We are happy to introduce two new, major features in Load Impact that many users have asked for: parameterized data ("data stores") and custom metrics.
Parameterized data is when you're able to provide data in bulk using some common format - often a CSV (comma-separated) file that you upload, and which you can then access from your load script. The typical example is when you have e.g. 10,000 login names and passwords that you want to use in your load test. Entering these by hand into your load script code is prohibitively time-consuming, but with parameterized data you just upload a text file with all the usernames and passwords, and are then able to access these from inside your load script.
Custom metrics is a feature that allows you to store arbitrary result metrics during a load test. A typical use-case would be to store the load time for a certain page on your site (as opposed to just storing the load time for individual URLs/resources on the page). A more advanced use-case would be to fetch server monitoring data (via HTTP) from the web servers that are being tested, and log e.g. their CPU load along with the standard response time data collected by Load Impact. Any metrics stored with our custom metric feature will be visible in the test results interface, and can be plotted as graphs for easy correlation with the standard metrics.
Parameterized data in Load Impact
Parameterized data in Load Impact is implemented using something we call data stores. A data store is basically a two-dimensional array (a "table") with data that can be shared by multiple clients in a load test. The usage is simple: You create a new data store in the user scenario configuration interface, assign a name to it, then upload a text file with the data you want to insert into it. The data file should be in CSV format (comma-separated values) and be a two-dimensional table, but can contain any number of rows and columns. When you have a data store assigned to a user scenario your load test clients can then use the data store API functions to access the data store, and retrieve data from it.
Further reading: FAQ: How do I use parameterized data?
Custom metrics allows you to create your own, arbitrary result metrics and store sample points for them that you can then plot in graphs just like any other measurements. Custom metrics are really simple to use - in your load script you just have to call the special function result.custom_metric() and supply it with one parameter defining the name of the metric - e.g. "page 1 load time" - and one parameter defining the current measurement value for the same metric (a numeric value). Custom metrics can be used to plot all sorts of interesting measurement data, such as page load times, bandwidth usage for a single URL/resource, time to first byte for new TCP connections, and a multitude of things.
Further reading: FAQ: How do I create my own result metrics?