Shipping complex applications at high velocity lead to increased failures. Longer pipelines, scattered microservices, and more code inherently lead to bigger complexity where small mistakes may cost you big time.
OK, first things first. I have to admit that I am, first and foremost, an old-school UNIX systems administrator. I’m that grizzled sysadmin in your shop who soliloquizes wistfully about managing UUCP for email “back in the day.” Centralizing Logs? Yeah, we had syslog, and saved it all off to compressed files.
Distributed tracing is a household term nowadays – if your house is an IT department! Modern enterprises use cloud-native applications for agility and responsiveness to customer needs. When monitoring cloud-native applications, distributed tracing follows how transactions perform while traversing services or containers in the backend architecture. By definition, we’re describing production applications with requests, methods, database calls and logs that accompany a transaction.
Have you ever experienced user complaints and struggled to find the root cause of the performance degradation? I'm sure every IT operations professional has. Is it the application? Is it the underlying infrastructure? Is it the network? What if you have a single pane of glass that will gather all the relevant metrics and telemetry and display it in an intuitive and easy to understand fashion?
As more and more organizations adopt cloud-native technologies, the need to align business objectives and end-user experience with IT infrastructure’s availability and performance is ever-growing. This trend requires infrastructure monitoring setups to ensure that all of your systems are active and working together across your cloud environments, host operating systems, storage systems, etc.
The majority of monitoring and management solutions used in enterprises provide their customers with APIs (Application Programming Interfaces) and a CLI to facilitate DevOps type workflows. With IaC (Infrastructure as Code) becoming de facto and ubiquitous, decent APIs have long been a must have on product evaluation checklists; there are of course a few exceptions – namely products aimed only at SMB (Small and Medium Business), immature startups, or freeware.
SIGNL4 is a cloud-based mobile alerting and incident response service. Third-party systems like monitoring tools, control systems or IoT sensors detect abnormalities and transmit events to SIGNL4 over the Internet. What if your systems cannot transmit critical events anymore? That might happen when the Internet is down or when the tool itself has a problem. In this case, SIGNL4 would miss critical events and could not turn them into alert notifications to your IT admins, technicians and experts.
If you’re a 1.x user of InfluxDB, you’re most likely more familiar with InfluxQL than you are with Flux. To gain a deep understanding of Flux, it’s important to understand: However, you can still use Flux without studying those topics. In this TL;DR, we’ll convert common InfluxQL queries into Flux and identify patterns between the two languages to help you get started using Flux more easily if you come from a InfluxQL or SQL background.
