Leading software vendors have completed the range from traditional monitoring to full overview. However, there is still a knowledge gap for customers interested in the observation. As a result, some are feeling a bit cloudy about cloud observations. Hopefully, this article will help in adding clarity to the subject.
What is observability? Explained
So, what exactly does observability mean? Let’s start with the official definition. as defined by Wikipedia, “Observability is a measure of how well the internal states of a system can be predicted from the knowledge of its external outputs.”
We are already familiar with traditional surveillance. So, observability is simply a superset of surveillance. All elements of monitoring are also elements of observability. Look at the diagram below. The term observability has been defined and applied to cloud computing to obtain actionable insights using full-fidelity data.
Well-performing applications are critical to the growth of many online businesses and organizations that require observation supported by traditional monitoring methods.
Monitoring is necessary to have observability into the inner workings of your system. Adds additional insight by using observability matrix, tracingAnd Logging.
Observability Metrics, Tracing and Logging (Telemetry)
diagram by Peter Borgen,
Let’s see the importance of matrix, tracingAnd Logging As described in the book Distributed Systems Observability Cindy Sridharani,
- matrix It is a numerical representation of the data measured over an interval of time. Metrics can use the power of mathematical modeling and prediction to gain knowledge of the behavior of systems at present and in the future over time intervals. Since numbers are optimized for storage, processing, compression, and retrieval, metrics enable data to be retained for longer periods of time and easier to query. This factor makes the metric perfectly suited for building dashboards that reflect historical trends. Metrics also allow the data resolution to be gradually reduced. After a certain period of time, the data can be aggregated into daily or weekly frequencies.
- tracing – It represents a series of causality-related distributed events that encode the end-to-end request flow through a distributed system. Traces are representing logs; The data structure of the trace looks almost like an event log. A single trace can provide visibility into both the path traversed by the request and the structure of the request. The path of the request allows software engineers and SREs to understand the various services involved in the path of the request, and the structure of the request helps to understand the junctions and implications of asynchronous in the execution of the request.
- Logging – Logs are immutable, time-stamped records of discrete events that have occurred over time. Essentially a timestamp and a payload for each event context.
In a little over a year, we’ve covered the future of APM (Application Performance Monitoring) and the expansion of APM into observability. This is followed by a race between software vendors to define observability.
Recently, we looked at the evolution of an overview shared by industry-leading software vendors. In this article, we have defined observation. Plus, check out my seller-neutral list of current leaders in overview.