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From Sarah Aerni <>
Subject Re: [VOTE] Accept MADlib into the Apache Incubator
Date Fri, 11 Sep 2015 14:35:58 GMT
+1 (non-binding) On Wed, Sep 09, 2015 at 07:37PM, Roman Shaposhnik wrote: >
> Following the discussion earlier: > > > I would
like to call a VOTE for accepting > MADlib community as a new ASF incubator
> project. > > The proposal is available at: > > 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
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
> ( 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 > ( 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 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:
> > The documentation is currently licensed under 2-clause BSD license. > >
== Initial Source == > Initial source code is available at: > * MADlib: > * Testsuite: > * Contributors: > > 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 === > * (moderated subscriptions) > * > * > * > * > >
=== Git Repository === > > > === Issue
Tracking === > JIRA Project MADlib (MADLIB) > > We will also request
migration of our current JIRA available at > > >
=== Other Resources === > > Means of setting up regular builds for MADlib
on > 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.

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