JaCoCo VS prometheus

I will use here JaCoCo, where also the JaCoCo-Maven-lugin exists for the usage in your Maven builds. This article will show how to configure the code coverage to finally get the results for unit- and integration-tests.

In protection rules, I added build workflow in Require status checks to pass before merging. This is to ensure that before merging code in master branch, build should run successfully. I also added Jacoco Code Coverage to make sure that enough unit tests are available in project and Detekt to make sure that code in project is readable. I added them in build configuration. Even if one of them gives error, build will fail. Whenever, someone push code in pull request, build action will run and check if build is running successfully or not.

Code coverage analysis tools quantify the amount of tested code, serving as a valuable tool to inform on code structure and testing related decisions. We will make use of JaCoCo, JaCoCo produces reports on multiple kinds of code coverage metrics including instructions, line and branch coverage.

One specific example where I regularly use a profile in this way is for configuring code coverage. In all of my Java projects, I use JaCoCo for generating code coverage reports. I use JaCoCo during the Maven test phase. However, while developing I find it useful at times to exclude coverage reporting to reduce the build time. But in my CI/CD workflows in GitHub Actions, I activate the code coverage profile during pull-requests and pushes to the default branch. For pull-requests, my GitHub Actions workflow comments the code coverage on the PR and uploads the coverage report as a workflow artifact, where I can inspect it as necessary. And during a push to the default branch, my workflow updates coverage badges to keep them up to date with the current state of the default branch. I can also activate the code coverage profile locally while developing, such as prior to submitting a pull-request, to ensure that I didn't miss testing something.

In Unit tests, individual software code components are checked if it is working as expected or not. Unit tests isolate a function or module of code and verify its correctness. We can use tools like JaCoCo for Java and Mocha, and Jasmine for NodeJS to generate unit test reports. We can also send these reports to SonarQube which shows us code coverage and the percentage of your code covered by your test cases.

And there is the original jacoco/jacoco: (0.8.7: released this on May 5, 2021), but it's for Java. I'm not sure if we can use it with multiple flavors on Android.

Make sure to also update to Jacoco 0.8.7 to avoid test issues: https://github.com/jacoco/jacoco/releases/tag/v0.8.7

It's like Prometheus, but for logs. Okay it's not really to do with the Norse or Greek gods, instead Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by the open source project Prometheus. Built by Grafana Labs, Loki is designed for ease of use. Instead of indexing the contents of the logs, Loki provides a set of labels for each log stream. The latest update includes query acceleration with Bloom filters, native OTel support, Helm charts, and more. Check out the changelog for all the major changes and deprecations.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.