Benchmarks

Performance shoot-outs are being presented each year, at FOSS4G events1. The effort being made to prepare the benchmark, and the code changes returning back to the community resulting from this exercise are really helping to improve the software.

Currently, many web mapping applications are two way, in the sense that these applications are used to present and collect geospatial data from users. The examples ranges from sharing cycling tracks (with lots of points) to urban management public participation (with a few geometries). Open Street Map is one of the best examples of this web 2.0 concepts in the geospatial domain. Using Goodchild's metaphor, for six billion sensors, we need very fast upload services for spatial data.

Since geospatial data presentation is already being evaluated each year in FOSS4G events, in this paper we present a different benchmark that measure how fast our open source servers are handling spatial contributions.

We've set up a basic benchmark environment to evaluate CRUD (create, read, update and delete) geospatial operations performance. Datasets were created for the benchmark, considering simple geometries and more complex ones, but whenever possible data from the 2010 benchmark2 was used. Tests were made for 1, 10, 100, 1000 and 5000 CRUD operations in one request.

The services considered are: two different WFS-T implementations (deegree and Geoserver), the RDBMS Postgresql, the MapFish Server and the OSM API V0.6.

The WFS-T, MapFish and direct RDBMS CRUD operations are compared in terms of performance. The same operations were carried out on the same server, submitted by a local client, without any other processing overhead or network delays. The machine was always reconfigured and rebooted before the next test. Whenever necessary, developers were contacted to verify the correct dependencies and configuration of the server software.

Advances in sensor technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms remotely sensed data are collected, stored and processed.  This exponential growth of data archives and the increasing user’s demand for real-and near-real time remote sensing data products has pressurized remote sensing service providers to deliver the required services. The remote sensing community has recognized the challenge in processing large and complex satellite datasets to derive customized products. To address this high demand in computational resources, several efforts have been made in the past few years towards incorporation of high-performance computing models in remote sensing data collection, management and analysis. This study adds an impetus to these efforts by introducing the recent advancements in distributed computing technologies, MapReduce programming paradigm, to the area of remote sensing.