Dryad- Microsoft's answer to MR

While reading across the internet I came across Microsoft’s version to MapReduce called Dryad- which has been around for some time, but has not generated quite the buzz that Hadoop or MapReduce are doing.

http://research.microsoft.com/en-us/projects/dryadlinq/

DryadLINQ

DryadLINQ is a simple, powerful, and elegant programming environment for writing large-scale data parallel applications running on large PC clusters.

Overview

New! An academic release of Dryad/DryadLINQ is now available for public download.

The goal of DryadLINQ is to make distributed computing on large compute cluster simple enough for every programmers. DryadLINQ combines two important pieces of Microsoft technology: the Dryad distributed execution engine and the .NET Language Integrated Query (LINQ).

Dryad provides reliable, distributed computing on thousands of servers for large-scale data parallel applications. LINQ enables developers to write and debug their applications in a SQL-like query language, relying on the entire .NET library and using Visual Studio.

DryadLINQ translates LINQ programs into distributed Dryad computations:

  • C# and LINQ data objects become distributed partitioned files.
  • LINQ queries become distributed Dryad jobs.
  • C# methods become code running on the vertices of a Dryad job.

DryadLINQ has the following features:

  • Declarative programming: computations are expressed in a high-level language similar to SQL
  • Automatic parallelization: from sequential declarative code the DryadLINQ compiler generates highly parallel query plans spanning large computer clusters. For exploiting multi-core parallelism on each machine DryadLINQ relies on the PLINQ parallelization framework.
  • Integration with Visual Studio: programmers in DryadLINQ take advantage of the comprehensive VS set of tools: Intellisense, code refactoring, integrated debugging, build, source code management.
  • Integration with .Net: all .Net libraries, including Visual Basic, and dynamic languages are available.
  • and
  • Conciseness: the following line of code is a complete implementation of the Map-Reduce computation framework in DryadLINQ:
    • public static IQueryable<R>
      MapReduce<S,M,K,R>(this IQueryable<S> source,
      Expression<Func<S,IEnumerable<M>>> mapper,
      Expression<Func<M,K>> keySelector,
      Expression<Func<K,IEnumerable<M>,R>> reducer)
      {
      return source.SelectMany(mapper).GroupBy(keySelector, reducer);
      }

    and http://research.microsoft.com/en-us/projects/dryad/

    Dryad

    The Dryad Project is investigating programming models for writing parallel and distributed programs to scale from a small cluster to a large data-center.

    Overview

    New! An academic release of DryadLINQ is now available for public download.

    Dryad is an infrastructure which allows a programmer to use the resources of a computer cluster or a data center for running data-parallel programs. A Dryad programmer can use thousands of machines, each of them with multiple processors or cores, without knowing anything about concurrent programming.

    The Structure of Dryad Jobs

    A Dryad programmer writes several sequential programs and connects them using one-way channels. The computation is structured as a directed graph: programs are graph vertices, while the channels are graph edges. A Dryad job is a graph generator which can synthesize any directed acyclic graph. These graphs can even change during execution, in response to important events in the computation.

    Dryad is quite expressive. It completely subsumes other computation frameworks, such as Google’s map-reduce, or the relational algebra. Moreover, Dryad handles job creation and management, resource management, job monitoring and visualization, fault tolerance, re-execution, scheduling, and accounting.

    The Dryad Software Stack

    As a proof of Dryad’s versatility, a rich software ecosystem has been built on top Dryad:

    • SSIS on Dryad executes many instances of SQL server, each in a separate Dryad vertex, taking advantage of Dryad’s fault tolerance and scheduling. This system is currently deployed in a live production system as part of one of Microsoft’s AdCenter log processing pipelines.
    • DryadLINQ generates Dryad computations from the LINQ Language-Integrated Query extensions to C#.
    • The distributed shell is a generalization of the pipe concept from the Unix shell in three ways. If Unix pipes allow the construction of one-dimensional (1-D) process structures, the distributed shell allows the programmer to build 2-D structures in a scripting language. The distributed shell generalizes Unix pipes in three ways:
      1. It allows processes to easily connect multiple file descriptors of each process — hence the 2-D aspect.
      2. It allows the construction of pipes spanning multiple machines, across a cluster.
      3. It virtualizes the pipelines, allowing the execution of pipelines with many more processes than available machines, by time-multiplexing processors and buffering results.
    • Several languages are compiled to distributed shell processes. PSQL is an early version, recently replaced with Scope.

    Publications

    Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks
    Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly
    European Conference on Computer Systems (EuroSys), Lisbon, Portugal, March 21-23, 2007

    Video of a presentation on Dryad at the Google Campus, given by Michael Isard, Nov 1, 2007.

    Also interesting to read-

    Why does Dryad use a DAG?

    he basic computational model we decided to adopt for Dryad is the directed-acyclic graph (DAG). Each node in the graph is a computation, and each edge in the graph is a stream of data traveling in the direction of the edge. The amount of data on any given edge is assumed to be finite, the computations are assumed to be deterministic, and the inputs are assumed to be immutable. This isn’t by any means a new way of structuring a distributed computation (for example Condor had DAGMan long before Dryad came along), but it seemed like a sweet spot in the design space given our other constraints.

    So, why is this a sweet spot? A DAG is very convenient because it induces an ordering on the nodes in the graph. That makes it easy to design scheduling policies, since you can define a node to be ready when its inputs are available, and at any time you can choose to schedule as many ready nodes as you like in whatever order you like, and as long as you always have at least one scheduled you will continue to make progress and never deadlock. It also makes fault-tolerance easy, since given our determinism and immutability assumptions you can backtrack as far as you want in the DAG and re-execute as many nodes as you like to regenerate intermediate data that has been lost or is unavailable due to cluster failures.

    from

    http://blogs.msdn.com/b/dryad/archive/2010/07/23/why-does-dryad-use-a-dag.aspx

      IPSUR – A Free R Textbook

      Here is a free R textbook called IPSUR-

      http://ipsur.r-forge.r-project.org/book/index.php

      IPSUR stands for Introduction to Probability and Statistics Using R, ISBN: 978-0-557-24979-4, which is a textbook written for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra in a few places. Attendees of the class include mathematics, engineering, and computer science majors.

      IPSUR is FREE, in the GNU sense of the word. Hard copies are available for purchase here from Lulu and will be available (coming soon) from the other standard online retailers worldwide. The price of the book is exactly the manufacturing cost plus the retailers’ markup. You may be able to get it even cheaper by downloading an electronic copy and printing it yourself, but if you elect this route then be sure to get the publisher-quality PDF from theDownloads page. And double check the price. It was cheaper for my students to buy a perfect-bound paperback from Lulu and have it shipped to their door than it was to upload the PDF to Fed-Ex Kinkos and Xerox a coil-bound copy (and on top of that go pick it up at the store).

      If you are going to buy from anywhere other than Lulu then be sure to check the time-stamp on the copyright page. There is a 6 to 8 week delay from Lulu to Amazon and you may not be getting the absolute latest version available.

      Refer to the Installation page for instructions to install an electronic copy of IPSUR on your personal computer. See the Feedback page for guidance about questions or comments you may have about IPSUR.

      Also see http://ipsur.r-forge.r-project.org/rcmdrplugin/index.php for the R Cmdr Plugin

      This plugin for the R Commander accompanies the text Introduction to Probability and Statistics Using R by G. Jay Kerns. The plugin contributes functions unique to the book as well as specific configuration and functionality to R Commander, the pioneering work by John Fox of McMaster University.

      RcmdrPlugin.IPSUR’s primary goal is to provide a user-friendly graphical user interface (GUI) to the open-source and freely available R statistical computing environment. RcmdrPlugin.IPSUR is equipped to handle many of the statistical analyses and graphical displays usually encountered by upper division undergraduate mathematics, statistics, and engineering majors. Available features are comparable to many expensive commercial packages such as Minitab, SPSS, and JMP-IN.

      Since the audience of RcmdrPlugin.IPSUR is slightly different than Rcmdr’s, certain functionality has been added and selected error-checks have been disabled to permit the student to explore alternative regions of the statistical landscape. The resulting benefit of increased flexibility is balanced by somewhat increased vulnerability to syntax errors and misuse; the instructor should keep this and the academic audience in mind when usingRcmdrPlugin.IPSUR in the classroom

      Towards better analytical software

      Here are some thoughts on using existing statistical software for better analytics and/or business intelligence (reporting)-

      1) User Interface Design Matters- Most stats software have a legacy approach to user interface design. While the Graphical User Interfaces need to more business friendly and user friendly- example you can call a button T Test or You can call it Compare > Means of Samples (with a highlight called T Test). You can call a button Chi Square Test or Call it Compare> Counts Data. Also excessive reliance on drop down ignores the next generation advances in OS- namely touchscreen instead of mouse click and point.

      Given the fact that base statistical procedures are the same across softwares, a more thoughtfully designed user interface (or revamped interface) can give softwares an edge over legacy designs.

      2) Branding of Software Matters- One notable whine against SAS Institite products is a premier price. But really that software is actually inexpensive if you see other reporting software. What separates a Cognos from a Crystal Reports to a SAS BI is often branding (and user interface design). This plays a role in branding events – social media is often the least expensive branding and marketing channel. Same for WPS and Revolution Analytics.

      3) Alliances matter- The alliances of parent companies are reflected in the sales of bundled software. For a complete solution , you need a database plus reporting plus analytical software. If you are not making all three of the above, you need to partner and cross sell. Technically this means that software (either DB, or Reporting or Analytics) needs to talk to as many different kinds of other softwares and formats. This is why ODBC in R is important, and alliances for small companies like Revolution Analytics, WPS and Netezza are just as important as bigger companies like IBM SPSS, SAS Institute or SAP. Also tie-ins with Hadoop (like R and Netezza appliance)  or  Teradata and SAS help create better usage.

      4) Cloud Computing Interfaces could be the edge- Maybe cloud computing is all hot air. Prudent business planing demands that any software maker in analytics or business intelligence have an extremely easy to load interface ( whether it is a dedicated on demand website) or an Amazon EC2 image. Easier interfaces win and with the cloud still in early stages can help create an early lead. For R software makers this is critical since R is bad in PC usage for larger sets of data in comparison to counterparts. On the cloud that disadvantage vanishes. An easy to understand cloud interface framework is here ( its 2 years old but still should be okay) http://knol.google.com/k/data-mining-through-cloud-computing#

      5) Platforms matter- Softwares should either natively embrace all possible platforms or bundle in middle ware themselves.

      Here is a case study SAS stopped supporting Apple OS after Base SAS 7. Today Apple OS is strong  ( 3.47 million Macs during the most recent quarter ) and the only way to use SAS on a Mac is to do either

      http://goo.gl/QAs2

      or do a install of Ubuntu on the Mac ( https://help.ubuntu.com/community/MacBook ) and do this

      http://ubuntuforums.org/showthread.php?t=1494027

      Why does this matter? Well SAS is free to academics and students  from this year, but Mac is a preferred computer there. Well WPS can be run straight away on the Mac (though they are curiously not been able to provide academics or discounted student copies 😉 ) as per

      http://goo.gl/aVKu

      Does this give a disadvantage based on platform. Yes. However JMP continues to be supported on Mac. This is also noteworthy given the upcoming Chromium OS by Google, Windows Azure platform for cloud computing.

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