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Pig provides a high-level language for writing SQL-like operations that apply to datasets. The language is named Pig Latin, and the Pig project provides a compiler that produces MapReduce jobs from a Pig Latin script. Pig is not distributed with Hadoop Core, and is mature enough that the project has releases. At the time of writing, Pig 0.2.0 has been released. Pig also provides grunt, an interactive shell, for running Pig Latin commands directly. Cloudera, listed later in this chapter, provides online training for Pig. The site http://www.apache.org/dyn/closer.cgi/hadoop/pig provides the main distribution page. At present, it appears that the stock Pig distribution requires the underlying cluster to run Hadoop 0.17.0 or Hadoop 0.18.0. The setup is as simple as unpacking the distribution and setting the environment variable PIG_CLASSPATH to the directory that contains the hadoop-site.xml file that defines your cluster. The following should work: export PIG_CLASSPATH=${HADOOP_HOME}/conf

The Mahout project aims to build scalable machine learning algorithms. Its plan is to build libraries for the ten machine learning algorithms listed in http://www.cs.stanford.edu/ people/ang//papers/nips06-mapreducemulticore.pdf. As of the time of writing, the first release, 0.1, has been made available for download. The Taste project (a recommendation engine) has become a part of Mahout and is included in the 0.1 release. There is a tutorial available at http://lucene.apache.org/mahout/taste.html. Mahout requires Maven for operation, and it is not clear from the documentation how to run the examples, including the Taste examples, without Maven. Mahout also provides a number of distributed clustering algorithms, including k-means, dirichlet, mean-shift, and canopy. There are also two Bayesian classifiers: the naive and the complementary na ve. An implementation of watchmaker is provided for building evolutionary algorithms and support for matrix and vector operations.

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Opens the session object, acquiring necessary resources. Determines if a specific object is associated with the database. Clears the session of all loaded instances, cancels any saves, updates, or deletions that have not been completed. Retains any iterators that are in use. Disassociates an object from the session so that subsequent changes to it will not be persisted. Flushes all pending changes into the database all saves, updates, and deletions will be carried out; essentially, this synchronizes the session with the database. Determines if the session has been closed. Determines if the database is synchronized with the database.

This will be useful for future fills and strokes At this point, you are free to continue with more segments in your path to create additional subpaths Or you can beginPath at any time to start over and clear the path list entirely As with most complex systems, it is often better to see them in action Let s depart from our line examples and use the HTML5 Canvas API to start to create a new scene that illustrates a forest with a trail-running path This scene will serve as a logo of sorts for our race event And as with any picture, we will start with a basic element, which in this case is the canopy of a simple pine tree Listing 2-8 shows how to draw the pine tree s canopy Listing 2-8.

At the time of writing, Hama is an incubation project. It requires HBase as an underlying storage framework. The project is intended to be used for large-scale numerical analyses and data mining. The project will provide matrix-vector and matrix-matrix multiplication, linear equation solving, tools for working with graphs, data sorting, and methods of finding eigenvalues and eigenvectors. The project is undergoing development and is pre release 0.1.

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