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From Tommaso Teofili <tommaso.teof...@gmail.com>
Subject Re: [VOTE] Accept MRQL into the Incubator
Date Wed, 06 Mar 2013 17:11:37 GMT
+1

Tommaso


2013/3/6 Alex Karasulu <akarasulu@apache.org>

> +1 (binding)
>
>
> On Wed, Mar 6, 2013 at 7:04 PM, Leonidas Fegaras <fegaras@cse.uta.edu
> >wrote:
>
> > Dear ASF members,
> > I would like to call for a VOTE for acceptance of MRQL into the
> Incubator.
> > The vote will close on Monday March 11, 2013.
> >
> > [ ] +1 Accept MRQL into the Apache incubator
> > [ ] +0 Don't care.
> > [ ] -1 Don't accept MRQL into the incubator because...
> >
> > Full proposal is pasted below and the corresponding wiki is
> >
> > http://wiki.apache.org/**incubator/MRQLProposal<
> http://wiki.apache.org/incubator/MRQLProposal>
> >
> > Only VOTEs from Incubator PMC members are binding,
> > but all are welcome to express their thoughts.
> > Sincerely,
> > Leonidas Fegaras
> >
> >
> > = Abstract =
> >
> > MRQL is a query processing and optimization system for large-scale,
> > distributed data analysis, built on top of Apache Hadoop and Hama.
> >
> > = Proposal =
> >
> > MRQL (pronounced ''miracle'') is a query processing and optimization
> > system for large-scale, distributed data analysis. MRQL (the MapReduce
> > Query Language) is an SQL-like query language for large-scale data
> > analysis on a cluster of computers. The MRQL query processing system
> > can evaluate MRQL queries in two modes: in MapReduce mode on top of
> > Apache Hadoop or in Bulk Synchronous Parallel (BSP) mode on top of
> > Apache Hama. The MRQL query language is powerful enough to express
> > most common data analysis tasks over many forms of raw ''in-situ''
> > data, such as XML and JSON documents, binary files, and CSV
> > documents. MRQL is more powerful than other current high-level
> > MapReduce languages, such as Hive and PigLatin, since it can operate
> > on more complex data and supports more powerful query constructs, thus
> > eliminating the need for using explicit MapReduce code. With MRQL,
> > users will be able to express complex data analysis tasks, such as
> > PageRank, k-means clustering, matrix factorization, etc, using
> > SQL-like queries exclusively, while the MRQL query processing system
> > will be able to compile these queries to efficient Java code.
> >
> > = Background =
> >
> > The initial code was developed at the University of Texas of Arlington
> > (UTA) by a research team, led by Leonidas Fegaras. The software was
> > first released in May 2011. The original goal of this project was to
> > build a query processing system that translates SQL-like data analysis
> > queries to efficient workflows of MapReduce jobs. A design goal was to
> > use HDFS as the physical storage layer, without any indexing, data
> > partitioning, or data normalization, and to use Hadoop (without
> > extensions) as the run-time engine. The motivation behind this work
> > was to build a platform to test new ideas on query processing and
> > optimization techniques applicable to the MapReduce framework.
> >
> > A year ago, MRQL was extended to run on Hama. The motivation for this
> > extension was that Hadoop MapReduce jobs were required to read their
> > input and write their output on HDFS. This simplifies reliability and
> > fault tolerance but it imposes a high overhead to complex MapReduce
> > workflows and graph algorithms, such as PageRank, which require
> > repetitive jobs. In addition, Hadoop does not preserve data in memory
> > across consecutive MapReduce jobs. This restriction requires to read
> > data at every step, even when the data is constant. BSP, on the other
> > hand, does not suffer from this restriction, and, under certain
> > circumstances, allows complex repetitive algorithms to run entirely in
> > the collective memory of a cluster. Thus, the goal was to be able to
> > run the same MRQL queries in both modes, MapReduce and BSP, without
> > modifying the queries: If there are enough resources available, and
> > low latency and speed are more important than resilience, queries may
> > run in BSP mode; otherwise, the same queries may run in MapReduce
> > mode. BSP evaluation was found to be a good choice when fault
> > tolerance is not critical, data (both input and intermediate) can fit
> > in the cluster memory, and data processing requires complex/repetitive
> > steps.
> >
> > The research results of this ongoing work have already been published
> > in conferences (WebDB'11, EDBT'12, and DataCloud'12) and the authors
> > have already received positive feedback from researchers in academia
> > and industry who were attending these conferences.
> >
> > = Rationale =
> >
> > * MRQL will be the first general-purpose, SQL-like query language for
> > data analysis based on BSP.
> > Currently, many programmers prefer to code their MapReduce
> > applications in a higher-level query language, rather than an
> > algorithmic language. For instance, Pig is used for 60% of Yahoo
> > MapReduce jobs, while Hive is used for 90% of Facebook MapReduce
> > jobs. This, we believe, will also be the trend for BSP applications,
> > because, even though, in principle, the BSP model is very simple to
> > understand, it is hard to develop, optimize, and maintain non-trivial
> > BSP applications coded in a general-purpose programming
> > language. Currently, there is no widely acceptable declarative BSP
> > query language, although there are a few special-purpose BSP systems
> > for graph analysis, such as Google Pregel and Apache Giraph, for
> > machine learning, such as BSML, and for scientific data analysis.
> >
> > * MRQL can capture many complex data analysis algorithms in
> > declarative form.
> > Existing MapReduce query languages, such as HiveQL and PigLatin,
> > provide a limited syntax for operating on data collections, in the
> > form of relational joins and group-bys. Because of these limitations,
> > these languages enable users to plug-in custom MapReduce scripts into
> > their queries for those jobs that cannot be declaratively coded in
> > their query language. This nullifies the benefits of using a
> > declarative query language and may result to suboptimal, error-prone,
> > and hard-to-maintain code. More importantly, these languages are
> > inappropriate for complex scientific applications and graph analysis,
> > because they do not directly support iteration or recursion in
> > declarative form and are not able to handle complex, nested scientific
> > data, which are often semi-structured. Furthermore, current MapReduce
> > query processors apply traditional query optimization techniques that
> > may be suboptimal in a MapReduce or BSP environment.
> >
> > * The MRQL design is modular, with pluggable distributed processing
> > back-ends, query languages, and data formats.
> > MRQL aims to be both powerful and adaptable. Although Hadoop is
> > currently the most popular framework for large-scale data analysis,
> > there are a few alternatives that are currently shaping form,
> > including frameworks based on BSP (eg, Giraph, Pregel, Hama), MPI
> > (eg, OpenMPI), etc. MRQL was designed in such a way so that it will
> > be easy to support other distributed processing frameworks in the
> > future. As an evidence of this claim, the MRQL processor required
> > only 2K extra lines of Java code to support BSP evaluation.
> >
> > = Initial Goals =
> >
> > Some current goals include:
> >
> > * apply MRQL to graph analysis problems, such as k-means clustering
> > and PageRank
> >
> > * apply MRQL to large-scale scientific analysis (develop general
> > optimization techniques that can apply to matrix multiplication,
> > matrix factorization, etc)
> >
> > * process additional data formats, such as Avro, and column-based
> > stores, such as HBase
> >
> > * map MRQL to additional distributed processing frameworks, such as
> > Spark and OpenMPI
> >
> > * extend the front-end to process more query languages, such as
> > standard SQL, SPARQL, XQuery, and PigLatin
> >
> > = Current Status =
> >
> > The current MRQL release (version 0.8.10) is a beta release. It is
> > built on top of Hadoop and Hama (no extensions are needed). It
> > currently works on Hadoop up to 1.0.4 (but not on Yarn yet) and Hama
> > 0.5.0. It has only been tested on a small cluster of 20 nodes (80
> > cores).
> >
> > == Meritocracy ==
> >
> > The initial MRQL code base was developed by Leonidas Fegaras in May
> > 2011, and was continuously improved throughout the years. We will
> > reach out other potential contributors through open forums. We plan
> > to do everything possible to encourage an environment that supports a
> > meritocracy, where contributors will extend their privileges based on
> > their contribution. MRQL's modular design will facilitate the
> > strategic extensions to various modules, such as adding a standard-SQL
> > interface, introducing new optimization techniques, etc.
> >
> > == Community ==
> >
> > The interest in open-source query processing systems for analyzing
> > large datasets has been steadily increased in the last few years.
> > Related Apache projects have already attracted a very large community
> > from both academia and industry. We expect that MRQL will also
> > establish an active community. Several researchers from both academia
> > and industry who are interested in using our code have already
> > contacted us.
> >
> > == Core Developers ==
> >
> > The initial core developer was Leonidas Fegaras, who wrote the
> > majority of the code. He is an associate professor at UTA, with
> > interests in cloud computing, databases, web technologies, and
> > functional programming. He has an extensive knowledge and working
> > experience in building complex query processing systems for databases,
> > and compilers for functional and algorithmic programming languages.
> >
> > == Alignment ==
> >
> > MRQL is built on top of two Apache projects: Hadoop and Hama. We have
> > plans to incorporate other products from the Hadoop ecosystem, such as
> > Avro and HBase. MRQL can serve as a testbed for fine-tuning and
> > evaluating the performance of the Apache Hama system. Finally, the
> > MRQL query language and processor can be used by Apache Drill as a
> > pluggable query language.
> >
> > = Known Risks =
> >
> > == Orphaned Products ==
> >
> > The initial committer is from academia, which may be a risk, since
> > research in academia is publication-driven, rather than
> > product-driven. It happens very often in academic research, when a
> > project becomes outdated and doesn't produce publishable results, to
> > be abandoned in favor of new cutting-edge projects. We do not believe
> > that this will be the case for MRQL for the years to come, because it
> > can be adapted to support new query languages, new optimization
> > techniques, and new distributed back-ends, thus sustaining enough
> > research interest. Another risk is that, when graduate students who
> > write code graduate, they may leave their work undocumented and
> > unfinished. We will strive to gain enough momentum to recruit
> > additional committers from industry in order to eliminate these risks.
> >
> > == Inexperience with Open Source ==
> >
> > The initial developer has been involved with various projects whose
> > source code has been released under open source license, but he has no
> > prior experience on contributing to open-source projects. With the
> > guidance from other more experienced committers and participants, we
> > expect that the meritocracy rules will have a positive influence on
> > this project.
> >
> > == Homogeneous Developers ==
> >
> > The initial committer comes from academia. However, given the interest
> > we have seen in the project, we expect the diversity to improve in the
> > near future.
> >
> > == Reliance on Salaried Developers ==
> >
> > Currently, the MRQL code was developed on the committer's volunteer
> > time. In the future, UTA graduate students who will do some of the
> > coding may be supported by UTA and funding agencies, such as NSF.
> >
> > == Relationships with Other Apache Products ==
> >
> > MRQL has some overlapping functionality with Hive and Tajo, which are
> > Data Warehouse systems for Hadoop, and with Drill, which is an
> > interactive data analysis system that can process nested data. MRQL
> > has a more powerful data model, in which any form of nested data, such
> > as XML and JSON, can be defined as a user-defined datatype. More
> > importantly, complex data analysis tasks, such as PageRank, k-means
> > clustering, and matrix multiplication and factorization, can be
> > expressed as short SQL-like queries, while the MRQL system is able to
> > evaluate these queries efficiently. Furthermore, the MRQL system can
> > run these queries in BSP mode, in addition to MapReduce mode, thus
> > achieving low latency and speed, which are also Drill's goals.
> > Nevertheless, we will welcome and encourage any help from these
> > projects and we will be eager to make contributions to these projects
> > too.
> >
> > == An Excessive Fascination with the Apache Brand ==
> >
> > The Apache brand is likely to help us find contributors and reach out
> > to the open-source community. Nevertheless, since MRQL depends on
> > Apache projects (Hadoop and Hama), it makes sense to have our software
> > available as part of this ecosystem.
> >
> > = Documentation =
> >
> > Information about MRQL can be found at http://lambda.uta.edu/mrql/
> >
> > = Initial Source =
> >
> > The initial MRQL code has been released as part of a research project
> > developed at the University of Texas at Arlington under the Apache 2.0
> > license for the past two years. The source code is currently hosted
> > on GitHub at: https://github.com/fegaras/**mrql<
> https://github.com/fegaras/mrql>MRQL’s release artifact
> > would consist of a single tarball of packaging and test code.
> >
> > = External Dependencies =
> >
> > The MRQL source code is already licensed under the Apache License,
> > Version 2.0. MRQL uses JLine which is distributed under the BSD
> > license.
> >
> > = Cryptography =
> >
> > Not applicable.
> >
> > = Required Resources =
> >
> > == Mailing Lists ==
> >
> > * mrql-private
> > * mrql-dev
> > * mrql-user
> >
> > == Subversion Directory ==
> >
> > * Git is the preferred source control system:
> > git://git.apache.org/mrql
> >
> > == Issue Tracking ==
> >
> > * A JIRA issue tracker, MRQL
> >
> > == Wiki ==
> >
> >  * Moinmoin wiki, http://wiki.apache.org/mrql
> >
> > = Initial Committers =
> >
> > * Leonidas Fegaras <fegaras AT cse DOT uta DOT edu>
> > * Upa Gupta <upa.gupta AT mavs DOT uta DOT edu>
> > * Edward J. Yoon <edwardyoon AT apache DOT org>
> > * Maqsood Alam <maqsoodalam AT hotmail DOT com>
> > * John Hope <john.hope AT oracle DOT com>
> > * Mark Wall <mark.wall AT oracle DOT com>
> > * Kuassi Mensah <kuassi.mensah AT oracle DOT com>
> > * Ambreesh Khanna <ambreesh.khanna AT oracle DOT com>
> > * Karthik Kambatla <kasha AT cloudera DOT com>
> >
> > = Affiliations =
> >
> > * Leonidas Fegaras (University of Texas at Arlington)
> > * Upa Gupta (University of Texas at Arlington)
> > * Edward J. Yoon (Oracle corp)
> > * Maqsood Alam (Oracle corp)
> > * John Hope (Oracle corp)
> > * Mark Wall (Oracle corp)
> > * Kuassi Mensah (Oracle corp)
> > * Ambreesh Khanna (Oracle corp)
> > * Karthik Kambatla (Cloudera)
> >
> > = Sponsors =
> >
> > == Champion ==
> >
> > * Edward J. Yoon <edwardyoon AT apache DOT org>
> >
> > == Nominated Mentors ==
> >
> > * Alex Karasulu <akarasulu AT apache DOT org>
> > * Edward J. Yoon <edwardyoon AT apache DOT org>
> >
> > == Sponsoring Entity ==
> >
> > Incubator PMC
> >
> >
>
>
> --
> Best Regards,
> -- Alex
>

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