+1 Tommaso
2013/3/6 Alex Karasulu <akaras...@apache.org> > +1 (binding) > > > On Wed, Mar 6, 2013 at 7:04 PM, Leonidas Fegaras <fega...@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 >
