How far does system monitoring reach? How do we know what to observe? Which monitoring tool to choose? Let’s dive into the topic.
For demonstration purposes, we implemented two dummy microservices using aiohttp framework and deployed it on localhost alongside with PostgreSQL database. In this post, we will cover a single request timing breakdown. We will be using Elastic solutions to visualize the results.
We’ve built a high density and high capacity network. In order to properly operate such network, we designed a custom monitoring and analytics solution based on Elastic Stack and MQTTbeat receiving the measurements from all 494 IoT gateways (i.e. 494 Wi-Fi APs) through an MQTT broker. In this post I’m sheding some light on the different aspects of the solution and custom dashboards.
There are multiple platforms that help you with handling tickets. If you are starting your business, you might simply reply to those emails directly via your mail client, but it quickly becomes too hard to follow and handle all the issues. We chose the tool, from the same vendor, we were already using for different purpose for easy integration. It was great for following threads, tagging and grouping issues, taking notes and automating responses. In this tool we had access to basic statistics like ticket count or time graph with incoming tickets, but we were interested in more advanced stats, which could give us more insight into what is going on. That’s why we decided to integrate that tool with already used by us Elastic Stack.