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From leerho@gmail.com <lee...@gmail.com>
Subject DataSketches Proposal
Date Sat, 23 Feb 2019 06:21:15 GMT
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 <kenn@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 <leerho@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 <kenn@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 <leerho@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 <leerho@gmail.com> wrote:
> > > >>
> > > >> > I'm not sure the attached document made it through.
> > > >> >
> > > >> > On Fri, Feb 22, 2019 at 7:28 PM leerho <leerho@gmail.com>
wrote:
> > > >> >
> > > >> > >
> > > >> > >
> > > >> >
> > > >>
> > > >
> > > > ---------------------------------------------------------------------
> > > > To unsubscribe, e-mail: general-unsubscribe@incubator.apache.org
> > > > For additional commands, e-mail: general-help@incubator.apache.org
> > >
> >
> 

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