Let’s be honest, heaven is a place where IT operations just work. There is no need to monitor and resolve any issues, and your days are filled with deep, innovative thoughts that grow your product or service to new heights. But unfortunately, we don’t live in heaven, and we get to deal with operational issues. The good news is that innovations in AI and tracing make dealing with operational issues much easier and faster to resolve so that it can almost feel like heaven.
Distributed tracing is the ability to track and observe requests though services or components of a distributed system. Mapping all the points between all the individual system calls required to fulfill a particular request.
With the increase of distributed architectures, micro-services, and containerization, the need for distributed tracing has become an essential component of an applications observability and operations. Tracing and the data it generates is essential for maintaining and troubleshooting any possible incident or failure to a distributed system.
A trace maps a chain of related events and provides the data for the time spent on each step of the request, the total time to handle the request, and the details for all the steps of the request. This data allows for a faster mean time to resolve a complex and identifies performance bottlenecks.
Traces have many benefits. One of the main benefits of tracing is the ability to track all transactions from start to finish. This allows for the identifying of dependencies and performance bottlenecks to optimize the design of the distributed system.
AIOps is the blending of big data and machine learning to streamline IT operations such as anomaly detection, event correlation, predictive analytics, and root-cause investigation. This requires the ability to collect data, aggregate the data, analyze the data, and execute on the findings.
More simply put, AIOps puts all the data in one place and allows AI and machine learning to learn and identify areas that may be causing issues or failures in the system.
As distributed systems continue to increase in popularity, the amount of data being generated becomes too large for any number of humans to manage. The containerization of services and components has siloed data, making it hard to understand how shared resources across the system are causing cascading failures.
AIOps can use at the same time multiple data sources, data gathering methods, real-time and deep data analysis tooling, and data visualization tooling. This creates multiple benefits for optimizing any operation including24/7 monitoring for data insights, intelligent automation, predictive analytics, correlated incidents, improved team communication, better customer experience, and much more.
In conclusion, AIOps will monitor the system for the business and eliminate the need for manual intervention. Because it can monitor data in real-time at deeper levels than is possible for humans, AIOps can find insights into your system that were previously hidden from the business.
Hopefully by this point this is an obvious answer. AIOps will aggregate and analyze the systems data in real-time to find current or future incidents in the data and then correlate it with other data to capture the entirety of the incident across the system so you know exactly what is happening and to what services and components. Now, marry that data to a trace and you also know exactly where the incident is happening. Tracing will map the incidents found by the AIOps across the system for immediate discovery and resolution.
Together, AIOps and Tracing can help find and resolve issues across the business before it effects the business or a customer. Saving time and money for the business. Businesses must employ system observability tooling that can match AIOps with tracing. Current tooling that just leverage statistical, correlated based AIOps with out tracing will fall behind in their observability efforts and accumulate tech-debt.
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