Legacy tools are no longer helpful in monitoring modern applications that are distributed. Application performance monitoring has become tough due to the increasing size and complexity of modern applications. Organizations are using observable applications that can be easily monitored. Application performance monitoring has changed a lot over the years. With the rise of new-age technologies, a paradigm shift is seen in the monitoring process. However, there is a thin line between observability and monitoring and, IT professionals may often get confused.
Modern systems are complex and require rigorous monitoring to detect incidents in real-time. The main motive of monitoring is to enhance operability and reduce the MTTR (Mean Time to Resolve). Monitoring lets IT teams know about critical issues in their IT infrastructure. Predefined logs and metrics are used to monitor the internal states of software systems in an organization. Businesses analyze the performance of their applications via monitoring. System failures are never linear which, makes application performance monitoring tougher. Traditional monitoring tools are no more used for complex distributed applications. New-age solutions like AI for application monitoring are being used by businesses.
Monitoring does not make your systems immune to power outages. Instead, they provide information about the health of software systems. With advanced monitoring solutions, you can get a panoramic view of system health and predict IT outages. Having said this, monitoring is a crucial process for examining system health and running complex software systems without any latency. However, monitoring cannot be done on a system that is rigid and does not reveal its internal states. Monitoring and observability go hand-to-hand to boost the IT infrastructure of an organization.
A system that is not observable cannot be monitored rigorously. Observability is defined as the ability to view the internal states of a software system or application. The internal states of a software system are decoded based on the external outputs. If a software system is not providing external outputs, there is no way one can know its internal states. Monitoring is something that is conducted after making sure the system is observable. An observable software system or application will help in finding answers to questions like:
- What request was made to a software system and, what were the performance checkpoints?
- How did a software system execute any request?
- Did the software system behave the way it was expected?
- What is the reason for the failure or declination of a user/employee request?
Observability is dependent on three pillars that are as follows:
- Logs: Log data denotes the interaction of the user with an application, website, server, or database. The interactions of employees with software systems along with user activities are recorded as log data. Logs help in determining the change in behavior of software systems. It can be used to determine the exact event that went wrong and led to an IT incident. A monitoring tool that records log data continuously will offer high observability into the software systems.
- Metrics: Metrics are measurements (numbers) that are produced over a course of time. Metrics can help in determining the performance bottlenecks and tune them accordingly. For example, the number of requests handled per second is a metric that can be used to determine the speed of a software system.
- Traces: When a request is made to a distributed IT infrastructure, it moves through multiple nodes before getting executed. A trace is an exact path taken by a request/event from starting till execution. Tracing is an important pillar of observability that also deduces the other two pillars. For example, you choose performance metrics based on the system path that is to be analyzed. Log data is also collected in relevance to the system path being traced.
How observability is the evolution of monitoring?
- Monitoring tells you when something goes wrong with your software systems or applications. On the other hand, observability tells you why something went wrong with your software systems or applications.
- Monitoring is concerned with the overall health of a software system whereas observability is concerned with the internal states of the system.
- Monitoring is for tracking the performance of software systems in terms of speed, connectivity, and reliability. On the other hand, observability provides contextual insights to understand a specific system outage.
- Observability is more granular than monitoring and has evolved from it.
How to enhance observability for rigorous monitoring?
Since the traditional monitoring tools don’t offer rich observability into software systems, businesses are looking for better solutions. AIOps (Artificial Intelligence for IT Operations) can make your software systems more observable. The complex and distributed IT infrastructure cannot be monitored manually by system administrators. It is why AI for application monitoring is the solution. Observability and monitoring can be improved with AIOps are as follows:
- An AIOps based analytics platform collects log data and telemetry data rigorously without any manual efforts. With abundant data about user experience and system performance, you can enhance observability.
- AI for application monitoring can aid in round-the-clock monitoring even when no one is at the business facility.
- An AIOps based analytics platform studies the behavior of software systems and generates alerts in real-time for any anomaly. With high observability, AIOps platforms can find an anomaly even when no one is aware of it.
- Once an IT incident is discovered, an AIOps based analytics platform generates actionable insights to solve the issue quicker. It also finds the underlying cause of an IT incident in less time.
In a nutshell
When the traditional monitoring tools started to fail for distributed and cloud infrastructures, there was a need for high observability. The best AIOps tools and products in the market are now offering rigorous monitoring with the least manual effort.