For a generation of IT operations (ITOps) engineers, log monitoring was almost synonymous with application monitoring. To monitor your applications, you analyzed logs for signs of trouble.
Effective application monitoring for modern environments requires much more than just analyzing log data, however. It requires the broader functionality that only artificial intelligence for IT operations (AIOps) can provide.
Traditional Log Monitoring
It’s easy to understand why log monitoring traditionally formed the foundation for application monitoring. Logs have been around for decades. For example, syslog originated in the 1980s, and other types of log files were in use even earlier. These logs helped teams keep track of everything that happened within an application or a host system. As such, logs offered an easy, one-stop solution for gaining visibility into what your application or host infrastructure was doing.
The Problem with Log Monitoring
Unfortunately, log monitoring alone is no longer sufficient to gain full visibility into modern application environments. This is true for several reasons:
- Not everything keeps a log. Today’s environments often include components that simply don’t store data inside a log. Some of the microservices within your application may not log anything to a standard log file, for example. Therefore, to gain complete visibility for application monitoring purposes, you need to look at data beyond what is available from logs.
- Logs don’t tell you how things fit together. A modern application environment could involve dozens of logs, each devoted to an individual component or layer of the stack. The data inside those logs is great if you need to monitor just one element of the environment. To understand how the different pieces fit together, however, you must be able to map the relationships between all the components and understand their dependencies. Individual logs do little to help with this requirement.
- Manual log monitoring is not scalable. In the old days, when you had just a handful of logs to monitor, it was possible to analyze them by hand. If you knew something was amiss with your application, you could check timestamps to see what was happening around the time things went awry, and sort through it manually. Today, however, the sheer volume of log data you have to work with makes this impractical. Modern log monitoring must be more automated.
- Log monitoring doesn’t resolve problems. Analyzing the data in logs may help you identify problems, but it doesn’t resolve them. That is a separate process.
This is all to say that, while log monitoring still has a place in application monitoring, it no longer suffices on its own to enable broad visibility and management for modern applications.
How AIOps Takes Application Monitoring to the Next Level
The growing demand to move beyond log monitoring is part of the reason why AIOps has exploded in popularity. By using machine learning (ML) and automation to drive application monitoring, AIOps allows IT teams to do much more than just look at log files and try to work through problems based on them.
With AIOps, you can:
- Analyze more than just logs. Because AIOps enables completely automated data collection, it makes it possible to collect data from multiple sources, even if the data is not logged in a formal way. You can trace every API call and monitor every new deployment, for instance, without having to log those events.
- Gain holistic visibility. The sophisticated ML algorithms within AIOps-powered monitoring tools can map and understand the complex relationships between different layers of hardware and software in a way that humans would struggle to do. For example, an AIOps tool can instantly determine which physical server is hosting a given container instance, or automatically discover a new endpoint that has come online. In turn, AIOps can keep track of the ever-evolving relationships within an application environment, rather than merely monitoring individual components.
- Scale monitoring without limit. When your monitoring process depends at least in part on manual log monitoring, your ability to monitor is limited by the amount of time that your IT team can devote to the task. AIOps, however, offers a completely automated—and, by extension, completely scalable—approach. With AIOps, you can monitor a thousand applications or servers just as easily as you can monitor a dozen.
- Resolve issues automatically. Likewise, not only do AIOps-powered tools identify problems, but they can also recommend solutions or even remediate issues automatically. Automated response is another critical factor in enabling truly scalable monitoring.
In all of these ways, AIOps provides greater visibility and more accurate insights than log monitoring alone.
The Future of Application Monitoring
I don’t mean to suggest that logs or log monitoring are going to go away. They will likely continue to form the foundation for software monitoring for years to come.
However, log monitoring on its own will no longer be enough. Effectively monitoring and managing the performance of modern applications will require AIOps-powered tools that can analyze additional data points, understand complex relationships, and automate the entire process of both monitoring and incident response. That’s the only way for IT teams to stay ahead of monitoring challenges in a world where the scale and complexity of software environments will steadily increase for the foreseeable future.
