DevOps: Observability vs Monitoring


Modern web applications cannot be efficiently monitored with legacy techniques that rely on managing “predictable” failures. DevOps is a culture with roles and methods that aim to increase uptime in the software development lifecycle (SDLC) and lessen downtime. Monitoring solutions help organizations do exactly that! 

With monitoring tools, you can identify and resolve issues as they arise in the SDLC at the earliest, thereby reducing downtime of developers’ teams. If the DevOps team takes too long to resolve issues in the SDLC, the downtime costs go very high!

Monetary losses due to IT downtime often depend upon your industry type, revenue, outage duration; time of the outage, etc. According to Gartner, the average cost of IT downtime is $5600/minute.  As per another Avaya study, the downtime costs can be anywhere from $140k to $540k per hour. Not to mention, there are other indirect costs of IT downtime as well that are not monetary to be specific, such as cost of interruptions, retention, productivity, and so on.

To summarize, downtime is expensive and it can ruin the reputation of your company. However, it is also unavoidable, as IT environments are getting more complex day by day. 

Now, responding to problems in the SDLC requires you to know about downtime in real-time. That’s when robust monitoring and observability tools come in!

Is observability a new buzzword?

Is observability a modified version of monitoring?

Are these two terms the same or different?

In this post, we will be elucidating how monitoring and observability are different from each other in DevOps, and why DevOps teams need automation in their incident management for faster MTTR.

What Is Monitoring in DevOps?

Monitoring tools go way back in history circa the 1990s when IT teams deployed monitoring tools to identify problems in the application environments and send alerts when something was unusual. Today, modern monitoring tools have become way more sophisticated, as they leverage advanced analytics to identify issues, no matter how complex the software environments are.

So, the basic objective of monitoring is to identify problems.

Monitoring solutions help DevOps teams monitor and get deeper insights into the real-time state of the systems with the help of predefined logs and metrics. With such close monitoring, DevOps teams are able to identify issues as they occur in the SDLC. Furthermore, monitoring tools also help with better alerting, dashboard design, and analysis of long-term trends.

Here are three main incident management objectives that monitoring in DevOps supports:

  • Issue detection for incidents such as bugs, unauthorized activities, outages, service deterioration, etc. Further, it aids in alerting to such problems in real-time and shows relevant data in dashboards.
  • Issue resolution by detecting which components are causing issues and wherein the SDLC. It does so by presenting data that aids troubleshooting and RCA (Root Cause Analysis). 
  • Continuous improvement of the entire product delivery lifecycle by extracting valuable insights that further promote better financial planning, capacity planning, reporting, and performance engineering. 

Bottom-line is that monitoring in the DevOps CI/CD pipeline accelerates the collection of data that further helps with automated issue detection, improved alerting, and better system health analysis. All of these factors eventually contribute to faster incident resolution. 

What Is Observability in DevOps?

Observability in SDLC is a newer term that prominently came into the picture in 2016 and got quite popular amongst DevOps teams and Site Reliability Engineers (SREs). Observability can be a bit complicated to understand, as the notion is new. 

Basically, observability means gathering actionable data so as to truly understand the problems in SDLC that are identified by the monitoring tools. Observability is a broader notion in DevOps than monitoring; say if monitoring is a branch, then observability is the tree in DevOps.

Monitoring helps you identify when and where issues arise, whereas observability helps you understand the bigger picture by answering ‘why the issue occurred in the first place. Observability enables the extraction of actionable insights from the logs of the monitoring tool. With such invaluable insights, you get a better idea of the performance and health of your system, environments, and applications.

Key components of observability in DevOps are as follows:

  • Logging to keep track of all incidents so teams can learn from previous incidents and find the source of the issue faster. This accelerates debugging.  
  • Tracing for a better understanding of the relation between cause and impact of an incident. Traces are like the storytelling data that is visually represented in the form of waterfall graphs. With these graphs, developers will be able to keep track of time taken in a system, through hops, queues, and servers. Tracing makes the observable system more efficient and accelerates the identification of the root cause.
  • Metrics are the collected quantitative data that helps developers identify long-term trends.

Observability reaps myriads of benefits such as interpreting insights from large amounts of data. These actionable insights are accessible to teams so they may solve anomalies. 

We know, as organizations scale with time, more and more systems get added to the infrastructure which makes it complex. Such complex environments create numerous logs every second, which can get overwhelming to handle for organizations. 

The more data gathered and analyzed, the easier it gets to leverage the data and drive incident resolution.

Today, observability is very prominent in DevOps SDLC methodologies. Back in time, developers built new products and introduced features, whilst testing and Ops teams looked after dependability. Such a siloed approach threw monitoring activities beyond the reach of development. Plus, code debugging was never a primary concern as systems were ONLY built to be successful. These legacy methods prevented developers from understanding the dependencies and application semantics. Hence, apps were developed with a lack of inherent dependability. Monitoring tools were unable to extract optimal amounts of data for issue detection for distributed environments. 

However, DevOps completely transformed the face of traditional SDLC. Today, monitoring is not restricted to just the collection of logs, metrics, and distributed incident traces, rather it is leveraged to make systems observable. 

Observability extends the scope to the development segment as it facilitates methods, people, and processes in the SDLC. Seamless collaboration between cross-functional teams viz. Dev, Ops, and QA are a must when framing systems, which will further help them achieve observability goals. Also, the QA personnel will be able to employ insightful monitoring during the testing process. As a result, Dev and ITOps teams can skillfully test a dependable system for real-time performance, drive continuous iteration and identify potential issues before they affect end-users. 

The Difference: Observability vs. Monitoring in DevOps

Monitoring is a branch of observability. Only a dependable system that is observable can be monitored.

  • Monitoring tools keep track of the overall performance and health of systems, collect performance data and send alerts when something goes wrong. It relies on collecting predefined sets of logs or metrics. 
  • Observability tools help teams proactively debug their system. Observability leverages insightful data collected from monitoring together with ML and analytics to provide deeper visibility into what’s happening, where it's happening, why it’s happening, and how to fix it. It relies on exploring characteristics and patterns and does NOT rely upon predefined parameters.

Now, the biggest challenge with observability is distributed tracing amongst application services and it requires immense expertise and years of experience. Organizations need to have a strong understanding of the fundamental principles of tracing requests flowing across application services. 

That’s where automation comes in!

Modern software delivery revolves around containerization, CI/CD pipeline, micro-services, and other complex components, which are bound to create bottlenecks for observability. 

Applications are deployed faster today with the smallest components, and teams often lag behind to keep up with the dependencies. Now, applications become observable through code instrumentation, as it provides insights into their internal execution. This approach provides high fidelity data which organizations can leverage to keep up with the pace of the CI/CD pipeline. 

Automation plays a vital role in successful CI/CD processes. End-to-end automated methodologies not only accelerate observability and monitoring in application services but also effectively reduce MTTR.

Topics: DevOps