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When discussing the technical foundations of observability, several key components, often referred to as the “pillars,” emerge. While there is no universally agreed-upon number of pillars, this post will focus on four fundamental elements: metrics, logs, traces, and profiles.
Due to the vast amount of data generated by metrics, logs, and traces, sampling is often employed to reduce data volume while maintaining representative information. However, it’s important to balance the need for data reduction with the potential loss of valuable insights.
Effectively visualizing observability data is crucial for gaining insights. Tools and dashboards should be designed to present information in a clear and actionable manner.
The fourth pillar of observability is profiling. Unlike metrics, logs, and traces, which provide a broad overview of system behavior, profiling focuses on the granular details of code execution.
A key advantage of profiling is its ability to provide insights at the code level, enabling developers to pinpoint performance issues with precision. However, profiling can be resource-intensive and requires careful consideration of when and how to collect data.
To collect the data necessary for metrics, logs, traces, and profiles, instrumentation is required. Instrumentation involves adding code or agents to applications and infrastructure to capture relevant data points.
There are two primary types of instrumentation:
Instrumentation is the process of collecting data from a system for observability purposes. There are several key types:Â Â
When choosing instrumentation methods, it’s essential to balance the need for data with the potential performance impact.
Two critical technologies for observability are Open Metrics and Open Telemetry:
While Open Metrics is more focused, Open Telemetry provides a more comprehensive solution for observability.
eBPF (Extended Berkeley Packet Filter) has emerged as a transformative technology in the observability landscape. It empowers developers to dynamically instrument applications and systems without modifying source code, providing unparalleled flexibility and efficiency.
The RED method (Rate, Error, Duration) offers a structured approach to monitoring and analyzing system health. Developers can effectively identify and address performance issues by focusing on these three key metrics.
The RED method aligns well with SRE (Site Reliability Engineering) practices by enabling the definition of SLIs (Service Level Indicators) and SLOs (Service Level Objectives).
By leveraging eBPF to collect data on rate, error, and duration, organizations can gain deep insights into system performance and identify potential issues proactively. This combination provides a robust foundation for building reliable and efficient applications.
Key benefits of this approach:
Effective observability is built on a solid foundation of metrics, logs, traces, and profiles. By understanding these pillars and the instrumentation techniques to collect them, organizations can gain unprecedented visibility into their systems. The RED method provides a practical framework for applying these insights to assess service health and identify areas for improvement.
As technology continues to evolve, the importance of observability will only grow. By embracing these concepts and tools, organizations can build more resilient, efficient, and reliable systems.
To learn about common observability challenges and how Coroot addresses them, check out our previous post, “Conquering observability challenges with Coroot”
