OpenTelemetry is a Cloud Native Computing Foundation(CNCF) incubating project aimed at standardizing the way we instrument applications for generating telemetry data(logs, metrics, and traces). However, OpenTelemetry does not provide storage and visualization for the collected telemetry data. And that’s where an OpenTelemetry backend is needed. Cloud computing and containerization made deploying and scaling applications easier.

Log analysis is a very powerful feature for an application when it comes to debugging and finding out which flow is working properly in the application and which is not. In a world of containerization and cloud computing, it is essential to understand logs generated by a Docker environment to maintain healthy performing applications. In this article, we will discuss log analysis in Docker and how logging in Docker containers is different than in other applications.

Elasticsearch and Splunk can both be used as log analysis tools for software applications. Elasticsearch, as part of the Elastic Stack, offers a highly scalable, open-source solution for real-time search and analytics across diverse data types, excelling in customization but with a steeper learning curve.

Logs are useful for troubleshooting and identifying issues in applications, as they provide a record of events and activities. However, managing log data can be challenging due to the large volume of log events generated by modern applications, as well as the need to balance the level of detail in the logs and the impact on the application's performance.

Kubernetes has since emerged as “THE” container orchestration platform for deploying and managing containerized workloads as a result of its robust capabilities. However, the complexity of its architecture and its dynamic nature present significant challenges in monitoring deployed workloads and the platform itself. Kubernetes monitoring is crucial for maintaining the health, performance, and reliability of containerized applications.

Modern-day software systems emit millions of log lines per minute. Cloud computing and containerization have made it easy to have distributed systems. Distributed systems emit logs from multiple sources. While developers have always used logs to debug stand-alone applications, centralized logging solves the challenges of modern-day distributed software systems.

Effective log management is a fundamental aspect of maintaining and troubleshooting today's complex systems and applications. The sheer volume of data generated by various software and hardware components can make it challenging to identify and resolve issues in a timely manner. Open-source log management tools offer a cost-efficient and customizable approach for collecting, analyzing, and visualizing log data.

Information about the containers and pods on your cluster may be obtained using the kubectl logs command. These logs allow you to know the performance of your applications, whether they are failing or healthy, and are particularly useful for debugging and troubleshooting purposes. In this article, we will see how to use the kubectl logs command to get information from existing resources in a Kubernetes cluster. Before we dive in, let's first take a quick look Kubernetes architecture and logging.

Kubernetes is a powerful orchestration tool for managing containers, but it comes with its own set of challenges. One of the biggest hurdles is effectively logging what's happening in your system. As your applications grow and spread across clusters, keeping track of their behavior becomes crucial. In this article, we will discuss logging in Kubernetes, common Kubernetes log types, and how logs can be effectively tracked and managed.

Kubectl Top command can be used to retrieve snapshots of resource utilization of pods or nodes in your Kubernetes cluster. Resource utilization is an important thing to monitor for Kubernetes cluster owners. In order to monitor resource utilization, you can keep track of things like CPU, memory, and storage. In this article, we will see how to use kubectl Top command to get and read metrics about pods and nodes. We will also breakdown the output to understand what it means.