You can’t understand your pipeline, or correlate pipeline events with application performance and end-user experience, if you don’t understand what is happening inside your application. Once code is deployed, teams need to understand what is happening within their applications as each release flows down the delivery pipeline. Observability is not just for operations and should be employed during development. Gives fast feedback about (code) changes, even in production Look for solutions that are effective at baselining historical performance, performing sophisticated comparisons and detecting outliers and anomalies in real time. Observability solutions built with real-time analytics surface relevant patterns and deliver actionable insights before you need them. Old-school alert triggers are often inaccurate, causing floods of alerts that frustrate on-call engineers. Leverages in-stream AI for faster and more accurate alerting, directed troubleshooting and rapid insightsĪs much as we love humans, there’s no denying that cloud-native environments produce too much data for people to make sense of manually. Your solution should also allow custom dashboards that can help keep an eye on particular services of interest. Observability should give you intuitive visualizations that require no configuration - like dashboards, charts and heat maps - and make it easy to interact with key metrics in real time. Makes it easy to use, visualize and explore data out of the boxĪ completely fake statistic by a fictional analyst firm shows that most companies use only 12% of the capabilities their software systems provide. Your observability solution should have all capabilities fully integrated, providing you with relevant contextual information throughout your troubleshooting. Obviously, this doesn’t work when your actions are measured in seconds. It’s not uncommon to find application owners flagging a performance issue with one tool, then contacting another IT operations team that uses a different tool to try to understand how the issue is impacting critical workloads and business performance. Organizations manage multiple point tools. Enables a seamless workflow across monitoring, troubleshooting and resolution with correlation and data links between metrics, traces and logs Choosing to rely on common languages and frameworks will give you the most flexibility not only in how you collect data, but also what cloud solutions you use. Proprietary agents are difficult to maintain, degrade service performance and may be outdated before you know it. Plan on using open, standards-based data collection from day one. Leverages open, flexible instrumentation and makes it easy for developers to use Plan for that need now, as you begin to adopt microservices, because it will be very difficult (and costly) to add it later. If you have microservices running on Kubernetes-orchestrated containers that spin up and down automatically in minutes, or serverless functions that instantiate for only seconds, you’ll need a much finer view. As you start to collect data from more dynamic microservices running on ephemeral containers and serverless functions, you’ll need to collect data in different ways than you did in a virtual machine environment. how many people are angry at you and/or how much it’s costing). Operates at speed and resolution of your software-defined (or cloud) infrastructureĭifferent use cases require different resolutions, depending on how critical they are (a.k.a. When assessing observability solutions, look for those that do not sample and also retain all your traces, as well as populate dashboards, service maps and trace navigations with meaningful information that will actually help you monitor and troubleshoot your application.
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