DataSketches Proposal WRT Commons-Math

[le...@gmail.com, le...@gmail.com]

As your suggestion may come up again, I have addressed it by adding a small section to the proposal itself (in the Google Doc).

On 2019/02/25 17:36:31, Ted Dunning <te...@gmail.com> wrote: 
> There is also the general question of whether it is better to be a
> top-level project or to become a contribution to commons math.
> 
> 
> 
> On Sun, Feb 24, 2019 at 10:56 PM leerho <le...@gmail.com> wrote:
> 
> > Yes I will try that tomorrow.
> >
> > On Sun, Feb 24, 2019 at 7:34 PM Kenneth Knowles <ke...@apache.org> wrote:
> >
> > > Can you share the Google doc with the proposal? Per Ted's advice, we can
> > > iterate quickly there and move it to the wiki when it becomes a bit more
> > > stable.
> > >
> > > Kenn
> > >
> > > On Fri, Feb 22, 2019 at 10:21 PM leerho@gmail.com <le...@gmail.com>
> > > wrote:
> > >
> > > > Thanks for the offer.  i am a neophyte at this process and email app!
> >  I
> > > > could use a lot of help getting this off the ground!  Also, I'm not
> > sure
> > > > that Mr. Chen and Mr. Onofré have fully accepted taking this on :)
> > > >
> > > > Lee.
> > > >
> > > > On 2019/02/23 06:03:58, Kenneth Knowles <ke...@apache.org> wrote:
> > > > > Nice.
> > > > >
> > > > > I would very much like to help mentor this project, though you
> > already
> > > > have
> > > > > a couple good ones.
> > > > >
> > > > > I concur with incubator as sponsoring entity.
> > > > >
> > > > > Kenn (VP Apache Beam)
> > > > >
> > > > > On Fri, Feb 22, 2019 at 9:45 PM leerho <le...@gmail.com> wrote:
> > > > >
> > > > > > I didn't realize that this mail list does not accept PDF files,
> > > > apparently
> > > > > > only text.  So let me try one more time ... :)  Please let me know
> > if
> > > > > > this works!
> > > > > >
> > > > > >
> > > > > > = Apache DataSketches Proposal[1] =
> > > > > >
> > > > > > == Abstract ==
> > > > > >
> > > > > > DataSketches.GitHub.io is an open source, high-performance library
> > > of
> > > > > > stochastic streaming algorithms commonly called "sketches" in the
> > > data
> > > > > > sciences. Sketches are small, stateful programs that process
> > massive
> > > > data
> > > > > > as a stream and can provide approximate answers, with mathematical
> > > > > > guarantees, to computationally difficult queries
> > orders-of-magnitude
> > > > faster
> > > > > > than traditional, exact methods.
> > > > > >
> > > > > > This proposal is to move DataSketches to the Apache Software
> > > > > > Foundation(ASF) transferring ownership of its copyright
> > intellectual
> > > > > > property to the ASF.  Thereafter, DataSketches would be officially
> > > > known as
> > > > > > Apache DataSketches and its evolution and governance would come
> > under
> > > > the
> > > > > > rules and guidance of the ASF.
> > > > > >
> > > > > > == Introduction ==
> > > > > >
> > > > > > The DataSketches library contains carefully crafted implementations
> > > of
> > > > > > sketch algorithms that meet rigorous standards of quality and
> > > > performance
> > > > > > and provide capabilities required for large-scale production
> > systems
> > > > that
> > > > > > must process and analyze massive data. The DataSketches core
> > > > repository is
> > > > > > written in Java with a parallel core repository written in C++ that
> > > > > > includes Python wrappers. The DataSketches library also includes
> > > > special
> > > > > > repositories for extending the core library for Apache Hive and
> > > Apache
> > > > Pig.
> > > > > > The sketches developed in the different languages share a common
> > > binary
> > > > > > storage format so that sketches created and stored in Java, for
> > > > example,
> > > > > > can be fully used in C++, and visa versa.  Because the stored
> > sketch
> > > > > > "images" are just a "blob" of bytes (similar to picture images),
> > they
> > > > can
> > > > > > be shared across many different systems, languages and platforms.
> > > > > >
> > > > > > The DataSketches documentation website,
> > > https://datasketches.github.io
> > > > ,
> > > > > > includes general tutorials, a comprehensive research section with
> > > > > > references to relevant academic papers, extensive examples for
> > using
> > > > the
> > > > > > core library directly as well as examples for accessing the library
> > > in
> > > > > > Hive, Pig, and Apache Spark.
> > > > > >
> > > > > > The DataSketches library also includes a characterization
> > repository
> > > > for
> > > > > > long running test programs that are used for studying accuracy and
> > > > > > performance of these sketches over wide ranges of input variables.
> > > The
> > > > data
> > > > > > produced by these programs is used for generating the many
> > > performance
> > > > > > plots contained in the documentation website and for academic
> > > > > > publications.
> > > > > >
> > > > > > The code repositories used for production are versioned and
> > published
> > > > to
> > > > > > Maven Central on periodic intervals as the library evolves.
> > > > > >
> > > > > > The DataSketches library also includes several experimental
> > > > repositories
> > > > > > for use-cases outside the large-scale systems environments, such as
> > > > > > sketches for mobile, IoT devices (Android), command-line access of
> > > the
> > > > > > sketch library, and an experimental repository for vector-based
> > > > sketches
> > > > > > that performs approximate Singular Value Decomposition (SVD)
> > analysis
> > > > that
> > > > > > could potentially be used in Machine Learning (ML) applications.
> > > > > >
> > > > > > == Background ==
> > > > > >
> > > > > > The DataSketches library was started in 2012 as internal Yahoo
> > > project
> > > > to
> > > > > > dramatically reduce time and resources required for distinct
> > (unique)
> > > > > > counting.  An extensive search on the Internet at the time yielded
> > a
> > > > number
> > > > > > of theoretical papers on stochastic streaming algorithms with
> > > > pseudocode
> > > > > > examples, but we did not find any usable open-source code of the
> > > > quality we
> > > > > > felt we needed for our internal production systems.  So we started
> > a
> > > > small
> > > > > > project (one person) to develop our own sketches working directly
> > > from
> > > > > > published theoretical papers.
> > > > > >
> > > > > > The DataSketches library was designed from the start with the
> > > > objective of
> > > > > > making these algorithms, usually only described in theoretical
> > > papers,
> > > > > > easily accessible to systems developers for use in our internal
> > > > production
> > > > > > systems. By necessity, the code had to be of the highest quality
> > and
> > > > > > thoroughly tested. The wide variety of our internal production
> > > systems
> > > > > > drove the requirement that the sketch implementations had to have
> > an
> > > > > > absolute minimum of external, run-time dependencies in order to
> > > > simplify
> > > > > > integration and troubleshooting.
> > > > > >
> > > > > > Our internal experiments demonstrated dramatic positive impact on
> > the
> > > > > > performance of our systems.  As a result, the DataSketches library
> > > > quickly
> > > > > > evolved to include different types of sketches for different types
> > of
> > > > > > queries, such as frequent-items (a.k.a, heavy-hitters) algorithms,
> > > > > > quantile/histogram algorithms, and weighted and unweighted sampling
> > > > > > algorithms.
> > > > > >
> > > > > > We quickly discovered that developing these sketch algorithms to be
> > > > truly
> > > > > > robust in production environments is quite difficult and requires
> > > deep
> > > > > > understanding of the underlying mathematics and statistics as well
> > as
> > > > > > extensive experience in developing high quality code for 24/7
> > > > production
> > > > > > systems. This is a difficult combination of skills for any one
> > > > organization
> > > > > > to collect and maintain over time. It became clear that this
> > > technology
> > > > > > needed a community larger than Yahoo to evolve.  In November, 2015,
> > > > this
> > > > > > factor, along with Yahoo’s strong experience and support of open
> > > > source,
> > > > > > led to the decision to open source this technology under an Apache
> > > 2.0
> > > > > > license on GitHub. Since that time our community has expanded
> > > > considerably
> > > > > > and the key contributors to this effort includes leading research
> > > > > > scientists from a number of universities as well as practitioners
> > and
> > > > > > researchers from a number of major corporations. The core of this
> > > > group is
> > > > > > very active as we meet weekly to discuss research directions and
> > > > > > engineering priorities.
> > > > > >
> > > > > > It is important to note that our internal systems at Yahoo use the
> > > > current
> > > > > > public GitHub open source DataSketches library and not an internal
> > > > version
> > > > > > of the code.
> > > > > >
> > > > > > The close collaboration of scientific research and engineering
> > > > development
> > > > > > experience with actual massive-data processing systems has also
> > > > produced
> > > > > > new research publications in the field of stochastic streaming
> > > > algorithms,
> > > > > > for example:
> > > > > >
> > > > > > * Daniel Anderson, Pryce Bevan, Kevin J. Lang, Edo Liberty, Lee
> > > > Rhodes, and
> > > > > > Justin Thaler. A high-performance algorithm for identifying
> > frequent
> > > > items
> > > > > > in data streams. In ACM IMC 2017.
> > > > > >
> > > > > > * Anirban Dasgupta, Kevin J. Lang, Lee Rhodes, and Justin Thaler. A
> > > > > > framework for estimating stream expression cardinalities. In
> > > *EDBT/ICDT
> > > > > > Proceedings ‘16 *, pages 6:1–6:17, 2016.
> > > > > >
> > > > > > * Mina Ghashami, Edo Liberty, Jeff M. Phillips. Efficient Frequent
> > > > > > Directions Algorithm for Sparse Matrices. In ACM SIGKDD Proceedings
> > > > ‘16,
> > > > > > pages 845-854, 2016.
> > > > > >
> > > > > > * Zohar S. Karnin, Kevin J. Lang, and Edo Liberty. Optimal quantile
> > > > > > approximation in streams. In IEEE FOCS Proceedings ‘16, pages
> > 71–78,
> > > > 2016.
> > > > > >
> > > > > > * Kevin J Lang. Back to the future: an even more nearly optimal
> > > > cardinality
> > > > > > estimation algorithm. arXiv preprint
> > > https://arxiv.org/abs/1708.06839,
> > > > > > 2017.
> > > > > >
> > > > > > * Edo Liberty. Simple and deterministic matrix sketching. In ACM
> > KDD
> > > > > > Proceedings ‘13, pages 581– 588, 2013.
> > > > > >
> > > > > > * Edo Liberty, Michael Mitzenmacher, Justin Thaler, and Jonathan
> > > > Ullman.
> > > > > > Space lower bounds for itemset frequency sketches. In ACM PODS
> > > > Proceedings
> > > > > > ‘16, pages 441–454, 2016.
> > > > > >
> > > > > > * Michael Mitzenmacher, Thomas Steinke, and Justin Thaler.
> > > Hierarchical
> > > > > > heavy hitters with the space saving algorithm. In SIAM ALENEX
> > > > Proceedings
> > > > > > ‘12, pages 160–174, 2012.
> > > > > >
> > > > > > == The Rationale for Sketches ==
> > > > > >
> > > > > > In the analysis of big data there are often problem queries that
> > > don’t
> > > > > > scale because they require huge compute resources and time to
> > > generate
> > > > > > exact results. Examples include count distinct, quantiles, most
> > > > frequent
> > > > > > items, joins, matrix computations, and graph analysis.
> > > > > >
> > > > > > If we can loosen the requirement of “exact” results from our
> > queries
> > > > and be
> > > > > > satisfied with approximate results, within some well understood
> > > bounds
> > > > of
> > > > > > error, there is an entire branch of mathematics and data science
> > that
> > > > has
> > > > > > evolved around developing algorithms that can produce approximate
> > > > results
> > > > > > with mathematically well-defined error properties.
> > > > > >
> > > > > > With the additional requirements that these algorithms must be
> > small
> > > > > > (compared to the size of the input data), sublinear (the size of
> > the
> > > > sketch
> > > > > > must grow at a slower rate than the size of the input stream),
> > > > streaming
> > > > > > (they can only touch each data item once), and mergeable (suitable
> > > for
> > > > > > distributed processing), defines a class of algorithms that can be
> > > > > > described as small, stochastic, streaming, sublinear mergeable
> > > > algorithms,
> > > > > > commonly called sketches (they also have other names, but we will
> > use
> > > > the
> > > > > > term sketches from here on).
> > > > > >
> > > > > > To be truly streaming and be able to process data in a single pass,
> > > > > > sketches must make absolute minimum assumptions about the input
> > > stream.
> > > > > > This is critically important, as there is no “second chance” to
> > > > process the
> > > > > > data.
> > > > > >
> > > > > > For example, sketches should not make assumptions about the order
> > of
> > > > stream
> > > > > > items, the stream length, the dynamic range of values, or the
> > > > distribution
> > > > > > of item occurrence frequencies. Sketches should be tolerant of
> > NaNs,
> > > > Nulls
> > > > > > and empty objects. About the only thing that the sketch needs to
> > know
> > > > about
> > > > > > the stream is how to extract items from it and what type the item
> > is,
> > > > e.g.,
> > > > > > is it a numeric value or a string.
> > > > > >
> > > > > > As far as the sketch is concerned, the input stream is a sequence
> > of
> > > > items
> > > > > > in some unknown random order with unknown random values.
> > > > > >
> > > > > > The sketch is essentially a complex state machine and combined with
> > > the
> > > > > > random input stream defines a stochastic process. We then apply
> > > > > > probabilistic methods to interpret the states of the stochastic
> > > > process in
> > > > > > order to extract useful information about the input stream itself.
> > > The
> > > > > > resulting information will be approximate, but we also use
> > additional
> > > > > > probabilistic methods to extract an estimate of the likely
> > > probability
> > > > > > distribution of error.
> > > > > >
> > > > > > There is a significant scientific contribution here that is
> > defining
> > > > the
> > > > > > state machine, understanding the resulting stochastic process,
> > > > developing
> > > > > > the probabilistic methods, and proving mathematically, that it all
> > > > works!
> > > > > > This is why the scientific contributors to this project are a
> > > critical
> > > > and
> > > > > > strategic component to our success.  The development engineers
> > > > translate
> > > > > > the concepts of the proposed state machine and probabilistic
> > methods
> > > > into
> > > > > > production-quality code. Even more important, they work closely
> > with
> > > > the
> > > > > > scientists, feeding back system and user requirements, which leads
> > > not
> > > > only
> > > > > > to superior product design, but to new science as well.  A number
> > of
> > > > > > scientific papers our members have published (see above) is a
> > direct
> > > > result
> > > > > > of this close collaboration.
> > > > > >
> > > > > > Because sketches are small they can be processed extremely fast,
> > > often
> > > > many
> > > > > > orders-of-magnitude faster than traditional exact computations. For
> > > > > > interactive queries there may not be other viable alternatives, and
> > > in
> > > > the
> > > > > > case of real-time analysis, sketches are the only known solution.
> > > > > >
> > > > > > For any system that needs to extract useful information from
> > massive
> > > > data
> > > > > > sketches are essential tools that should be tightly integrated into
> > > the
> > > > > > system’s analysis capabilities. This technology has helped Yahoo
> > > > > > successfully reduce data processing times from days to hours or
> > > > minutes on
> > > > > > a number of its internal platforms and has enabled subsecond
> > queries
> > > on
> > > > > > real-time platforms that would have been infeasible without
> > sketches.
> > > > > > The Rationale for Apache DataSketches
> > > > > > Other open source implementations of sketch algorithms can be found
> > > on
> > > > the
> > > > > > Internet. However, we have not yet found any open source
> > > > implementations
> > > > > > that are as comprehensive, engineered with the quality required for
> > > > > > production systems, and with usable and guaranteed error
> > properties.
> > > > Large
> > > > > > Internet companies, such as Google and Facebook, have published
> > > papers
> > > > on
> > > > > > sketching, however, their implementations of their published
> > > > algorithms are
> > > > > > proprietary and not available as open source.
> > > > > >
> > > > > > The DataSketches library already provides integrations with a
> > number
> > > of
> > > > > > major Apache data processing platforms such as Apache Hive, Apache
> > > Pig,
> > > > > > Apache Spark and Apache Druid, and is also integrated with a number
> > > of
> > > > > > other open source data processing platforms such as Splice Machine,
> > > > GCHQ
> > > > > > Gaffer and PostgreSQL.
> > > > > >
> > > > > > We believe that having DataSketches as an Apache project will
> > provide
> > > > an
> > > > > > immediate, worthwhile, and substantial contribution to the open
> > > source
> > > > > > community, will have a better opportunity to provide a meaningful
> > > > > > contribution to both the science and engineering of sketching
> > > > algorithms,
> > > > > > and integrate with other Apache projects.  In addition, this is a
> > > > > > significant opportunity for Apache to be the "go-to" destination
> > for
> > > > users
> > > > > > that want to leverage this exciting technology.
> > > > > >
> > > > > > == Initial Goals ==
> > > > > >
> > > > > > We are breaking our initial goals into short-term (2-6 months) and
> > > > > > intermediate to long-term ( 6 months to 2 years):
> > > > > >
> > > > > > Our short-term goals include:
> > > > > >
> > > > > > * Understanding and adapting to the Apache development process and
> > > > > > structures.
> > > > > >
> > > > > > * Start refactoring codebase and move various DataSketches
> > > repositories
> > > > > > code to Apache Git repository.
> > > > > >
> > > > > > * Continue development of new features, functions, and fixes.
> > > > > >
> > > > > > * Specific sub-projects (e.g., C++ and Python) will continue to be
> > > > > > developed and expanded.
> > > > > >
> > > > > >
> > > > > > The intermediate to long term goals include:
> > > > > >
> > > > > > * Completing the design and implementation of the C++ sketches to
> > > > > > complement what is already available in Java, and the Python
> > wrappers
> > > > of
> > > > > > those C++ sketches.
> > > > > >
> > > > > > * Expanding the C++ build framework to include Windows and the
> > > popular
> > > > > > Linux variants.
> > > > > >
> > > > > > * Continued engagement with the scientific research community on
> > the
> > > > > > development of new algorithms for computationally difficult
> > problems
> > > > that
> > > > > > heretofore have not had a sketching solution.
> > > > > >
> > > > > > == Current Status ==
> > > > > >
> > > > > > The DataSketches GitHub project has been quite successful.  As of
> > > this
> > > > > > writing (Feb, 2019) the number of downloads measured by the Nexus
> > > > > > Repository Manager at https://oss.sonatype.org has grown by
> > nearly a
> > > > > > factor
> > > > > > of 10 over the past year to about 55 thousand per month. The
> > > > > > DataSketches/sketches-core repository has about 560 stars and 141
> > > > forks,
> > > > > > which is pretty good for a highly specialized library.
> > > > > >
> > > > > > === Development Practices ===
> > > > > >
> > > > > > ==== Source Control ====
> > > > > >
> > > > > > All of our developers have extensive experience with Git version
> > > > control
> > > > > > and follow accepted practices for use of Pull Requests (PRs), code
> > > > reviews
> > > > > > and commits to master, for example.
> > > > > >
> > > > > > ==== Testing ====
> > > > > >
> > > > > > Sketches, by their nature are probabilistic programs and don’t
> > > > necessarily
> > > > > > behave deterministically.  For some of the sketches we
> > intentionally
> > > > insert
> > > > > > random noise into the code as this gives us the mathematical
> > > properties
> > > > > > that we need to guarantee accuracy.  This can make the behavior of
> > > > these
> > > > > > algorithms quite unintuitive and provides significant challenges to
> > > the
> > > > > > developer who wishes to test these algorithms for correctness. As a
> > > > result,
> > > > > > our testing strategy includes two major components: unit tests, and
> > > > > > characterization tests.
> > > > > >
> > > > > > ===== Unit Testing =====
> > > > > >
> > > > > > Our unit tests are primarily quick tests to make sure that we
> > > exercise
> > > > all
> > > > > > critical paths in the code and that key branches are executed
> > > > correctly. It
> > > > > > is important that they execute relatively fast as they are
> > generally
> > > > run on
> > > > > > every code build. The sketches-core repository alone has about 22
> > > > thousand
> > > > > > statements, over 1300 unit tests and code coverage of about 98.2%
> > as
> > > > > > measured by Atlassian/Clover.  It is our goal for all of our code
> > > > > > repositories that are used in production that they have code
> > coverage
> > > > > > greater than 90%.
> > > > > >
> > > > > > ===== Characterization Testing =====
> > > > > >
> > > > > > In order to test the probabilistic methods that are used to
> > interpret
> > > > the
> > > > > > stochastic behaviors of our sketches we have a separate
> > > > characterization
> > > > > > repository that is dedicated to this.  To measure accuracy, for
> > > > example,
> > > > > > requires running thousands of trials at each of many different
> > points
> > > > along
> > > > > > the domain axis. Each trial compares its estimated results against
> > a
> > > > known
> > > > > > exact result producing an error for that trial.  These error
> > > > measurements
> > > > > > are then fed into our Quantiles sketch to capture the actual
> > > > distribution
> > > > > > of error at that point along the axis. We then select quantile
> > > contours
> > > > > > across all the distributions at points along the axis.  These
> > > contours
> > > > can
> > > > > > then be plotted to reveal the shape of the actual error
> > distribution.
> > > > These
> > > > > > distributions are not at all Gaussian, in fact they can be quite
> > > > complex.
> > > > > > Nonetheless, these distributions are then checked against our
> > > > statistical
> > > > > > guarantees inherent to the specific sketch algorithm and its
> > > > parameters.
> > > > > > There are many examples of these characterization error
> > distributions
> > > > on
> > > > > > our website. The runtimes of these tests can be very long and can
> > > range
> > > > > > from many minutes to hours, and some can run for days.  Currently,
> > we
> > > > have
> > > > > > separate characterization repositories for Java and C++ / Python.
> > > > > >
> > > > > > It is our goal that we perform this characterization analysis for
> > all
> > > > of
> > > > > > our sketches.  By definition, the code that runs these
> > > characterization
> > > > > > tests is open-source so others can run these tests as well.  We do
> > > not
> > > > have
> > > > > > formal releases of this code (because it is not production code)
> > and
> > > > it is
> > > > > > not published to Maven Central.
> > > > > >
> > > > > > === Meritocracy ===
> > > > > >
> > > > > > DataSketches was initially developed based on requirements within
> > > > Yahoo. As
> > > > > > a project on GitHub, DataSketches has received contributions from
> > > > numerous
> > > > > > individual developers from around the world, dedicated research
> > work
> > > > from
> > > > > > senior scientists at Amazon and Visa, and academic researchers from
> > > > > > Georgetown University, Princeton, and MIT.
> > > > > >
> > > > > > As a project under incubation, we are committed to expanding our
> > > > effort to
> > > > > > build an environment which supports a meritocracy. We are focused
> > on
> > > > > > engaging the community and other related projects for support and
> > > > > > contributions. Moreover, we are committed to ensure contributors
> > and
> > > > > > committers to DataSketches come from a broad mix of organizations
> > > > through a
> > > > > > merit-based decision process during incubation. We believe strongly
> > > in
> > > > the
> > > > > > DataSketches premise that fulfills the concept of a well engineered
> > > and
> > > > > > scientifically rigorous library that implements these powerful
> > > > algorithms
> > > > > > and are committed to growing an inclusive community of DataSketches
> > > > > > contributors and users.
> > > > > >
> > > > > > === Community ===
> > > > > >
> > > > > > Yahoo has a long history and active engagement in the Open Source
> > > > > > community. Major projects include: Vespa.ai, Bullet, Moloch,
> > > Panoptes,
> > > > > > Screwdriver.cd, Athenz, HaloDB, Maha, Mendel, TensorFlowOnSpark,
> > > > gifshot,
> > > > > > fluxible, as well as the creation, contribution and incubation of
> > > many
> > > > > > Apache projects such as Apache Hadoop, Pig, Bookkeeper, Oozie,
> > > > Zookeeper,
> > > > > > Omid, Pulsar, Traffic Server, Storm, Druid, and many more.
> > > > > >
> > > > > > Every day, DataSketches is actively used by a organizations and
> > > > > > institutions around the world for batch and stream processing of
> > > data.
> > > > We
> > > > > > believe acceptance will allow us to consolidate existing
> > > > > > DataSketches-related work, grow the DataSketches community, and
> > > deepen
> > > > > > connections between DataSketches and other open source projects.
> > > > > >
> > > > > > === Introduction to the Core Developers & Contributors ===
> > > > > >
> > > > > > The core developers and contributors for DataSketches are from
> > > diverse
> > > > > > backgrounds, but primarily are scientists that love engineering and
> > > > > > engineers that love science. A large part of the value we bring
> > comes
> > > > from
> > > > > > this synthesis.  These individuals have already contributed
> > > > substantially
> > > > > > to the code, algorithms, and/or mathematical proofs that form the
> > > > basis of
> > > > > > the library.
> > > > > >
> > > > > > This core group also form the Initial Committers with write
> > > > permissions to
> > > > > > the repository. Those marked with (*) Meet weekly to plan the
> > > research
> > > > and
> > > > > > engineering direction of the project.
> > > > > >
> > > > > > ==== Scientists That Love Engineering ====
> > > > > >
> > > > > > * Eshcar Hillel: Senior Research Scientist, Yahoo Labs, Israel.
> > > > Interests:
> > > > > > distributed systems, scalable systems and platforms for big data
> > > > > > processing, concurrent algorithms and data structures,
> > > > > >
> > > > > > * Kevin Lang: (*) Distinguished Research Scientist, Yahoo Labs,
> > > > Sunnyvale,
> > > > > > California. Interests: algorithms, theoretical and applied
> > > mathematics,
> > > > > > encoding and compression theory, theoretical and applied
> > performance
> > > > > > optimization.
> > > > > >
> > > > > > * Edo Liberty: (*) Director of Research, Head of Amazon AI Labs,
> > Palo
> > > > Alto,
> > > > > > California. Manages the algorithms group at Amazon AI. We build
> > > > scalable
> > > > > > machine learning systems and algorithms which are used both
> > > internally
> > > > and
> > > > > > externally by customers of SageMaker, AWS's flagship machine
> > learning
> > > > > > platform.
> > > > > >
> > > > > > * Jon Malkin: (*) Senior Scientist, Yahoo Labs, Sunnyvale.
> > Interests:
> > > > > > Computational advertising, machine learning, speech recognition,
> > > > > > data-driven analysis, large scale experimentation, big data,
> > > > stream/complex
> > > > > > event processing
> > > > > >
> > > > > > * Justin Thaler: (*) Assistant Professor, Department of Computer
> > > > Science,
> > > > > > Georgetown University, Washington D.C. Interests: algorithms and
> > > > > > computational complexity, complexity theory, quantum algorithms,
> > > > private
> > > > > > data analysis, and learning theory, developing efficient streaming
> > > and
> > > > > > sketching algorithms
> > > > > >
> > > > > > ==== Engineers That Love Science ====
> > > > > >
> > > > > > * Roman Leventov: Senior Software Engineer,  Metamarkets / Snap.
> > > > Interests:
> > > > > > design and implementation of data storing and data processing
> > > > (distributed)
> > > > > > systems, performance optimization, CPU performance, mechanical
> > > > sympathy,
> > > > > > JVM performance, API design, databases, (concurrent) data
> > structures,
> > > > > > memory management, garbage collection algorithms, language design
> > and
> > > > > > runtimes (their tradeoffs), distributed systems (cloud) efficiency,
> > > > Linux,
> > > > > > code quality, code transformation, pure functional programming
> > > models,
> > > > > > Haskell.
> > > > > >
> > > > > > * Lee Rhodes: (*) Distinguished Architect, lead developer and
> > founder
> > > > of
> > > > > > the DataSketches project, Yahoo, Sunnyvale, California.  Interests:
> > > > > > streaming algorithms, mathematics, computer science, high quality
> > and
> > > > high
> > > > > > performance code for the analysis of massive data, bridging the
> > > divide
> > > > > > between theory and practice.
> > > > > >
> > > > > > * Alexander Saydakov: (*) Senior Software Engineer, Yahoo,
> > Sunnyvale,
> > > > > > California. Interests: applied mathematics, computer science, big
> > > data,
> > > > > > distributed systems.
> > > > > >
> > > > > > === Introduction to Additional Interested Contributors ===
> > > > > >
> > > > > > These folks have been intermittently involved and contributed, but
> > > are
> > > > > > strong supporters of this project.
> > > > > >
> > > > > > * Frank Grimes: GitHub ID: frankgrimes97
> > > > > >
> > > > > > * Mina Ghashami: [mina.ghashami at gmail dot com] Ph.D. Computer
> > > > Science,
> > > > > > Univ of Utah. Interests: Machine Learning, Data Mining, matrix
> > > > > > approximation, streaming algorithms, randomized linear algebra.
> > > > > >
> > > > > > * Christopher Musco: [christopher.musco at gmail dot com] Ph.D.
> > > > Computer
> > > > > > Science, Research Instructor, Princeton University. Interests:
> > > > algorithmic
> > > > > > foundations of data science and machine learning, efficient methods
> > > for
> > > > > > processing and understanding large datasets, often working at the
> > > > > > intersection of theoretical computer science, numerical linear
> > > > algebra, and
> > > > > > optimization.
> > > > > >
> > > > > > * Graham Cormode: [g.cormode at warwick.ac dot uk] Ph.D. Computer
> > > > Science,
> > > > > > Professor, Warwick University, Warwick, England. Interests: all
> > > > aspects of
> > > > > > the "data lifecycle", from data collection and cleaning, through
> > > > mining and
> > > > > > analytics. (Professor Cormode is one of the world’s leading
> > > scientists
> > > > in
> > > > > > sketching algorithms)
> > > > > >
> > > > > > === Alignment ===
> > > > > >
> > > > > > The DataSketches library already provides integrations and example
> > > > code for
> > > > > > Apache Hive, Apache Pig, Apache Spark and is deeply integrated into
> > > > Apache
> > > > > > Druid.
> > > > > >
> > > > > > == Known Risks ==
> > > > > >
> > > > > > The following subsections are specific risks that have been
> > > identified
> > > > by
> > > > > > the ASF that need to be addressed.
> > > > > >
> > > > > > === Risk: Orphaned Products ===
> > > > > >
> > > > > > The DataSketches library is presently used by a number of
> > > > organizations,
> > > > > > from small startups to Fortune 100 companies, to construct
> > production
> > > > > > pipelines that must process and analyze massive data. Yahoo has a
> > > > long-term
> > > > > > commitment to continue to advance the DataSketches library;
> > moreover,
> > > > > > DataSketches is seeing increasing interest, development, and
> > adoption
> > > > from
> > > > > > many diverse organizations from around the world. Due to its
> > growing
> > > > > > adoption, we feel it is quite unlikely that this project would
> > become
> > > > > > orphaned.
> > > > > >
> > > > > > === Risk: Inexperience with Open Source ===
> > > > > >
> > > > > > Yahoo believes strongly in open source and the exchange of
> > > information
> > > > to
> > > > > > advance new ideas and work. Examples of this commitment are active
> > > open
> > > > > > source projects such as those mentioned above. With DataSketches,
> > we
> > > > have
> > > > > > been increasingly open and forward-looking; we have published a
> > > number
> > > > of
> > > > > > papers about breakthrough developments in the science of streaming
> > > > > > algorithms (mentioned above) that also reference the DataSketches
> > > > library.
> > > > > > Our submission to the Apache Software Foundation is a logical
> > > > extension of
> > > > > > our commitment to open source software.
> > > > > >
> > > > > > Key committers at Yahoo with strong open source backgrounds include
> > > > Aaron
> > > > > > Gresch, Alan Carroll, Alessandro Bellina, Anastasia Braginsky,
> > > Andrews
> > > > > > Sahaya Albert, Arun S A G, Atul Mohan, Brad McMillen, Bryan Call,
> > > Daryn
> > > > > > Sharp, Dav Glass, David Carlin, Derek Dagit, Eric Payne, Eshcar
> > > Hillel,
> > > > > > Ethan Li, Fei Deng, Francis Christopher Liu, Francisco
> > > Perez-Sorrosal,
> > > > Gil
> > > > > > Yehuda. Govind Menon, Hang Yang, Jacob Estelle, Jai Asher, James
> > > > Penick,
> > > > > > Jason Kenny, Jay Pipes, Jim Rollenhagen, Joe Francis, Jon Eagles,
> > > > Kihwal
> > > > > > Lee, Kishorkumar Patil, Koji Noguchi, Kuhu Shukla, Michael
> > Trelinski,
> > > > > > Mithun Radhakrishnan, Nathan Roberts, Ohad Shacham, Olga L.
> > > Natkovich,
> > > > > > Parth Kamlesh Gandhi, Rajan Dhabalia, Rohini Palaniswamy, Ruby Loo,
> > > > Ryan
> > > > > > Bridges, Sanket Chintapalli, Satish Subhashrao Saley, Shu Kit Chan,
> > > Sri
> > > > > > Harsha Mekala, Susan Hinrichs, Yonatan Gottesman, and many more.
> > > > > >
> > > > > > All of our core developers are committed to learn about the Apache
> > > > process
> > > > > > and to give back to the community.
> > > > > >
> > > > > > === Risk: Homogeneous Developers ===
> > > > > >
> > > > > > The majority of committers in this proposal belong to Yahoo due to
> > > the
> > > > fact
> > > > > > that DataSketches has emerged from an internal Yahoo project. This
> > > > proposal
> > > > > > also includes developers and contributors from other companies, and
> > > > who are
> > > > > > actively involved with other Apache projects, such as Druid.  We
> > > > expect our
> > > > > > entry into incubation will allow us to expand the number of
> > > > individuals and
> > > > > > organizations participating in DataSketches development.
> > > > > >
> > > > > > === Risk: Reliance on Salaried Developers ===
> > > > > >
> > > > > > Because the DataSketches library originated within Yahoo, it has
> > been
> > > > > > developed primarily by salaried Yahoo developers and we expect that
> > > to
> > > > > > continue to be the case near term. However, since we placed this
> > > > library
> > > > > > into open-source we have had a number of significant contributions
> > > from
> > > > > > engineers and scientists from outside of Yahoo. We expect our
> > > reliance
> > > > on
> > > > > > Yahoo salaried developers will decrease over time. Nonetheless,
> > Yahoo
> > > > is
> > > > > > committed to continue its strong support of this important project.
> > > > > >
> > > > > > === Risk: Lack of Relationship to other Apache Products ===
> > > > > >
> > > > > > DataSketches already directly interoperates with or utilizes
> > several
> > > > > > existing Apache projects.
> > > > > >
> > > > > > * Build
> > > > > >    * Apache Maven
> > > > > >
> > > > > > * Integrations and adaptors for the following projects naturally
> > have
> > > > them
> > > > > > as dependencies
> > > > > >    * Apache Hive
> > > > > >    * Apache Pig
> > > > > >    * Apache Druid
> > > > > >    * Apache Spark
> > > > > >
> > > > > > * Additional dependencies for the above integrations and adaptors
> > > > include
> > > > > >    * Apache Hadoop
> > > > > >    * Apache Commons (Math)
> > > > > >
> > > > > > There is no other Apache project that we are aware of that
> > duplicates
> > > > the
> > > > > > functionality of the DataSketches library.
> > > > > >
> > > > > > === Risk: An Excessive Fascination with the Apache Brand ===
> > > > > >
> > > > > > With this proposal we are not seeking attention or publicity.
> > Rather,
> > > > we
> > > > > > firmly believe in the DataSketches library and concept and the
> > > ability
> > > > to
> > > > > > make the DataSketches library a powerful, yet simple-to-use toolkit
> > > for
> > > > > > data processing. While the DataSketches library has been open
> > source,
> > > > we
> > > > > > believe putting code on GitHub can only go so far. We see the
> > Apache
> > > > > > community, processes, and mission as critical for ensuring the
> > > > DataSketches
> > > > > > library is truly community-driven, positively impactful, and
> > > innovative
> > > > > > open source software. While Yahoo has taken a number of steps to
> > > > advance
> > > > > > its various open source projects, we believe the DataSketches
> > library
> > > > > > project is a great fit for the Apache Software Foundation due to
> > its
> > > > focus
> > > > > > on data processing and its relationships to existing ASF projects.
> > > > > >
> > > > > > === Risk: Cryptography ===
> > > > > >
> > > > > > DataSketches does not contain any cryptographic code and is not a
> > > > > > cryptographic product.
> > > > > >
> > > > > > == Documentation ==
> > > > > >
> > > > > > The following documentation is relevant to this proposal. Relevant
> > > > portions
> > > > > > of the documentation will be contributed to the Apache DataSketches
> > > > > > project.
> > > > > >
> > > > > > * DataSketches website: https://datasketches.github.io.
> > > > > >
> > > > > > * DataSketches website repository:
> > > > > > https://github.com/DataSketches/DataSketches.github.io
> > > > > >
> > > > > > We will need an apache website for this documentation similar to
> > > > > >
> > > > > > * https://datasketches.apache.org
> > > > > >
> > > > > > == Initial Source ==
> > > > > >
> > > > > > The initial source for DataSketches which we will submit to the
> > > Apache
> > > > > > Foundation will include a number of repositories which are
> > currently
> > > > hosted
> > > > > > under the GitHub.com/datasketches organization:
> > > > > >
> > > > > > All github.com/datasketches repositories including:
> > > > > >
> > > > > > * Java
> > > > > >    * sketches-core: This repository has the core sketching classes,
> > > > which
> > > > > > are leveraged by some of the other repositories. This repository
> > has
> > > no
> > > > > > external dependencies outside of the DataSketches/memory
> > repository,
> > > > Java
> > > > > > and TestNG for unit tests. This code is versioned and the latest
> > > > release
> > > > > > can be obtained from Maven Central.
> > > > > >    * memory: Low level, high-performance memory data-structure
> > > > management
> > > > > > primarily for off-heap.
> > > > > >    * sketches-android: This is a new repository dedicated to
> > sketches
> > > > > > designed to be run in a mobile client, such as a cell phone. It is
> > > > still in
> > > > > > development and should be considered experimental.
> > > > > >    * sketches-hive: This repository contains Hive UDFs and UDAFs
> > for
> > > > use
> > > > > > within Hadoop grid environments. This code has dependencies on
> > > > > > sketches-core as well as Hadoop and Hive. Users of this code are
> > > > advised to
> > > > > > use Maven to bring in all the required dependencies. This code is
> > > > versioned
> > > > > > and the latest release can be obtained from Maven Central.
> > > > > >    * sketches-pig: This repository contains Pig User Defined
> > > Functions
> > > > > > (UDF) for use within Hadoop grid environments. This code has
> > > > dependencies
> > > > > > on sketches-core as well as Hadoop and Pig. Users of this code are
> > > > advised
> > > > > > to use Maven to bring in all the required dependencies. This code
> > is
> > > > > > versioned and the latest release can be obtained from Maven
> > Central.
> > > > > >    * sketches-vector: This is a new repository dedicated to
> > sketches
> > > > for
> > > > > > vector and matrix operations. It is still somewhat experimental.
> > > > > >    * characterization: This relatively new repository is for code
> > > that
> > > > we
> > > > > > use to characterize the accuracy and speed performance of the
> > > sketches
> > > > in
> > > > > > the library and is constantly being updated. Examples of the job
> > > > command
> > > > > > files used for various tests can be found in the src/main/resources
> > > > > > directory. Some of these tests can run for hours depending on its
> > > > > > configuration.
> > > > > >    * experimental: This repository is an experimental staging area
> > > for
> > > > code
> > > > > > that will eventually end up in another repository. This code is not
> > > > > > versioned and not registered with Maven Central.
> > > > > >    * sketches-misc: Demos and other code not related to production
> > > > > > deployment
> > > > > >
> > > > > > * C++ and Python
> > > > > >    * sketches-core-cpp: This is the C++/Python companion to the
> > Java
> > > > > > sketches-core. These implementations are binary compatible with
> > their
> > > > > > counterparts in Java. In other words, a sketch created and stored
> > in
> > > > C++
> > > > > > can be opened and read in Java and visa-versa. This site also has
> > our
> > > > > > Python adaptors that basically wrap the C++ implementations, making
> > > the
> > > > > > high performance C++ implementations available from Python.
> > > > > >    * sketches-postgres: This site provides the postgres-specific
> > > > adaptors
> > > > > > that wrap the C++ implementations making them available to the
> > > Postgres
> > > > > > database users.
> > > > > >    * characterization-cpp: This is the C++/Python companion to the
> > > Java
> > > > > > characterization repository.
> > > > > >    * experimental-cpp: This repository is an experimental staging
> > > area
> > > > for
> > > > > > C++ code that will eventually end up in another repository.
> > > > > >
> > > > > > * Command-Line Tools
> > > > > >    * sketches-cmd
> > > > > >    * homebrew-sketches
> > > > > >    * homebrew-sketches-cmd
> > > > > >
> > > > > > These projects have always been Apache 2.0 licensed. We intend to
> > > > bundle
> > > > > > all of these repositories since they are all complementary and
> > should
> > > > be
> > > > > > maintained in one project. Prior to our submission, we will combine
> > > > all of
> > > > > > these projects into a new git repository.
> > > > > >
> > > > > > == Source and Intellectual Property Submission Plan ==
> > > > > >
> > > > > > Contributors to the DataSketches project have also signed the Yahoo
> > > > > > Individual Contributor License Agreement (
> > > > https://yahoocla.herokuapp.com/
> > > > > > in order to contribute to the project.
> > > > > >
> > > > > > With respect to trademark rights, Yahoo does not hold a trademark
> > on
> > > > the
> > > > > > phrase “DataSketches.” Based on feedback and guidance we receive
> > > > during the
> > > > > > incubation process, we are open to renaming the project if
> > necessary
> > > > for
> > > > > > trademark or other concerns, but we would prefer not to have to do
> > > > that.
> > > > > >
> > > > > > == External Dependencies ==
> > > > > >
> > > > > > All external dependencies are licensed under an Apache 2.0 or
> > > > > > Apache-compatible license. As we grow the DataSketches community we
> > > > will
> > > > > > configure our build process to require and validate all
> > contributions
> > > > and
> > > > > > dependencies are licensed under the Apache 2.0 license or are under
> > > an
> > > > > > Apache-compatible license.
> > > > > >
> > > > > > == Required Resources ==
> > > > > >
> > > > > > === Mailing Lists ===
> > > > > >
> > > > > > We currently use a mix of mailing lists. We will migrate our
> > existing
> > > > > > mailing lists to the following:
> > > > > >
> > > > > > * dev@datasketches.incubator.apache.org
> > > > > >
> > > > > > * user@datasketches.incubator.apache.org
> > > > > >
> > > > > > * private@datasketches.incubator.apache.org
> > > > > >
> > > > > > * commits@datasketches.incubator.apache.org
> > > > > >
> > > > > > === Source Control ===
> > > > > >
> > > > > > The DataSketches team currently uses Git and would like to continue
> > > to
> > > > do
> > > > > > so. We request a Git repository for DataSketches with mirroring to
> > > > GitHub
> > > > > > enabled similar the following:
> > > > > >
> > > > > > * https://github.com/apache/incubator-datasketches.git
> > > > > >
> > > > > > === Issue Tracking ===
> > > > > >
> > > > > > We request the creation of an Apache-hosted JIRA. The DataSketches
> > > > project
> > > > > > is currently using the public GitHub issue tracker and the public
> > > > Google
> > > > > > Groups forum/sketches-user for issue tracking and discussions. We
> > > will
> > > > > > migrate and combine from these two sources to the Apache JIRA.
> > > > > >
> > > > > > Proposed Jira ID: DATASKETCHES
> > > > > >
> > > > > > == Initial Committers ==
> > > > > >
> > > > > > The following list of individuals have been extremely active in our
> > > > > > community and should have write (commit) permissions to the
> > > repository.
> > > > > >
> > > > > > * Eshcar Hillel                      [eshcar at verizonmedia dot
> > com]
> > > > > >
> > > > > > * Kevin Lang                    [langk at verizonmedia dot com]
> > > > > >
> > > > > > * Roman Leventov              [roman.leventov at c.metamarkets dot
> > > com]
> > > > > >
> > > > > > * Edo Liberty                   [libertye at amazon dot com]
> > > > > >
> > > > > > * Jon Malkin                    [jmalkin at verizonmedia dot com]
> > > > > >
> > > > > > * Lee Rhodes                  [lrhodes at verizonmedia dot com] &
> > > > [leerho
> > > > > > at gmail dot com]
> > > > > >
> > > > > > * Alexander Saydakov         [saydakov at verizonmedia dot com]
> > > > > >
> > > > > > * Justin Thaler                 [justin.thaler at georgetown dot
> > edu]
> > > > > >
> > > > > > == Affiliations ==
> > > > > >
> > > > > > The initial committers are from four organizations: Yahoo, Amazon,
> > > > > > Georgetown University, and Metamarkets/Snap.
> > > > > >
> > > > > > === Champion ===
> > > > > > (Recommended to me: )
> > > > > >
> > > > > > Liang Chen, Vice President of Apache CarbonData, [chenliang613 at
> > > > apache
> > > > > > dot org]
> > > > > > Jean-Baptiste Onofré,[[jb at nanthrax dot net]
> > > > > >
> > > > > > === Nominated Mentors ===
> > > > > > (Recommended to me: )
> > > > > >
> > > > > > Liang Chen, Vice President of Apache CarbonData, [chenliang613 at
> > > > apache
> > > > > > dot org]
> > > > > > Jean-Baptiste Onofré, jb at nanthrax dot net
> > > > > > Gil Yehuda, gyehuda at verizonmedia dot com
> > > > > >
> > > > > > === Sponsoring Entity ===
> > > > > >
> > > > > > * The Apache Incubator    **** This is our 1st choice ****
> > > > > >
> > > > > > * Apache Druid. The incubating Apache Druid project might also be a
> > > > logical
> > > > > > sponsor. However, DataSketches has applications in many areas of
> > > > computing
> > > > > > outside of Druid so our preference and recommendation is that
> > > > DataSketches
> > > > > > would ultimately be a top-level Apache project.
> > > > > >
> > > > > > ________________
> > > > > > [1] In 2017 Verizon acquired Yahoo and merged it with previously
> > > > acquired
> > > > > > AOL. The merged entity was originally called Oath, Inc., but has
> > > > recently
> > > > > > been renamed Verizon Media, Inc., a wholly-owned subsidiary of
> > > Verizon,
> > > > > > Inc.  Since Yahoo is the more recognized name, references in this
> > > > document
> > > > > > to Yahoo, are also a reference to Verizon Media, Inc.
> > > > > >
> > > > > > On Fri, Feb 22, 2019 at 9:35 PM Kenneth Knowles <ke...@apache.org>
> > > > wrote:
> > > > > >
> > > > > > > The subject line has me interested already. Follow examples like
> > > this
> > > > > > > maybe?
> > > > > > >
> > > > > > > 1.
> > > > > > >
> > > > > > >
> > > > > >
> > > >
> > >
> > https://lists.apache.org/thread.html/a5db74cc9e5ae89b3bfa5f4b07bfcc18dae84b7098232fb897cd47b7@%3Cgeneral.incubator.apache.org%3E
> > > > > > > 2.
> > > > > > >
> > > > > > >
> > > > > >
> > > >
> > >
> > https://lists.apache.org/thread.html/5a7f6a218b11a1cac61fbd53f4c995fd7716f8ad3751cf9f171ebd57@%3Cgeneral.incubator.apache.org%3E
> > > > > > >
> > > > > > > Kenn
> > > > > > >
> > > > > > > On Fri, Feb 22, 2019 at 8:05 PM leerho <le...@gmail.com> wrote:
> > > > > > >
> > > > > > > > I'll try again ... :)
> > > > > > > >
> > > > > > > > On Fri, Feb 22, 2019 at 8:00 PM Ted Dunning <
> > > ted.dunning@gmail.com
> > > > >
> > > > > > > wrote:
> > > > > > > >
> > > > > > > >> It didn't make it again
> > > > > > > >>
> > > > > > > >> On Fri, Feb 22, 2019, 8:35 PM leerho <le...@gmail.com>
> > wrote:
> > > > > > > >>
> > > > > > > >> > I'm not sure the attached document made it through.
> > > > > > > >> >
> > > > > > > >> > On Fri, Feb 22, 2019 at 7:28 PM leerho <le...@gmail.com>
> > > > wrote:
> > > > > > > >> >
> > > > > > > >> > >
> > > > > > > >> > >
> > > > > > > >> >
> > > > > > > >>
> > > > > > > >
> > > > > > > >
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> > >
> > --
> > From my cell phone.
> >
> 

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