Home > Spatial Database, Web GIS > Choosing the optimal configurations for GeoServer

Choosing the optimal configurations for GeoServer

What I wanted to do

Setup GeoServer with optimal performance. I define optimal performance as fast response with large number of concurrent users (HTTP request of WMS). The GeoServer will be serving vector line data with 80K features. The vector lines are currently stored as a feature class in ArcSDE with Oracle.


I have identified several parameters/configurations could improve the performance of GeoServer at OpenGeo and GeoServer’s documentation. In particular, here are a list of big questions that I need to answer:

1. What are the optimal JVM options

2. Which application server shall I use to serve GeoServer, Tomcat or Glassfish?

3. Which source data format shall I use to feed GeoServer? Shall I stay with ArcSDE or shall I export the feature class to a shapefile?

Here is the verision of some applications that I used:

  • Tomcat 6.0.33
  • Glassfish 3.1
  • GeoServer 2.1.1
  • JRE 1.6
  • ArcSDE 9.3.1
  • Oracle 11g


To find answers, I need to do some testing and find out what set of configurations give me the fastest HTTP response when GeoServer handles WMS request. To measure the testing result, I found an excellent tool called JMeter. It basically issues customized HTTP request to specified web service and measures the response time. It can also do the testing for multiple-user scenario.

I started with JVM options. A benchmark was established with default JVM settings, Tomcat, ArcSDE as source data format, and single user scenario. Then I changed one parameter for JVM option at a time, measured the response time and compared the result against the benchmark.

After finding the optimal JVM options, I switch the source data format from ArcSDE to shapefile and continue to run the testing on single user scenario.

Then I switched to multiple-user scenario with 10 users sending requests at 1 second interval. In this case, GeoServer’s performance decreases dramatically for either shapefile or ArcSDE as source data format.

I also did similar test with Glassfish.

Here is my findings:

Optimal JVM options: -server -Xms1024m -Xmx1024m -XX:MaxPermSize=512m -XX:+UseParNewGC. Of course, these options depend on the hardware configuration of my machine

Tomcat vs. Glassfish

  • In single user case: Glassfish is faster than Tomcat
  • In multiple user case: Tomcat is faster than Glassfish but more volatile. On average, Tomcat responses faster. But comparing with Glassfish, a lot more of Tomcat’s responses are either faster or slower, making the response time less predictable in Tomcat.
  • With user number increases, response time increases too. But Glassfish’s response time increases more than Tomcat for the same number of user increase

ArcSDE/RDBMS vs. Shapefile

  • In general, shapefile is faster than ArcSDE/RDBMS. In single user scenario, shapefile is more than 10 times faster than ArcSDE. This is reasonable because when ArcSDE is involved there is overhead of connecting to database. Even if there is database connection pooling in Tomcat, the overhead is still very costly
  • In multiple user case: response time increase for ArcSDE is less than shapefile. This is reasonable as RDBMS is designed to handle concurrent users scenario
  • In my 10-concurrent user scenario, even shapefile’s performance decreased more dramatically than ArcSDE with user increase, it still perform about 10 times better than ArcSDE. But I think with more and more concurrent users (100, 1K even 10K), ArcSDE/RDBMS’s performance should eventually out beat shapefile

So, if I want stability in performance, fast response time, AND if I predict only very small number of concurrent users, I should go with Glassfish and shapefile. In the case of large number of concurrent users, it seems that the combination of Tomcat and ArcSDE/RDBMS  should lead to better performance.

  1. guz
    May 22, 2012 at 00:10

    very interesting, I’ve always wondered how glassfish would behave serving geoserver instead of tomcat… thanx!

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