Microservice Observability Patterns [Part 2] - Evolutionary Patterns for Solving Observability Problems. Logs are easy to integrate into your application and provide the ability to represent any type of data in the form of strings.
In my previous article, I talked about the importance of logs and the differences between structured and unstructured logging. Logs are easy to integrate into your application and provide the ability to represent any type of data in the form of strings.
Metrics, on the other hand, are a numerical representation of data. These are often used to count or measure a value and are aggregated over a period of time. Metrics give us insights into the historical and current state of a system. Since they are just numbers, they can also be used to perform statistical analysis and predictions about the system’s future behaviour. Metrics are also used to trigger alerts and notify you about issues in the system’s behaviour.
Logs are represented as strings. They can be simple texts, JSON payloads, or key-value pairs (like we discussed in structured logging).
Metrics are represented as numbers. They measure something (like CPU usage, number of errors, etc.) and are numeric in nature.
Logs contain high-resolution data. This includes complete information about an event and can be used to correlate the flow (or path) that the event took through the system.
In case of errors, logs contain the entire stack trace of the exception, which allows us to view and debug issues originating from downstream systems as well. In short, logs can tell you what happened in the system at a certain time.
Metrics contain low-resolution data. This may include a count of parameters (such as requests, errors, etc.) and measures of resources (such as CPU and memory utilization). In short, metrics can give you a count of something that happened in the system at a certain time.
How to best monitor your external and third party API integrations and hold partners accountable to SLAs
On Wednesday, March 11, 2020, I conducted the webinar titled “Monitoring & Orchestrating Your Microservices Landscape using Workflow Automation”. Not only was I overwhelmed by the number of attendees, but we also got a huge list of interesting questions before and especially during the webinar. Some of them were answered, but a lot of them were not. I want to answer all open questions in this series of seven blog posts. Today I am posting the final two in the series.
In this article you'll find out exploring differences Between Monitoring and Observabilit. Monitoring vs Observability: We're explaining what is observability exactly and how does it differ from monitoring.
In this article, look at different ways to test microservices and how you can have a suitable testing strategy to begin with.
On Wednesday, March 11, 2020, I conducted the webinar titled “Monitoring & Orchestrating Your Microservices Landscape using Workflow Automation”. Not only was I overwhelmed by the number of attendees, but we also got a huge list of interesting...