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https://github.com/aseldawy/spatialhadoop2

The second generation of SpatialHadoop that ships as an extension
https://github.com/aseldawy/spatialhadoop2

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The second generation of SpatialHadoop that ships as an extension

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README 

        
IMPORTANT NOTICE

================

SpatialHadoop is no longer maintained. We recommend that you use our most recent system [Beast](https://bitbucket.org/eldawy/beast/src/master/) which is built for Spark and provides the following improvements over SpatialHadoop.

- **More file formats**: Beast supports many standard formats including Esri Shapefile, CSV, GeoJSON, and GPX.

- **Multidimensional data**: Beast inherently supports multidimensional data so it can index and process 3D or higher dimensions.

- **Better indexes**: Beast adopts new indexes including R*-Grove, the most advanced index for big spatial data.

- **Higher performance**: Thanks to the advanced features in Spark.

- **Easier to use**: You can run Beast without any installation on-top of your existing Spark installation.

- **Scalable visualization**: Visualize terabytes of data on an interactive map. Beast powers [UCR-Star](https://star.cs.ucr.edu) the home of terabytes of public geospatial data.

- **Raptor = Raster + Vector**: Beast hosts a novel component for processing raster and vector data concurrently. It is orders of magnitude faster than competetive systems.

- **Active**: Beast is still active in development and new features are regularly added to it.



SpatialHadoop

=============



[SpatialHadoop](http://spatialhadoop.cs.umn.edu) is an extension to Hadoop that provides efficient processing of

spatial data using MapReduce. It provides spatial data types to be used in

MapReduce jobs including point, rectangle and polygon. It also adds low level

spatial indexes in HDFS such as Grid file, R-tree and R+-tree. Some new

InputFormats and RecordReaders are also provided to allow reading and processing

spatial indexes efficiently in MapReduce jobs. SpatialHadoop also ships with

a bunch of spatial operations that are implemented as efficient MapReduce jobs

which access spatial indexes using the new components. Developers can implement

myriad spatial operations that run efficiently using spatial indexes.



How it works

============



SpatialHadoop is used in the same way as Hadoop. Data files are first loaded

into HDFS and indexed using the *index* command which builds a spatial index

of your choice over the input file. Once the file is indexed, you can execute

any of the spatial operations provided in SpatialHadoop such as range query,

k-nearest neighbor and spatial join. New operations are added occasionally

to SpatialHadoop such as polygon union and convex hull.



Install

=======



SpatialHadoop is packaged as a single jar file which contains all the required

classes to run. All operations including building the index can be accessed

through this jar file and it gets automatically distributed to all slave nodes

by the Hadoop framework. In addition the *spatial-site.xml* configuration file

needs to be placed in the *conf* directory of your Hadoop installation. This

allows you to configure the cluster accordingly.



Examples

========



Here are a few examples of how to use SpatialHadoop.



Generate a non-indexed spatial file with rectangles in a rectangular area of 1M x 1M



    shadoop generate test.rects size:1.gb shape:rect mbr:0,0,1000000,1000000 



Build a grid index over the generated file



    shadoop index test.rects sindex:grid test.grid shape:rect



Run a range query that selects rectangles overlapping the query area defined

by the box with the two corners (10, 20) and (2000, 3000). Results are stored

in the output file *rangequery.out*



    shadoop rangequery test.grid rect:10,10,2000,3000 rangequery.out shape:rect

    

Compile

=======



Advanced users and contributors might like to compile SpatialHadoop on their own machines.

SpatialHadoop can be compiled via [Maven](http://maven.apache.org/).

First, you need to grab your own version of the source code. You can do this through [git](http://git-scm.com/).

The source code resides on [github](http://github.com). To clone the repository, run the following command



    git clone https://github.com/aseldawy/spatialhadoop2.git

    

If you do not want to use git, you can still download it as a

[single archive](https://github.com/aseldawy/spatialhadoop2/archive/master.zip) provided by github.



Once you downloaded the source code, you need to make sure you have Any and Ivy installed on your system.

Please check the installation guide of [Maven](http://maven.apache.org/install.html) if you do not have it installed.



To compile SpatialHadoop, navigate to the source code and run the command:



    mvn compile



This will automatically retrieve all dependencies and compile the source code.



To build a redistribution package, run the command:



    mvn assembly:assembly



This Maven command will package all classes of SpatialHadoop along with the dependent jars

not included in Hadoop into an archive. This archive can be used to install SpatialHadoop

on any existing Hadoop cluster.