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From karthik tunga <karthik.tu...@gmail.com>
Subject Re: [PROPOSAL] Drill for the Apache Incubator
Date Thu, 16 Aug 2012 07:09:22 GMT
The proposal looks great. I was wondering what operations will drill
support ?
For example the dremel paper doesn't talk about joins, will drill support
joins ?

Sorry if I missed it, is there a dev mailing list I could subscribe to ?

Cheers,
Karthik

On 13 August 2012 23:55, Bernd Fondermann <bernd.fondermann@gmail.com>wrote:

> great proposal and a very promising mentor lineup.
>
> Have fun,
>
>   Bernd
>
> On Thu, Aug 2, 2012 at 11:40 PM, Ted Dunning <tdunning@apache.org> wrote:
> > Abstract
> > ========
> > Drill is a distributed system for interactive analysis of large-scale
> > datasets, inspired by Google’s Dremel (
> > http://research.google.com/pubs/pub36632.html).
> >
> > 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 (
> > https://developers.google.com/bigquery/docs/query-reference), which we
> call
> > DrQL. However, Drill is designed to support other languages and
> programming
> > models, such as the Mongo Query Language (
> > http://www.mongodb.org/display/DOCS/Mongo+Query+Language), Cascading (
> > http://www.cascading.org/) or Plume (https://github.com/tdunning/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 (http://tesla.hpl.hp.com/opensource/). 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: http://research.google.com/pubs/pub36632.html
> >
> > High-level slides have been published by MapR: TODO
> >
> > Initial Source
> > ==============
> > There is no initial source code. All source code will be developed within
> > the Apache Incubator.
> >
> > 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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