The Three Crucial Steps to Full-Stack Observability

The Three Essentials for Achieving Full-Stack Observability

Consider the following scenario, which frequently occurs in many businesses nowadays.

Sally, a website engineer, receives a page in the middle of the night about a website outage hurting digital consumers’ experiences. So every second counts, but she has no idea what the problem is based on the information in the pager alert on her phone. She gets out of bed and checks her performance monitoring tool to see what’s happening.

To her amazement, everything she can see without having to scroll has a problem. Every one of the almost 2,000 services for which she is accountable is turning red due to poor performance. It is terrible—terrible. Sally starts scrolling, but she can’t tell how many people are affected or if these services have anything in common, such as a cluster, a framework, a team, or similar services. She cannot also identify which metrics are generating unexpected readings swiftly.

So she goes through her application monitoring tool, a log management system, and an infrastructure monitoring tool one by one, looking for clues and commonalities. She even double-checks the device for real-time user tracking. It is time-consuming and prone to human mistakes, resulting in Sally losing sleep, dissatisfied clients, and wasted revenue for her job. However, this is the reality of how many companies deal with “observability.” They’ve got monitoring mixed up with actual observability.

Full-stack observability vs. siloed observability

Individual monitoring technologies can’t and shouldn’t silo the experience of observability—full-stack, end-to-end, seeing-the-whole-IT-stack visibility—which many enterprises are missing. Developer roles may be merging—in 2020, around 55% of developers worldwide who answered Stack Overflow’s poll identify as “full-stack” developers, compared to 29% in 2015. However, a full-stack developer, especially one working in a DevOps environment, is likely to use numerous tools and datasets to achieve what we call observability at New Relic: the ability to comprehend the behavior of your complex digital system.

And when we say “complex digital system,” we’re talking about all the code, services, infrastructure, user behavior, logs, metrics, events, and traces you collect across your landscape. Sally’s microservices and distributed systems increase her agility, scalability, and efficiency for customer-facing applications and critical workloads. Still, they also make it harder for her to see the big picture and achieve true observability.

Organizations aren’t to blame; as their IT estates increased and became more complicated, the number of monitoring solutions also grew. However, none of these tools provide a single source of truth for the whole stack’s end-to-end performance. It is supported by a report from the UBS Evidence Lab. Respondents in firms with a DevOps culture use an average of four to five tools each day to execute their jobs, ranging from APM to log management to SIEM.

On the other hand, juggling multiple monitoring tools to get a complete picture of your software systems or to find and fix problems creates blind spots, increases labor, and makes it more difficult to diagnose issues that may be affecting different parts of your estate or multiple layers of your application stack. In short, they get segmented monitoring, or “observability,” but only end-to-end observability counts in our book.

What does it mean to be observable from beginning to end?

So, to be clear, when we talk about end-to-end, or full-stack, observability, we mean:

• As engineers troubleshoot, debug, and improve performance throughout their whole stack, full-stack observability is their one source of truth. Without learning new tools or switching between them, users can detect and fix issues faster in one unified experience that gives connected context and reveals useful analytics—from logs to infrastructure and apps, distributed tracing serverless functions, and end-user experience.

Executing on and experiencing this form of observability requires prioritizing three system features for our hypothetical Sally engineer:

1. A context that is linked. When Sally looks at the health of one of her 2,000 services, she should understand how that service affects other services or components of the distributed system and how the Kubernetes cluster that hosts them affects those workloads and vice versa. And, all from one system, she should be able to see how the cluster and app issue is hurting the end-user experience on her company’s website, e-commerce portal, or mobile app.

Chegg, a provider of learning tools, thrives in a connected context. It created a complete picture of an incident by combining log messages with event and trace data. Whether an engineer is a backend developer, a system administrator, or a web developer, they all require immediate context throughout the entire stack, which is determined by the following attribute.

2. There is only one (open) source of truth. That includes storing, alerting, and analyzing operational data in a single location. Sally would require a platform that could accept metrics, events, logs, and traces from any source, including proprietary and open source agents, APIs, and built-in instrumentation. And that one location would have to be capable of scaling to handle the intake demand on her company’s busiest days. Companies frequently emphasize only one sort of telemetries, such as logs or metrics, or the sample data from a limited number of systems, apps, or instances. Both result in observability gaps and time-consuming troubleshooting.

“I’d get a 3 a.m. call about a problem, and the development engineer would tell me the application was performing perfectly, the network engineer would tell me the network was fine, and the infrastructure engineer would tell me utilization was fine,” says the operations manager at publishing and analytics company Elsevier. However, things were not as they seemed, and the actual difficulty came from the fact that they were staring at three distinct control planes.”

Full-stack observability is all about ingesting any telemetry data you want without worrying about scaling, constructing a pricey system for peak scale, or swiveling between various tools.

3. Exploration is easier and faster. Assume Sally’s firm provides her with a mechanism to ingest all metrics, events, logs, and traces from anywhere in the company’s IT stack. Sally recognizes interdependencies and up/downstream repercussions of issues thanks to the full-stack observability system, which gives context to the data. To view everything, she’s looking at one screen.

Consider this: all performance data from wherever, in real-time, on a single screen. That screen needs to be designed uniquely. Because Sally and her team will need intuitive visualizations that require no configuration to traverse massive, complex, distributed systems easily and rapidly comprehend and prioritize any issue. The goal of full-stack, end-to-end observability is to allow engineers to quickly investigate and discover system faults and troubleshoot and fix them before they become a client concern. It can only achieve lower mean time to resolution and higher uptime with speed. Developers should be able to experiment and chaos test confidently, knowing that their changes will not break the system. The advantages of full-stack observability are as follows.

Chegg’s digital experience dashboard provides a single dynamic view of aggregated data from throughout the Chegg portfolio and the flexibility to filter data to focus on a single product.

Sally’s dashboard should allow her to quickly examine massive systems using point-and-click filtering and grouping for all the components that make up her distributed system—apps, infrastructure, serverless services, third-party integrations, and so on. It shows her where anomalies are happening and what changes might be causing them. She rapidly recognizes the interconnectedness of difficulties across the system and any connections. Her team can use saved views when troubleshooting to increase productivity and communication. Sally should see an intuitive and current interface when she wakes up at 3 a.m. from a website that it can serve as every SRE’s and IT team’s daily real-time dashboard for knowing what’s going on throughout their whole environment.

The collection of all types of telemetry data is a hurdle for many firms when it comes to full-stack observability. Engineers adore their tools, especially those who work in a DevOps environment. Any platform that provides end-to-end observability must persuade engineers by demonstrating immediate and better value than their current tool. One method to inspire them to improve is to promise them more sleep.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of clouds, RDBMS, NoSQL, and machine learning database platforms.