<Observability and Monitoring In DevOps/>

When it comes to the software delivery process, observability and monitoring are two topics that are gaining awareness. Regardless of how you toiled hard to create high-quality software applications, bugs and errors will always reoccur. Factors like a sudden increase in the number of total users cause a software application to gain higher complexity. That is why your system must be observable.

Over time, major strides have taken place in the software delivery culture. It has shifted from monitoring to cloud-native. Applications residing in both the modern cloud-native environments and the traditional on-premises infrastructures are required to be highly available and resilient to failure. However, the methods utilized to meet these goals vary. There are various advantages of monitoring, such as boosting performance and productivity. For instance, you can allocate resources to meet the user requirements in an efficient manner. Similarly, you can identify and address the issues before they disrupt your business.

Observability In DevOps

Monitoring the continuous improvement/continuous delivery (CI/CD) pipelines makes it necessary to make each component of the pipe observable. These components should generate suitable data to automate problem detection and alerting, analysis of system health, and manual debugging.

A system should generate the following data types to be observable.

• Health checks – These are usually custom HTTP endpoints and facilitate orchestrators (e.g., Kubernetes). They are conducted to maintain the system’s health.
• Metrics – These refer to the numeric data representation that is obtained at certain intervals into a time series. This data is simple to store, and you can query it easily. This can be incredibly useful for those who are on the lookout for historical trends. For a longer period, you can compress numerical into shorter phases, such as daily, weekly, and monthly.
• Log entries – Discrete events are represented through log entries. These are important for debugging and entail stack traces and other key contextual data points to identify the root cause of observed failures.

Monitoring In DevOps

In the software development lifecycle, proper monitoring increases performance and productivity. It minimizes downtime and allocates resources and time efficiently so that you can strategize your upgrades and new projects in no time. Monitoring is essential for evaluating long-term patterns and trends for creating alerts and building dashboards. It enables you to determine how your applications are working, how they are expanding, and how they are being used. What makes monitoring distributed applications tricky is that production faults are non-linear and, thus, tough to predict. If you are looking to create and run microservice-based applications, then keep in mind that monitoring remains a highly effective tool. It offers a great perspective of your system health as long as the monitoring metrics and rules focus on actionable data. This way, you can determine the outcomes of failures and make relevant fixes timely.

Implementing Monitoring and Observability

Monitoring and observability solutions are created to:

• Identify unexpected side effects of added functionality or changes
• Detect long-term trends for business purposes and capacity planning
• Identify and facilitate debug outages, unauthorized activity, bugs, and service degradation
• Offer indicators of an outage or service degradation.

With DevOps, installing a tool is insufficient for meeting the objectives. Here, tools can hinder or help the effort. Monitoring systems must not be limited to a single team or individual. Empowering all developers to gain monitoring expertise leads to a culture of data-driven decision-making. Therefore, outages drop as overall system debuggability improves. There are many tips to implement monitoring and observability effectively. For starters, monitoring must identify what is broken and what led it to this state before irrecoverable damage is caused. The time-to-restore is the primary metric in the event of an outage or service gradation. A major contributor to TTR is the ability to quickly understand what malfunctioned and identify the fastest way to restore the affected service. There are two ways to assess a system: blackbox monitoring and whitebox monitoring.

Blackbox Monitoring

In the blackbox monitoring system, the input is sent to the system, just like how a customer does it, to be reviewed. This can be any of the following:

• Calling a complete webpage to be rendered
• Making RPC calls to an exposed endpoint
• Making HTTP calls to a public API

Blackbox monitoring is a sampling-based method. The blackbox system monitors the system responsible for user requests. A blackbox system can offer coverage of the target system’s surface area. This could include assessing external API methods. A scheduling system can help you govern this system to ensure that these inputs are sent at the right rate for proper simulation of customer behavior.

Whitebox Monitoring

Whitebox monitoring involves monitoring applications that run on a server. It is based on metrics exposed by the system’s internals, including an HTTP handler, interfaces like the JVM Profiling interface, and logs.

• Monitoring MySQL queries that run on a database server
• Reviewing the number of users using a web application throughout the day and notifying if something goes wrong.
• Performing advanced detection of behavior. For instance, a user who does not go through the usual steps when they sign in to your application or reset a password.

Creating a Continuously Observable System

Attaining observability does not necessarily have to be complex. There are metrics to work with, such as your application’s memory, CPU, and network. System logs ensure your system’s observability. Over time, logs become harder to handle and costly to store. With tools like OpenTelemetry, you can increase your logging effectiveness. OpenTelemetry is versatile, i.e., you can use it for logging, tracing, and metric collation. Tracing adds efficiency to your observable system and allows you to determine the source of a problem in a distributed environment.