great proposal and a very promising mentor lineup. Have fun,
Bernd On Thu, Aug 2, 2012 at 11:40 PM, Ted Dunning <tdunn...@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 --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
