Traditionally, monolithic software environments—where single-tiered software applications combined different components into one program—were standard. For example, a simple e-commerce application could include several functions bundled together, such as inventory, payment, and shipping. When something went wrong, and issues cropped up, it was relatively easy to identify which part of the code was at fault and why. You could dig through transactions inside that part of the application to find bottlenecks or errors.
Many organizations continue to find monolithic environments serve their purposes well. But as more engineering organizations move from monoliths to microservices, containers, and serverless architectures, what they gain in speed and flexibility is countered by increased complexity. Microservices environments can include dozens or hundreds of services, and seeing how they connect and how requests flow through them to diagnose an issue can be challenging—but also critical.
Not only that, the increased adoption of DevOps and site reliability engineering (SRE) practices coupled with technologies like orchestration, automation, and CI/CD for frequent software deployments in highly distributed environments, also introduce greater complexity for application monitoring. There are more points of failure in a distributed system, not to mention the added complexity of having various teams managing different parts of the system.
When issues occur, if you don’t have the right monitoring instrumentation in place, you risk wasting a significant amount of precious time searching across your distributed systems, increasing mean time to resolution (MTTR). The time squandered while searching for answers leaves you with less time for innovating and developing new software or features.