Getting The Best Performance For GeoJSON Map Visualizations: PostGIS Vs CouchDB Backend
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Formal Metadata
Title 
Getting The Best Performance For GeoJSON Map Visualizations: PostGIS Vs CouchDB Backend

Title of Series  
Author 

License 
CC Attribution  ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and noncommercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this license. 
Identifiers 

Publisher 
FOSS4G, Open Source Geospatial Foundation (OSGeo)

Release Date 
2013

Language 
English

Production Place 
Nottingham

Content Metadata
Subject Area  
Abstract 
In order to deliver rich user experience to user, features (attribute data and geometries) have to be sent to the client for mouseover visual effects, synchronization between charts, tables and maps, and onthefly classifications. GeoJSON is one of the most popular encodings for the transfer of features for clientside map visualization. The performance of client visualizations depends on a number of factors: message size, client memory allocation, bandwidth, and the speed of the database backend amongst the main ones. Large GeoJSONencoded datasets can substantially slow down loading and stylization times, and also crash the browser when too many geometries are requested. A combination of techniques can be used to reduce the size of the data (polygon generalization, compression, etc). The choice of an opensource DBMS for geospatial applications used to be easy: PostGIS is powerful, wellsupported, robust and fast RDBMS ? On the other hand, unstructured data, such as (Geo)JSON, may be better served by documentoriented DBMS such as Apache CouchDB. The performance of PostGIS and CouchDB in producing GeoJSON polygons with different combination of factors that are known to affect performance was tested: compression of GeoJSON (zip) to reduce transmission times, different levels of geometry generalization (reducing the number of vertices in transferred geometries), precision reduction (the reduction of numbers of decimal digits encoding coordinates), and the use of a topological JSON encoding of geometries (TopoJSON) to avoid redundancy of edges transferred. We present the results of a benchmark exercise testing the performance of an OpenLayers interface backed by a persistence layer implemented using PostGIS and CouchD. Test data were collected using an automated test application based on Selenium, which allowed to gather repeated observations for every combination of factors and build statistical models of performance. These statistical models help to pick the best combination of techniques and DBMS, and to gauge the relative contribution of every technique to the overall performance.

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Quicksort
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Set (mathematics)
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Point (geometry)
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Line (geometry)
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Covering space
File format
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3 (number)
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19 (number)
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Bit
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Functional (mathematics)
Singleprecision floatingpoint format
Number
Computer animation
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Point (geometry)
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17:29
Geometry
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Graph (mathematics)
Table (information)
Number
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Factor analysis
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Dependent and independent variables
Zoom lens
Graph (mathematics)
Geometry
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Usability
Prediction
Graph (mathematics)
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Pi
Personal digital assistant
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19:06
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Multitier architecture
Random number
Graph (mathematics)
Geometry
Set (mathematics)
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Route of administration
Area
Variable (mathematics)
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Data model
Number
Type theory
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Point (geometry)
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Numerical digit
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2 (number)
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Average
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Multiplication sign
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output
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00:01
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