Hi Kenneth Please try this link : https://docs.google.com/document/d/1_cnesVLtKqPeUYxJvsd_2MTFwgeC1wUqI6cDPCbBRSM/edit#heading=h.97rxea60t2yw
Regards Liang Kenneth Knowles wrote > I could not access that document. I suggest you need to turn on link > sharing. > > Kenn > > On Mon, Feb 25, 2019 at 12:00 PM > leerho@ > < > leerho@ > > wrote: > >> Try this link: >> https://docs.google.com/document/d/19JKevzFQNcaLA51LFLUlP1hzdFDW7oDJrJO8N6weDv8/edit?usp=sharing >> >> >> On 2019/02/25 05:55:50, leerho < > leerho@ > > wrote: >> > Yes I will try that tomorrow. >> > >> > On Sun, Feb 24, 2019 at 7:34 PM Kenneth Knowles < > kenn@ > > 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@ > < > leerho@ > > >> > > 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 < > kenn@ > > 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@ > > 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@.apache >> > > > > > >> > > > > > * > user@.apache >> > > > > > >> > > > > > * > private@.apache >> > > > > > >> > > > > > * > commits@.apache >> > > > > > >> > > > > > === 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@ > > > >> > > > 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@ > > >> wrote: >> > > > > > > >> > > > > > > > I'll try again ... :) >> > > > > > > > >> > > > > > > > On Fri, Feb 22, 2019 at 8:00 PM Ted Dunning < >> > > > ted.dunning@ >> > > > > >> > > > > > > wrote: >> > > > > > > > >> > > > > > > >> It didn't make it again >> > > > > > > >> >> > > > > > > >> On Fri, Feb 22, 2019, 8:35 PM leerho < > leerho@ > > >> wrote: >> > > > > > > >> >> > > > > > > >> > I'm not sure the attached document made it through. >> > > > > > > >> > >> > > > > > > >> > On Fri, Feb 22, 2019 at 7:28 PM leerho < > leerho@ > > >> > > > wrote: >> > > > > > > >> > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > >> > > > > > > >> >> > > > > > > > >> > > > > > > > >> > > > >> --------------------------------------------------------------------- >> > > > > > > > To unsubscribe, e-mail: >> > general-unsubscribe@.apache >> > > > > > > > For additional commands, e-mail: >> > > > general-help@.apache >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > > >> --------------------------------------------------------------------- >> > > > To unsubscribe, e-mail: > general-unsubscribe@.apache >> > > > For additional commands, e-mail: > general-help@.apache >> > > > >> > > > >> > > >> > -- >> > From my cell phone. >> > >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: > general-unsubscribe@.apache >> For additional commands, e-mail: > general-help@.apache >> >> -- Sent from: http://apache-incubator-general.996316.n3.nabble.com/ --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
