Spans are fundamental building blocks of distributed tracing. A single trace in distributed tracing consists of a series of tagged time intervals known as spans. Spans represent a logical unit of work in completing a user request or transaction. Distributed tracing is critical to application performance monitoring in microservice-based architecture. Before we deep dive into spans, let's have a brief overview of distributed tracing.

Both DataDog and Jaeger are tools used to monitor application performance. The difference lies in what they monitor and terms of usage. Jaeger is an open-source tool focused on distributed tracing of requests in a microservice architecture. While DataDog is a SaaS APM vendor covering most monitoring needs of an application. Application performance monitoring is the process of keeping your app's health in check. APM tools enable you to be proactive about meeting the demands of your customers.

Modern digital organizations have rapidly adopted microservices-based architecture for their applications. Distributed tracing tools help monitor microservices-based applications. Choosing the right distributed tracing tool is critical. How do you know which is the right one for you? In this post, we will cover the top 13 distributed tracing tools in 2024 that can solve your monitoring and observability needs.

Nextjs is a production-ready React framework for building single-page web applications. It enables you to build fast and user-friendly static websites, as well as web applications using Reactjs. Using OpenTelemetry Nextjs libraries, you can set up end-to-end tracing for your Nextjs applications. Nextjs has its own monitoring feature, but it is only limited to measuring the metrics like core web vitals and real-time analytics of the application.

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.

In microservices architecture, observability tools enable you to create central dashboards to gauge the health of your distributed systems. New age observability tools have shifted to providing quick workflows to debug application issues. In this post we will explore top 11 observability tools that you can consider to use for your software systems. In today's digital economy, distributed architectures have become the norm.

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.

The kubectl logs tail command is a tool that allows users to stream the logs of a pod in real-time while using Kubernetes. This command is particularly useful for debugging and monitoring applications, as it enables users to view log output as it is generated and quickly identify any issues or problems with their application. In this article, we will see how to use the kubectl logs tail command to stream logs, the benefits of using the command, and an advanced tool for streaming logs.

Welcome to SigNal 36, the 36th edition of our monthly product newsletter! We crossed 10 Million Docker downloads for our open source project. We’ve enhanced our Dashboards UX and incorporated feedback from users in different areas of our product. Let’s see what humans of SigNoz were up to in the month of April 2024.