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From Arun C Murthy <>
Subject Re: [VOTE] Accept Drill into the Apache Incubator
Date Wed, 08 Aug 2012 05:48:58 GMT
+1 (binding)

On Aug 7, 2012, at 7:41 PM, Ted Dunning wrote:

> 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
> = Drill =
> == Abstract ==
> Drill is a distributed system for interactive analysis of large-scale
> datasets, inspired by
> [[|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
> [[|Google
> BigQuery]], which we call DrQL. However, Drill is designed to support
> other languages and programming models, such as the
> [[|Mongo Query
> Language]], [[|Cascading]] or
> [[|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 [[|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
> [[|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://
> === 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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Arun C. Murthy
Hortonworks Inc.

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