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From Alex Karasulu <>
Subject Re: [VOTE] Accept MRQL into the Incubator
Date Wed, 06 Mar 2013 17:10:23 GMT
+1 (binding)

On Wed, Mar 6, 2013 at 7:04 PM, Leonidas Fegaras <>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
> 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
> = 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:**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://
> == Issue Tracking ==
> * A JIRA issue tracker, MRQL
> == Wiki ==
>  * Moinmoin wiki,
> = 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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