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From Ted Dunning <>
Subject [VOTE] Accept Drill into the Apache Incubator
Date Wed, 08 Aug 2012 02:41:23 GMT
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!


= 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
BigQuery]], which we call DrQL. However, Drill is designed to support
other languages and programming models, such as the
[[|Mongo Query
Language]], [[|Cascading]] or
 * 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
 * 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

== 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 ===

== 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
 * Grant Ingersoll <grant at lucidimagination dot com> – Chief
Scientist at Lucid Imagination, Committer for Lucene, Mahout and other
 * Isabel Drost <isabel at apache dot org> – Software Developer at
Nokia Gate 5 GmbH, Committer for Lucene, Mahout and other projects.

=== Sponsoring Entity ===

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