Observability Platform — Introduction
This post is part of a series of blogs about setting up an Observability platform for an organization. This series includes the details of observability platform components, architecture, and the tool stack used to build the platform.
- Observability Platform — Introduction
- Observability Platform — Components and tools
- Observability Platform — Metrics (Prometheus & Grafana)
- Observability Platform — Logs
- Observability Platform — Traces
An observability platform is a set of tools and practices used to gain insight into the performance, health, and behaviour of applications. It involves collecting, analyzing, and visualizing various types of data, such as logs, metrics, and traces, to understand how different system components behave.
Traditional monitoring focuses on application logs and tracking predefined metrics and thresholds to ensure the smooth operation of a system. However, when dealing with distributed systems built on microservices, relying solely on traditional monitoring might not suffice. We need deeper insights into the internal states of the system. Observability tools can provide insights into the interactions between microservices and help ensure the overall health and performance of the system. Also, observability allows us to detect anomalies and issues proactively by analyzing patterns and trends in the data, even before they reach critical levels.
Key components of an observability platform often include:
- Logging: Collecting and storing log data generated by applications, services, and infrastructure components. Logs provide a record of events and activities within a system, which can be useful for troubleshooting issues and understanding system behaviour.
- Metrics: Collecting and storing numerical data about the performance and behaviour of a system, such as CPU usage, memory utilisation, response times, and error rates. Metrics help in monitoring system health and performance over time, and they can be used to set up alerts for abnormal behaviour.
- Tracing: Capturing and analyzing distributed traces, which are records of the paths that requests take as they flow through a distributed system. Tracing helps in understanding the end-to-end latency of requests and identifying performance bottlenecks across different system components.
- Visualization and Analysis: Providing tools for visualizing and analyzing the data collected from logs, metrics, and traces. Visualization tools help in identifying patterns, trends, and anomalies in the data, while analysis tools provide capabilities for querying and exploring the data in depth.
- Alerting and Monitoring: Setting up alerts based on predefined thresholds or conditions to notify operators and developers about potential issues or anomalies in the system. Monitoring tools continuously monitor the health and performance of the system and trigger alerts when predefined conditions are met.
Overall, an observability platform enables organizations to gain comprehensive insights into the behaviour of their systems, facilitate proactive monitoring and troubleshooting, and ultimately improve the reliability, performance, and user experience of their applications and services.
We will discuss the tools and components of observability in the next part this series, Observability Platform — Components and tools
