WMS Performance Shootout 2009

Jeff McKenna
Jeff McKennaDirector at Gateway Geomatics
WMS Performance Shootout Andrea Aime (GeoServer) Jeff McKenna (MapServer)
Executive summary ,[object Object]
MapServer ,[object Object],[object Object]
Raster: global imagery ,[object Object],[object Object]
Raster: ECW and GeoTiff mosaics
Benchmarking History ,[object Object]
FOSS4G 2008: OpenGeo run and published the second comparison with some review from the MapServer developers. Focus on simple thematic mapping, raster data access, WFS and tile caching ,[object Object]
Past MapServer improvements ,[object Object]
raster read optimization (single pass for multiple bands)
enhancing polygon labelling placement
EPSG codes caching
PostGIS improvements
Oracle improvements
Past GeoServer improvements ,[object Object]
GML writing speed-ups
Raster data access improvements
PostGIS data store optimizations
Rules of engagement ,[object Object]
Each server performs exactly the same workload ,[object Object]
Same data backends
Same image output format ,[object Object]
Data used cannot be modified for display, other than indexing
All testing to be done on the same benchmarking machines
Hardware Configuration Bench WMS Database JMeter GeoServer MapServer Shapefiles ECW GeoTIFF Oracle PostGIS SDE
Software Specs ,[object Object]
Apache 2.2
FastCGI - 8 static processes ,[object Object],[object Object]
Tomcat 6.0.20 - 8 threads ,[object Object],[object Object]
SDE 9.3.1
PostGIS 1.4.0
Hardware specs ,[object Object]
1 Xeon Nocona 3Ghz, 1 GB RAM, OS: RedHat Enterprise 5.3 ,[object Object],[object Object]
4 Xeon Nocona 3.4Ghz, 8 GB RAM
6 73 GB, 15K RPM hard drives, OS: RedHat Enterprise 5.3 ,[object Object],[object Object]
1 x Intel Core2 Duo CPU E6750 @ 2.66GHz / Dual Core, 4Gb RAM
100Gb SATA 3.0Gb/s 7200 rpm HD, OS: RedHat Enterprise 5.3
Hot vs Cold Benchmarks ,[object Object]
The software has had the occasion to perform whatever pre-processing is necessary (e.g., open connections to the data source) ,[object Object],[object Object]
There is no existing cache ,[object Object]
Hot benchmarks are more practical to run
Methodology ,[object Object]
Each test uses a random set of requests stored in a CSV file: no two requests in the same run are equal, but all servers perform the same workload
For each request the random factors are: ,[object Object]
The geographic envelope (extent) ,[object Object]
The other GIS server is shut down while the tests are run
Datasets Used ,[object Object]
Point layer: gnis_names09 : all location and points of interest names for the state of Texas in the GNIS database. 103000 points, 2.8 MB shapefile, EPSG:4269
Line layer, edges_merge : all line elements (rivers and roads) from the TIGER 2008 dataset for the state of Texas. over 5M lines, 1.2GB shapefile, 1.4GB dbf, EPSG:4269
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WMS Performance Shootout 2009