One of the chief complexities in running large scale containerized applications is the need for continuous systems/application monitoring. Containers are very different from traditional VMs and the 3 tier applications that run on them. Monitoring that needs to ensure that SLAs promised to the business are being met as well as an ability to forecast usage trends while identifying problem areas such as bugs, capacity challenges, slowing performance, and any potential downtime.

Last week, Rachel published a guide describing the advantages of dynamic sampling. In it, we discussed varying sample rates to achieve a target collection rate overall, and having different sample rates for distinct kinds of keys. We also teased the idea of combining the two techniques to preserve the most important events and traces for debugging without drowning them out in a sea of noise.

AWS Lambda has a cool feature that can be both a blessing and a nightmare for a serverless application, depending on whether it’s properly handled by our code: the retry behavior. A retry occurs when an invocation of a Lambda function results in an error and the AWS Lambda platform automatically invokes the function again, with the same event payload. Before we get deeper, make sure you are familiar with the AWS documentation on the subject.

It’s been a while since the new Monitive codename “Freyja” was launched in private beta, and even though a lot of things have happened, development is moving forward a bit slow than I’ve hoped. Nevertheless, I’m happy that the core monitoring engine is running smoothly and it’s a great foundation for the years to come.

More often than we’d like to admit, we tend to underestimate the impact of every moving part within an organization—especially those that seem small or insignificant. And usually, it’s not until we’re facing the fallout of neglecting that seemingly insignificant factor when we realize what a mistake we’ve made.

There are countless Grafana dashboards that will only ever be seen internally. But there are also a number of large organizations that have made their dashboards public for a variety of uses. These dashboards can be interesting to browse, giving you an insider’s peek into how real Grafana users set up their visualizations, with actual live data to boot. Perhaps some of them will inspire you to get to work on your own Grafana?