WTX Map Transformation Performance

The following performance test was done with a 40MB flat file transforming from one file to 8 different output files. This test is done on a Lenovo T61 with 3GB of RAM and 2.o GHz Core 2 Intel Chipset:

All nine transformation was processed in 2:13 minutes.

Please see the document below for more performance tuning tips on your WTX maps:

ftp://ftp.software.ibm.com/software/websphere/integration/wdatastagetx/1120.pdf

soapUI: Datapower XML Firewall and Multi protocol Gateway Performance Testing

soapUI has limited support for testing standard HTML Web interfaces on top of the existing REST testing support.

Set up your local endpoints for your XML Firewall and Multi Protocol Gateway in SOAPUI:

For more information on this please visit the following link:

http://www.soapui.org/Web-/-HTTP/getting-started.html

WebSphere DataPower SOA Appliance performance tuning

This article provides performance tuning approaches, guidelines and tips for IBM WebSphere DataPower SOA Appliance, since performance is a key attribute for WebSphere DataPower it is very important to be able to tune the device to achieve expected performance results.

http://www.ibm.com/developerworks/webservices/library/ws-dpperformance/index.html?ca=drs-

Datapower Enchanced Performance

Diagram below shows the CPU overhead of various common tasks. (Notice the parsing level is low hereā€”the main hit when parsing is memory utilization.) Notice the impact of security operations. This can be helped somewhat with hardware-assisted acceleration, but the cost-benefit of hardware acceleration boards is often debated. Also note that abusing these security features to consume CPU resources is one way of mounting attacks.

Message sizes is often another major stumbling block for Java-based software systems processing XML. In modern day real-world systems, we are now seeing huge SOAP messages on the order of hundreds of megabytes or even gigabytes in size. The conundrum is how to process these, given constraints on maximum JVM heap sizes in many platforms. Due to aggressive built-in streaming and compression, appliances can handle messages larger than their actual memory space.