Elasticsearch and MongoDB are popular document-oriented databases. While Elasticsearch is known for its advanced indexing and search capabilities, MongoDB is one of the most established NoSQL databases.

Tools like Fluentbit and Fluentd make log management more efficient by centralizing log data from multiple sources and providing the ability to monitor and analyze it all in one place.

Logs are an integral part of any system that helps you get information about the application state and how it handles its operations. The goal of this blog is to compare the commonly used logging solutions, i.e., ElasticSearch (ELK stack) and Loki (PLG stack), with SigNoz on three parameters: ingestion, query, and storage.

In this tutorial, we will learn about Docker container lifecycle. But first, let me share a personal anecdote. On a hot summer afternoon in 2021, my manager called me out of the blue and said, “Muskan, the project file you shared with me is not working on my machine. Could you please come over and fix the setup? It’s really urgent.” I rushed to his cabin and tried my best but could not fix the dependencies issues.

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.

Docker containers are transient (lasting for a very short time), spawning quickly and in high numbers, which causes metrics bursts. This makes monitoring a challenge due to Docker's scaling and redeployment features. Docker stats is a built-in feature of Docker containers. The docker stats command returns a live data stream of your running containers.

One of the easiest ways to see if the application running in our nodes is in an optimized state or not is by monitoring them. It is the last yet critical stage of any software development lifecycle. It opens up many possible improvements in your application, networking, IT automation, and other miscellaneous configurations. As we move towards microservice architecture, containerization and orchestration tools are rising.

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.

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.

Jaeger and OpenTracing are both open-source projects. Jaeger was originally built by teams at Uber and then open-sourced. The OpenTracing project was also started by teams at Uber, and hence they are compatible with each other. While Jaeger is an end-to-end distributed tracing tool, OpenTracing is a set of APIs and libraries that can be used to instrument your application.