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From "Franklin, Matthew B." <mfrank...@mitre.org>
Subject RE: [VOTE] Accept Drill into the Apache Incubator
Date Wed, 08 Aug 2012 19:36:52 GMT
+1 (binding)

>-----Original Message-----
>From: Ted Dunning [mailto:ted.dunning@gmail.com]
>Sent: Tuesday, August 07, 2012 10:41 PM
>To: general@incubator.apache.org
>Subject: [VOTE] Accept Drill into the Apache Incubator
>
>I would like to call a vote for accepting Drill for incubation in the
>Apache Incubator. The full proposal is available below.  Discussion
>over the last few days has been quite positive.
>
>Please cast your vote:
>
>[ ] +1, bring Drill into Incubator
>[ ] +0, I don't care either way,
>[ ] -1, do not bring Drill into Incubator, because...
>
>This vote will be open for 72 hours and only votes from the Incubator
>PMC are binding.  The start of the vote is just before 3AM UTC on 8
>August so the closing time will be 3AM UTC on 11 August.
>
>Thank you for your consideration!
>
>Ted
>
>http://wiki.apache.org/incubator/DrillProposal
>
>= Drill =
>
>== Abstract ==
>Drill is a distributed system for interactive analysis of large-scale
>datasets, inspired by
>[[http://research.google.com/pubs/pub36632.html|Google's Dremel]].
>
>== Proposal ==
>Drill is a distributed system for interactive analysis of large-scale
>datasets. Drill is similar to Google's Dremel, with the additional
>flexibility needed to support a broader range of query languages, data
>formats and data sources. It is designed to efficiently process nested
>data. It is a design goal to scale to 10,000 servers or more and to be
>able to process petabyes of data and trillions of records in seconds.
>
>== Background ==
>Many organizations have the need to run data-intensive applications,
>including batch processing, stream processing and interactive
>analysis. In recent years open source systems have emerged to address
>the need for scalable batch processing (Apache Hadoop) and stream
>processing (Storm, Apache S4). In 2010 Google published a paper called
>"Dremel: Interactive Analysis of Web-Scale Datasets," describing a
>scalable system used internally for interactive analysis of nested
>data. No open source project has successfully replicated the
>capabilities of Dremel.
>
>== Rationale ==
>There is a strong need in the market for low-latency interactive
>analysis of large-scale datasets, including nested data (eg, JSON,
>Avro, Protocol Buffers). This need was identified by Google and
>addressed internally with a system called Dremel.
>
>In recent years open source systems have emerged to address the need
>for scalable batch processing (Apache Hadoop) and stream processing
>(Storm, Apache S4). Apache Hadoop, originally inspired by Google's
>internal MapReduce system, is used by thousands of organizations
>processing large-scale datasets. Apache Hadoop is designed to achieve
>very high throughput, but is not designed to achieve the sub-second
>latency needed for interactive data analysis and exploration. Drill,
>inspired by Google's internal Dremel system, is intended to address
>this need.
>
>It is worth noting that, as explained by Google in the original paper,
>Dremel complements MapReduce-based computing. Dremel is not intended
>as a replacement for MapReduce and is often used in conjunction with
>it to analyze outputs of MapReduce pipelines or rapidly prototype
>larger computations. Indeed, Dremel and MapReduce are both used by
>thousands of Google employees.
>
>Like Dremel, Drill supports a nested data model with data encoded in a
>number of formats such as JSON, Avro or Protocol Buffers. In many
>organizations nested data is the standard, so supporting a nested data
>model eliminates the need to normalize the data. With that said, flat
>data formats, such as CSV files, are naturally supported as a special
>case of nested data.
>
>The Drill architecture consists of four key components/layers:
> * Query languages: This layer is responsible for parsing the user's
>query and constructing an execution plan.  The initial goal is to
>support the SQL-like language used by Dremel and
>[[https://developers.google.com/bigquery/docs/query-reference|Google
>BigQuery]], which we call DrQL. However, Drill is designed to support
>other languages and programming models, such as the
>[[http://www.mongodb.org/display/DOCS/Mongo+Query+Language|Mongo
>Query
>Language]], [[http://www.cascading.org/|Cascading]] or
>[[https://github.com/tdunning/Plume|Plume]].
> * Low-latency distributed execution engine: This layer is responsible
>for executing the physical plan. It provides the scalability and fault
>tolerance needed to efficiently query petabytes of data on 10,000
>servers. Drill's execution engine is based on research in distributed
>execution engines (eg, Dremel, Dryad, Hyracks, CIEL, Stratosphere) and
>columnar storage, and can be extended with additional operators and
>connectors.
> * Nested data formats: This layer is responsible for supporting
>various data formats. The initial goal is to support the column-based
>format used by Dremel. Drill is designed to support schema-based
>formats such as Protocol Buffers/Dremel, Avro/AVRO-806/Trevni and CSV,
>and schema-less formats such as JSON, BSON or YAML. In addition, it is
>designed to support column-based formats such as Dremel,
>AVRO-806/Trevni and RCFile, and row-based formats such as Protocol
>Buffers, Avro, JSON, BSON and CSV. A particular distinction with Drill
>is that the execution engine is flexible enough to support
>column-based processing as well as row-based processing. This is
>important because column-based processing can be much more efficient
>when the data is stored in a column-based format, but many large data
>assets are stored in a row-based format that would require conversion
>before use.
> * Scalable data sources: This layer is responsible for supporting
>various data sources. The initial focus is to leverage Hadoop as a
>data source.
>
>It is worth noting that no open source project has successfully
>replicated the capabilities of Dremel, nor have any taken on the
>broader goals of flexibility (eg, pluggable query languages, data
>formats, data sources and execution engine operators/connectors) that
>are part of Drill.
>
>== Initial Goals ==
>The initial goals for this project are to specify the detailed
>requirements and architecture, and then develop the initial
>implementation including the execution engine and DrQL.
>Like Apache Hadoop, which was built to support multiple storage
>systems (through the FileSystem API) and file formats (through the
>InputFormat/OutputFormat APIs), Drill will be built to support
>multiple query languages, data formats and data sources. The initial
>implementation of Drill will support the DrQL and a column-based
>format similar to Dremel.
>
>== Current Status ==
>Significant work has been completed to identify the initial
>requirements and define the overall system architecture. The next step
>is to implement the four components described in the Rationale
>section, and we intend to do that development as an Apache project.
>
>=== Meritocracy ===
>We plan to invest in supporting a meritocracy. We will discuss the
>requirements in an open forum. Several companies have already
>expressed interest in this project, and we intend to invite additional
>developers to participate. We will encourage and monitor community
>participation so that privileges can be extended to those that
>contribute. Also, Drill has an extensible/pluggable architecture that
>encourages developers to contribute various extensions, such as query
>languages, data formats, data sources and execution engine operators
>and connectors. While some companies will surely develop commercial
>extensions, we also anticipate that some companies and individuals
>will want to contribute such extensions back to the project, and we
>look forward to fostering a rich ecosystem of extensions.
>
>=== Community ===
>The need for a system for interactive analysis of large datasets in
>the open source is tremendous, so there is a potential for a very
>large community. We believe that Drill's extensible architecture will
>further encourage community participation. Also, related Apache
>projects (eg, Hadoop) have very large and active communities, and we
>expect that over time Drill will also attract a large community.
>
>=== Core Developers ===
>The developers on the initial committers list include experienced
>distributed systems engineers:
> * Tomer Shiran has experience developing distributed execution
>engines. He developed Parallel DataSeries, a data-parallel version of
>the open source [[http://tesla.hpl.hp.com/opensource/|DataSeries]]
>system. He is also the author of Applying Idealized Lower-bound
>Runtime Models to Understand Inefficiencies in Data-intensive
>Computing (SIGMETRICS 2011). Tomer worked as a software developer and
>researcher at IBM Research, Microsoft and HP Labs, and is now at MapR
>Technologies. He has been active in the Hadoop community since 2009.
> * Jason Frantz was at Clustrix, where he designed and developed the
>first scale-out SQL database based on MySQL. Jason developed the
>distributed query optimizer that powered Clustrix. He is now a
>software engineer and architect at MapR Technologies.
> * Ted Dunning is a PMC member for Apache ZooKeeper and Apache Mahout,
>and has a history of over 30 years of contributions to open source. He
>is now at MapR Technologies. Ted has been very active in the Hadoop
>community since the project's early days.
> * MC Srivas is the co-founder and CTO of MapR Technologies. While at
>Google he worked on Google's scalable search infrastructure. MC Srivas
>has been active in the Hadoop community since 2009.
> * Chris Wensel is the founder and CEO of Concurrent. Prior to
>founding Concurrent, he developed Cascading, an Apache-licensed open
>source application framework enabling Java developers to quickly and
>easily develop robust Data Analytics and Data Management applications
>on Apache Hadoop. Chris has been involved in the Hadoop community
>since the project's early days.
> * Keys Botzum was at IBM, where he worked on security and distributed
>systems, and is currently at MapR Technologies.
> * Gera Shegalov was at Oracle, where he worked on networking, storage
>and database kernels, and is currently at MapR Technologies.
> * Ryan Rawson is the VP Engineering of Drawn to Scale where he
>developed Spire, a real-time operational database for Hadoop. He is
>also a committer and PMC member for Apache HBase, and has a long
>history of contributions to open source. Ryan has been involved in the
>Hadoop community since the project's early days.
>
>We realize that additional employer diversity is needed, and we will
>work aggressively to recruit developers from additional companies.
>
>=== Alignment ===
>The initial committers strongly believe that a system for interactive
>analysis of large-scale datasets will gain broader adoption as an open
>source, community driven project, where the community can contribute
>not only to the core components, but also to a growing collection of
>query languages and optimizers, data formats, data formats, and
>execution engine operators and connectors. Drill will integrate
>closely with Apache Hadoop. First, the data will live in Hadoop. That
>is, Drill will support Hadoop FileSystem implementations and HBase.
>Second, Hadoop-related data formats will be supported (eg, Apache
>Avro, RCFile). Third, MapReduce-based tools will be provided to
>produce column-based formats. Fourth, Drill tables can be registered
>in HCatalog. Finally, Hive is being considered as the basis of the
>DrQL implementation.
>
>== Known Risks ==
>
>=== Orphaned Products ===
>The contributors are leading vendors in this space, with significant
>open source experience, so the risk of being orphaned is relatively
>low. The project could be at risk if vendors decided to change their
>strategies in the market. In such an event, the current committers
>plan to continue working on the project on their own time, though the
>progress will likely be slower. We plan to mitigate this risk by
>recruiting additional committers.
>
>=== Inexperience with Open Source ===
>The initial committers include veteran Apache members (committers and
>PMC members) and other developers who have varying degrees of
>experience with open source projects. All have been involved with
>source code that has been released under an open source license, and
>several also have experience developing code with an open source
>development process.
>
>=== Homogenous Developers ===
>The initial committers are employed by a number of companies,
>including MapR Technologies, Concurrent and Drawn to Scale. We are
>committed to recruiting additional committers from other companies.
>
>=== Reliance on Salaried Developers ===
>It is expected that Drill development will occur on both salaried time
>and on volunteer time, after hours. The majority of initial committers
>are paid by their employer to contribute to this project. However,
>they are all passionate about the project, and we are confident that
>the project will continue even if no salaried developers contribute to
>the project. We are committed to recruiting additional committers
>including non-salaried developers.
>
>=== Relationships with Other Apache Products ===
>As mentioned in the Alignment section, Drill is closely integrated
>with Hadoop, Avro, Hive and HBase in a numerous ways. For example,
>Drill data lives inside a Hadoop environment (Drill operates on in
>situ data). We look forward to collaborating with those communities,
>as well as other Apache communities.
>
>=== An Excessive Fascination with the Apache Brand ===
>Drill solves a real problem that many organizations struggle with, and
>has been proven within Google to be of significant value. The
>architecture is based on academic and industry research. Our rationale
>for developing Drill as an Apache project is detailed in the Rationale
>section. We believe that the Apache brand and community process will
>help us attract more contributors to this project, and help establish
>ubiquitous APIs. In addition, establishing consensus among users and
>developers of a Dremel-like tool is a key requirement for success of
>the project.
>
>== Documentation ==
>Drill is inspired by Google's Dremel. Google has published a
>[[http://research.google.com/pubs/pub36632.html|paper]] highlighting
>Dremel's innovative nested column-based data format and execution
>engine.
>
>== Initial Source ==
>The requirement and design documents are currently stored in MapR
>Technologies' source code repository. They will be checked in as part
>of the initial code dump.
>
>== Cryptography ==
>Drill will eventually support encryption on the wire. This is not one
>of the initial goals, and we do not expect Drill to be a controlled
>export item due to the use of encryption.
>
>== Required Resources ==
>
>=== Mailing List ===
> * drill-private
> * drill-dev
> * drill-user
>
>=== Subversion Directory ===
>Git is the preferred source control system: git://git.apache.org/drill
>
>=== Issue Tracking ===
>JIRA Drill (DRILL)
>
>== Initial Committers ==
> * Tomer Shiran <tshiran at maprtech dot com>
> * Ted Dunning <tdunning at apache dot org>
> * Jason Frantz <jfrantz at maprtech dot com>
> * MC Srivas <mcsrivas at maprtech dot com>
> * Chris Wensel <chris and concurrentinc dot com>
> * Keys Botzum <kbotzum at maprtech dot com>
> * Gera Shegalov <gshegalov at maprtech dot com>
> * Ryan Rawson <ryan at drawntoscale dot com>
>
>== Affiliations ==
>The initial committers are employees of MapR Technologies, Drawn to
>Scale and Concurrent. The nominated mentors are employees of MapR
>Technologies, Lucid Imagination and Nokia.
>
>== Sponsors ==
>
>=== Champion ===
>Ted Dunning (tdunning at apache dot org)
>
>=== Nominated Mentors ===
> * Ted Dunning <tdunning at apache dot org> – Chief Application
>Architect at MapR Technologies, Committer for Lucene, Mahout and
>ZooKeeper.
> * Grant Ingersoll <grant at lucidimagination dot com> – Chief
>Scientist at Lucid Imagination, Committer for Lucene, Mahout and other
>projects.
> * Isabel Drost <isabel at apache dot org> – Software Developer at
>Nokia Gate 5 GmbH, Committer for Lucene, Mahout and other projects.
>
>=== Sponsoring Entity ===
>Incubator
>
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