+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
