeley and engineers and data scientists at > Pivotal (former > EMC/Greenplum). > > The initial MADlib codebase came from EMC/Greenplum, UC > Berkeley, the > University of Wisconsin, and the University of Florida. The > project > was publicly documented in a paper at VLDB 2012 > ( > http://vldb.org/pvldb/vol5/p1700_joehellerstein_vldb2012.pdf). Today > > MADlib has contributors from around the world including both > individuals > and institutions. For example, recent contributions have > come from > Pivotal, Stanford University, and the University of Illinois > at Chicago. > > > MADlib was conceived from the outset as a free, open source library > > for all to use and contribute to. Since its inception, the community > has > steadily added new methods in the areas of mathematics, > statistics, > machine learning, and data transformation. The current > library includes > over 30 principle algorithms as well as many > additional operators and > utility functions. > > The methods in MADlib are designed both for in- or > out-of-core > execution, and for the shared-nothing, scale-out parallelism > offered > by modern parallel database engines, ensuring that computation is > done > close to the data. The core functionality is written in declarative > > SQL statements, which orchestrate data movement to and from disk, and > > across networked machines. Single-node inner loops take advantage of > SQL > extensibility to call out to high performance math libraries in > > user-defined scalar and aggregate functions. At the highest level, > tasks > that require iteration and/or structure definition are coded in > Python > driver routines, which are used only to kick off the data-rich > > computations that happen within the database engine. > > The first > platforms supported by MADlib were Greenplum Database and > PostgreSQL. > With the development of HAWQ SQL-on-Hadoop technology by > Pivotal, MADlib > offers a way to perform predictive analytics on very > large data sets > stored on a Hadoop cluster. > > Today, MADlib is in active development and > is deployed on a wide > variety of industry and academic projects across > many different > verticals. > > == Rationale == > Enterprises today are > seeing the value of landing very large > quantities of data in Hadoop > clusters with the goal improving their > products and processes. With the > proliferation of increasingly > sophisticated SQL-on-Hadoop technologies > such as HAWQ, analysts can > use the familiar SQL language to query this > data at scale. This > effectively opens the door to Hadoop in the > enterprise. > > Adding SQL-based predictive analytics like MADlib to the > equation > enables organizations to reason across large data sets without > > resorting to sampling, which has been a traditional approach when > > confronted with scale problems. Operating on all of the data with > MADlib > results in more robust and accurate models. > > Since MADlib is a SQL-based > interface, organizations do not need to > re-train their teams on an > unfamiliar programming language since SQL > skills are ubiquitous in > today's enterprises. > > Given the high velocity of innovation happening in > the underlying > Hadoop ecosystem, any SQL-based predictive analytics > technology that > plays in this ecosystem must be commensurately agile to > keep up with > the community. We strongly believe that in the Big Data > space, this > can be optimally achieved through a vibrant, diverse, > self-governed > community collectively innovating around a single codebase > while at > the same time cross-pollinating with various other data > management > communities. Apache Software Foundation is the ideal place to > meet > those ambitious goals. > > == Initial Goals == > Our initial goals > are to bring MADlib into the ASF, transition the > engineering and > governance processes to be in accordance with the > "Apache Way" and foster > a collaborative development model closely > aligned with that of HAWQ. > > > Another important goal is encouraging efforts to port to other > execution > engines. > > The MADlib project will continue developing new functionality > in an > open, community-driven way. We envision accelerating innovation > under > ASF governance, in order to meet the requirements of a wide variety > of > predictive analytics use cases. > > We will also require transitioning > of existing project infrastructure > (source code, JIRA, mailing list) to > the ASF infrastructure. > > == Current Status == > Currently, the project > is available at http://madlib.net/. The > codebase is licensed under the a > 2-clause BSD license. Our current > governance model could be described as > a "benevolent dictator" one. As > stated above, the existing MADlib > community feels that closer > alignment with HAWQ community, infrastructure > and the governance model > as it is being proposed to ASF will allow MADlib > project to thrive > much more compared to relative isolation from HAWQ. > > > === Meritocracy === > Our proposed list of initial committers include the > current MADlib R&D > team at Pivotal and existing active members of the > open source > project. This group will form a base for the broader > community we will > invite to collaborate on the codebase. We intend to > radically expand > the initial developer and user community by running the > project in > accordance with the "Apache Way". Users and new contributors > will be > treated with respect and welcomed. By participating in the > community > and providing quality patches/support that move the project > forward, > they will earn merit. They also will be encouraged to provide > non-code > contributions (documentation, events, community management, > etc.) and > will gain merit for doing so. Those with a proven support and > quality > track record will be encouraged to become committers. > > === > Community === > If MADlib is accepted for incubation, the primary initial > goal will be > transitioning the core community towards embracing the > Apache Way of > project governance. We would solicit major existing > contributors to > become committers on the project from the start. > > === > Core Developers === > MADlib core developers are skilled in working as part > of openly > governed communities. That said, most of the core developers > are > currently NOT affiliated with the ASF and would require new ICLAs > > before committing to the project. > > === Alignment === > The following > existing ASF projects can be considered when reviewing > the MADlib > proposal: > > Apache Mahout project's goal is to build an environment for > quickly > creating scalable performant machine learning applications. > Apache > Mahout is, perhaps, the oldest machine learning library in Hadoop > > ecosystem. The three major components of Mahout are an environment for > > building scalable algorithms, many new Scala + Spark (H2O in progress) > > algorithms, and Mahout's mature Hadoop MapReduce algorithms. We see > the > two projects benefiting from each other's experience of > implementing > similar classes of algorithms and look forward to a > fruitful exchange of > ideas between the two communities. > > Apache Spark is a fast engine for > processing large datasets, typically > from a Hadoop cluster, and > performing batch, streaming, interactive, > or machine learning workloads. > Recently, Apache Spark has embraced > SQL-like APIs around DataFrames at > its core. Because of that we would > expect a level of collaboration > between the two projects. Spark > project also contains a library (MLlib) > that is the closest cousin to > MADlib. MLlib is Apache Spark's scalable > machine learning library. We > see the two projects benefiting from each > other's experience of > implementing similar classes of algorithms and look > forward to a > fruitful exchange of ideas between the two communities. > > > Apache Hive is a data warehouse software that facilitates querying and > > managing large datasets residing in distributed storage. Hive provides > a > mechanism to project structure onto this data and query the data > using a > SQL-like language called HiveQL. We see a potential for MADlib > to > leverage Hive as a backend the same way it currently leverages > > PostgreSQL-derived SQL backends. This could be especially useful for > > longer running algorithms. > > Apache Drill is a schema-free SQL query > engine for Hadoop, NoSQL and > Cloud Storage. We see a potential for MADlib > to leverage Drill as a > backend the same way it currently leverages > PostgreSQL-derived SQL > backends. This could be especially useful for > analyzing data coming > from heterogenous sources and federated by the > Drill engine. > > == Known Risks == > Development has been sponsored mostly > by a single company (or its > predecessors) thus far and coordinated mainly > by the core Pivotal R&D > team. > > So far, the project's governance model > has explicitly been a > "benevolent dictator" one. For the project to fully > transition to the > "Apache Way", development must shift towards the > meritocracy-centric > model of growing a community of contributors balanced > with the needs > for extreme stability and core implementation coherency. > > > === Orphaned products === > The community proposing MADlib for incubation > is an independent open > source community. Even though Pivotal happens to > be the biggest > corporate sponsor of the project (by means of employing > the core team) > the community goes beyond those affiliated with Pivotal. > On top of > that, Pivotal is fully committed to maintain its position as > one of > the leading providers of SQL-based analytics aimed squarely at > data > scientists. MADlib is the only game in town that can leverage SQL > APIs > ranging from traditional RDBMS technology all the way to data > > warehousing (Pivotal Greenplum Database) and into SQL-on-Hadoop > (HAWQ). > Moreover, Pivotal has a vested interest in making MADlib > succeed by > driving its close integration with sister ASF projects. We > expect this to > further reduces the risk of orphaning the product. > > Even in the absence > of support by a particular vendor such as Pivotal, > and in a worst-case > scenario where HAWQ and Greenplum Database fail to > gain traction in OSS, > the existence of an established PostgreSQL OSS > project means there’s will > still be a working stack for MADlib. > > === Inexperience with Open Source > === > MADlib has been an open source project from the outset. All > developers > working on the project (regardless of their employment > affiliation) > did so completely in the open. While the governance model of > MADlib > has been more of a benevolent dictator model, the project has > always > been receptive to accepting contributions from all sources and > > including them in future releases based on thorough code review, > testing, > and compliance with the project’s coding best practices. > > === > Homogeneous Developers === > While most of the initial committers are > employed by Pivotal, there's > still a healthy level of interest coming > from academia. On top of that > we expect to spark curiosity in sister ASF > projects and attract > developers unaffiliated with Pivotal. Finally, > MADlib is being used > extensively whenever Pivotal engages with customers > on data science > projects. This typically means that the skills remain > within a > customer organization which further increases the chance of > turning > customer data scientists into MADlib contributors. > > === > Reliance on Salaried Developers === > A large percentage of the > contributors are paid to work in the Big > Data space. While they might > wander from their current employers, they > are unlikely to venture far > from their core expertise and thus will > continue to be engaged with the > project regardless of their current > employers. In addition, the project > is still enjoying popularity in > academic circles and we hope that will > help mitigate reliance on > salaried developers as well. > > === > Relationships with Other Apache Products === > As mentioned in the > Alignment section, MADlib may consider various > degrees of integration and > code exchange with Apache Spark (MLlib), > Apache Mahout, Apache Hive and > Apache Drill projects. We expect > integration points to be inside and > outside the project. We look > forward to collaborating with these > communities as well as other > communities under the Apache umbrella. > > > === An Excessive Fascination with the Apache Brand === > While we intend to > leverage the Apache "brand" when talking to other > projects as a testament > to our project’s neutrality, we have no plans > for making use of the > Apache brand in press releases nor posting > billboards advertising > acceptance of MADlib into Apache Incubator. > > == Documentation == > The > documentation is currently available at: > https://github.com/madlib/frontpage > > > The documentation is currently licensed under 2-clause BSD license. > > > == Initial Source == > Initial source code is available at: > * MADlib: > https://github.com/madlib/madlib > * Testsuite: > https://github.com/madlib/testsuite > * Contributors: > https://github.com/madlib/contrib > > The code is currently licensed under > 2-clause BSD license. > > == Source and Intellectual Property Submission > Plan == > As soon as MADlib is approved to join the Incubator, the source > code > will be transitioned via the Software Grant Agreement onto ASF > > infrastructure and in turn made available under the Apache License, > > version 2.0. We know of no legal encumbrances that would inhibit the > > transfer of source code to the ASF. > > == External Dependencies == > > > Runtime dependencies: > * boost-1.47.0 (Boost Software License) > * > _m_widen_init (MIT for this subcomponent of GCC) > * python-argparse-1.2.1 > (PSF LICENSE AGREEMENT FOR PYTHON 2.7.1) > * pyyaml-3.10 (MIT license) > * > cern_root-5.34 (LGPL, however this dependency will be removed > since the 2 > cern modules used are being entirely re-written in MADlib) > * eigen-3.2.2 > (Mozilla Public License) > * pyxb-1.2.4 (Apache license version 2) > * > python (Python Software Foundation License Version 2) > * mathjax-2.5 > (Apache license version 2) > > Build only dependencies: > * doxypy-0.4.2 > (GPL) > * cmake-2.8.4 (BSD 3-clause License) > * doxygen >= 1.8.4 (GPL) > * > flex >= 2.5.33 (BSD) > * bison >= 2.4 (GPL) > * latex (LaTeX Project Public > License) > * TikZ-UML (no license information) > > Cryptography > * N/A > > > == Required Resources == > > === Mailing lists === > * > priv...@madlib.incubator.apache.org (moderated subscriptions) > * > comm...@madlib.incubator.apache.org > * d...@madlib.incubator.apache.org > > * > iss...@madlib.incubator.apache.org > * u...@madlib.incubator.apache.org > > > > === Git Repository === > > https://git-wip-us.apache.org/repos/asf/incubator-madlib.git > > === Issue > Tracking === > JIRA Project MADlib (MADLIB) > > We will also request > migration of our current JIRA available at > http://jira.madlib.net/ > > > === Other Resources === > > Means of setting up regular builds for MADlib > on builds.apache.org > will require integration with Docker support. > > > == > Initial Committers == > * Anirudh Kondaveeti > * Caleb Welton > * Frank > McQuillan > * Gang Xiong > * Gautam Muralidhar > * Hitoshi Harada > * Hulya > Emir-farinas > * Ian Huston > * KeeSiong Ng > * Noel Sio > * Rahul Iyer > * > Rashmi Raghu > * Regunathan Radhakrishnan > * Ronert Obst > * Samuel > Ziegler > * Sarah Aerni > * Srivatsan Ramanujam > * Woo Jae Jung > * Xixuan > Feng > * Yu Yang > * Atri Sharma > * Greg Chase > * Chloe Jackson > * Roman > Shaposhnik > * Vaibhav Gumashta > * Ted Dunning > * Konstantin Boudnik > > > == Affiliations == > * Hortonworks: Vaibhav Gumashta > * MapR: Ted Dunning > > * WANDisco: Konstantin Boudnik > * Barclays: Atri Sharma > * Pivotal: > everyone else on this proposal > > == Sponsors == > > === Champion === > > Roman Shaposhnik > > === Nominated Mentors === > > The initial mentors are > listed below: > * Ted Dunning - Apache Member, MapR > * Konstantin Boudnik > - Apache Member, WANDisco > * Roman Shaposhnik - Apache Member, Pivotal > > > === Sponsoring Entity === > We would like to propose Apache incubator to > sponsor this project. >
