Traditionally, every component of an IT solution produced logs that were monitored by the operations teams. The process involved monitoring of logs produced by the application, database, application server, operating system, platform, and network, to find root causes and reasons for issues reported by users.

As the IT solutions architecture evolved towards the cloud, the focus shifted towards log analysis, metrics, and proactive troubleshooting, apart from log collection and monitoring. This concept is widely known as Observability. The main objective of observability is to build knowledge about the possible failure points and reasons even before anything goes wrong and notify the operations team in advance.

What is Observability?

Observability for a solution encompasses three basic aspects irrespective of the technology stacks, application architectures, and hosting platforms.

Notice the anomalies

This is carried forward from the previous generation of solutions where components produce heterogeneous logs that are monitored continuously. The enhancement to the concept is that now a single application is preferred to access, collect, and monitor all logs produced by all the components of the solution. This single monitoring application is also responsible for notifying the respective teams when it finds specific errors in the logs.

Such applications also capture events produced by the solution components to establish an audit trail of transactions and operations within the solution. They have capabilities to establish the trail across multiple layers of the solution, such as application, platform, and network, and also across multiple business applications of a solution, such as IAM applications, OLTP applications, and BI applications.

Analyze the data

Apart from monitoring and notifying the anomalies, the next step is to analyze the data collected in the form of logs. The logs are correlated with simultaneous events in other components and analyzed through various dimensions, such as time, frequency, and density. It helps to identify data patterns leading to an issue, performance bottlenecks, resource leaks, and so on.

Learn the lessons

Lessons and metrics are the most important outcome of setting up observability. They are of immense value to the operations team, developers, as well as business users. They provide actionable insights into the entire solution. They help in isolating problem areas within the solution, the areas with maximum issues and highest MTTR, the factors that irritate the users the most, the factors that affect the productivity of the users, the friendliness of the solution components, and more.

These lessons are used to learn the behavior of the users and improve the non-functional aspects of the solution, such as performance, reliability, and robustness. Accordingly, the next versions of the solution can bring in the required improvements.

How is observability different for Cloud solutions?

Everything that runs on the cloud either uses or caters to an on-demand service model. This is a very important differentiating factor for observability. Also, the cloud offers different levels of service isolation, in terms of exposing a ready-to-use application as a service or the platform to host the application as a service or the infrastructure as a service. Most importantly, a solution hosted on a cloud platform need not be limited to a single cloud service provider. It may span across multiple cloud providers, in the public or private domain, as well as on the on-premises infrastructure. It means that the operations may need to take care of multi-cloud or hybrid-cloud models.

Given the different options available for hosting and exposing the solutions on the cloud, the solution provider needs to be aware of the exact behavior of the solution at all points in time. Observability of the solution provides the handle to the operations and the DevOps teams to monitor, analyze, and understand the solution.

What to observe on the cloud?

For a cloud-based solution, multiple metrics must be captured regularly. It includes the following in no preferred order.

• Infrastructure metrics, such as the number of service instances in inactive, waiting, or active modes, resource allocation vs utilization, resource demand vs availability, latency factors, bandwidth, thread dumps and memory stacks, and so on.
• Application metrics, such as the error frequency, error density, error volume, demand vs availability, audit trails, and more.
• Security metrics, such as attack rates, intrusion frequency, security alerts volume, vulnerabilities, and more.
• Business metrics,such as bounce rates, hit rates, productivity, and hourly throughput, revenue maps, and so on.

Finally, why observability is a major concern?

Low impact of failure: Observability helps in building proactive actionable insights into the working of the solution. This aligns perfectly with the idea of on-demand services on the cloud. It is important to know the elements that could fail so that their impact can be minimized along with the cost of the operations on the cloud.

Better reliability: The other important factors are the availability and scalability of the solution on the cloud. With effective observability, the operations team can greatly enhance the availability, scalability, performance, and thereby reliability of the entire solution. It would help in defining their service level agreements (SLAs) better than before and directly impact the likeability and usage of their solution.

More secure: Security is also tied together in highly observable solutions. It is easy to identify any kind of security risks, especially in multi-cloud, poly-cloud, or hybrid-cloud scenarios.

What is the way forward?

Although a non-functional concern, observability must be built into the application and infrastructure set up right from the beginning. The application must expose enough events and log messages related to its operations and the environments must be configured with appropriate trace levels to produce the system events that can be continuously accessed and collected by advanced monitoring tools for further analysis. Proper visualization must be done for the analyzed data to highlight the problem areas.

Integrating advanced monitoring tools, such as ELK, Splunk, AWS CloudWatch, or Azure Monitor, within the solution framework can improve the overall observability of the solution. Such tools help in collecting the data from multiple sources and applying AI/ML learning models to produce metrics.