Operations | Monitoring | ITSM | DevOps | Cloud

Chaos Engineering


How to ensure your Kubernetes Pods have enough memory

Memory (or RAM, short for random-access memory) is a finite and critical computing resource. The amount of RAM in a system dictates the number and complexity of processes that can run on the system, and running out of RAM can cause significant problems, including: This problem can be mitigated using clustered platforms like Kubernetes, where you can add or remove RAM capacity by adding or removing nodes on-demand.


How a simple metric drives reliability culture at Slack

How do you track reliability in an organization with hundreds of engineers, dozens of daily production changes, and over 32 million monthly users? Even more, how do you do this in a way that's simple, presentable to executives, and doesn't dump a ton of extra work on to engineers' plates? Slack recently wrote about how they created the Service Delivery Index for Reliability (SDI-R), a simple yet comprehensive metric that became the basis for many of their reliability and performance indicators.


Deploying a multi-availability zone Kubernetes cluster for High Availability

Many cloud infrastructure providers make deploying services as easy as a few clicks. However, making those services high availability (HA) is a different story. What happens to your service if your cloud provider has an Availability Zone (AZ) outage? Will your application still work, and more importantly, can you prove it will still work? In this blog, we'll discuss AZ redundancy with a focus on Kubernetes clusters.


How to keep your Kubernetes Pods up and running with liveness probes

Getting your applications running on Kubernetes is one thing: keeping them up and running is another thing entirely. While the goal is to deploy applications that never fail, the reality is that applications often crash, terminate, or restart with little warning. Even before that point, applications can have less visible problems like memory leaks, network latency, and disconnections. To prevent applications from behaving unexpectedly, we need a way of continually monitoring them.


Automate reliability testing in your CI/CD pipeline using the Gremlin API

For many software engineering teams, most testing is done in their CI/CD pipeline. New deployments run through a gauntlet of unit tests, integration tests, and even performance tests to ensure quality. However, there's one key test type that's excluded from this list, and it's one that can have a critical impact on your application and your organization: reliability tests. As software changes, reliability risks get introduced.


How to ensure your Kubernetes Pods have enough CPU

Gremlin's Detected Risks feature immediately detects any high-priority reliability concerns in your environment. These can include misconfigurations, bad default values, or reliability anti-patterns. A common risk is deploying Pods without setting a CPU request. While it may seem like a low-impact, low-severity issue, not using CPU requests can have a big impact, including preventing your Pod from running.

More Reliability, Less Firefighting: How to Build a Proactive Reliability Program

Does it feel like your team spends all its time putting out incident fires? Change the story with a proactive reliability program that actively improves reliability. Join reliability expert and engineering leader Jeff Nickoloff for a webinar that lays out the common traits for successful reliability programs so you can build more reliability and spend less time firefighting. You’ll also get a downloadable checklist worksheet to help you create and evaluate your reliability program.

How Detected Risks helps you find reliability risks in minutes-without running any tests

This video showcases Gremlin's Detected Risks feature. Detected risks are high-priority reliability concerns that Gremlin automatically identifies in an environment. These include misconfigurations, bad default values, and reliability anti-patterns. Gremlin prioritizes these risks based on severity and impact, giving instantaneous feedback on risks and action items to improve the reliability and stability of each service.