So now that you have your deployment marker created, you know you’ll be able to quickly see if a regression occurs after a deployment. For this (and the next steps), we’ll use the Lightstep Sandbox to work with demo data that includes that type of regression.

To access the Sandbox, go to

An interactive demo launches (separate from this tutorial) and is ready for you to start. Feel free to do that first! When you’re done, or if you want to skip it for now, click the collapse button to close the demo.

An Exit Demo button displays at the bottom if you’re signed into Lightstep. Clicking this will take you out of the Sandbox and into your own project.

Now that we have some demo data, let’s find the regression.

  1. Click on Service Directory from the navigation bar.

  2. From the Service Directory page, select the iOS service.

  3. Scroll down to view the iOS operations and see that the /api/update-inventory operation has a few latency spikes. You see that there is actually one big spike in the p99 latency. Let’s look at this more closely.

  4. Click on that chart in the middle of that spike to set the Regression time period and click Investigate Regression. You can now start your comparison by selecting a baseline to compare the regression to.

  5. Click in a stable point of the latency chart to set the Baseline time period.

  6. Click Compare.
    The page redraws with additional analysis tools.

Lightstep offers a number of different tools you can use to start creating hypotheses about what is causing the regression. We’l explore those next.

What Did We Learn?

  • You can see deployment markers on the Service Health view.
  • When you click into a chart, you can set a comparison point to start your investigation.
  • You can select a baseline time period to compare the regression to and Lightstep shows you data from both time periods.