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As an IoT developer, one is often tasked with putting together a solution that includes one or more open source components. I remember, even as far back as 2014, using components like Eclipse Mosquitto MQTT broker and Eclipse Paho MQTT client for a pilot project with IoT Gateway at Intel. Fast forward a few years at Red Hat, where I used components like Eclipse Kura and Eclipse Kapua for a European industrial automation project. Without realizing it then, I was using these components from Eclipse IoT open source projects.
Image courtesy of Eclipse Foundation
SLIs, or service level indicators, are powerful metrics of service health. They’re often built up from simpler metrics that are monitored from the system. SLIs transform lower-level machine data into something that captures user happiness.
Your organization might already have processes with this same goal. Techniques like real-time telemetry and using synthetic data also build metrics that meaningfully represent service health. In this article, we’ll break down how these techniques vary, and the unique benefits of adopting SLIs.
When I first started following Charity on Twitter back in early 2019, I was quickly overwhelmed by the new words and concepts she was discussing. I liked the results she described: faster debugging, less alert fatigue, happier users. Those are all things I wanted for my team! But I was hung up on these big polysyllabic words, which stopped me from taking those first steps toward improving our own observability.
This post is my attempt to help orient folks who want to learn more about observability but maybe feel overwhelmed or intimidated by the vocabulary list, like I did. My goal is to get everyone on the same page about what these words mean so that we can focus on leveraging the tools and ideas to build better software and deliver more value!
At Logicify, we are proud to be software monitoring geeks. We love to monitor both the apps we develop and the ones we use internally. Not because they are sloppy. Not because we don’t trust our code. But because we love to keep abreast of events, control performance and eliminate the risks of an error. Monitoring helps us be proactive and avert issues before real users are affected.
In our double-sided system of user behavior and app condition monitoring, we use Graylog as a single data storage for logs and other data about the web app, and Grafana, a powerful data visualization tool. Combined and wisely configured, these two tools give an objective picture of the app’s performance at all times. For comprehensive snapshots of system behavior and, what is more important for apps in production, for proactive moves to iron troubles out, we collect monitoring data from a multiple layers. App-specific metrics are complemented by other analytics to give a broader picture of system state and performance.