springdoc-openapi VS Grafana

The issue is that the springdoc-openapi BOM brings an old version of the Spring Framework 6.0, which is incompatible with Spring Boot 3.2. There are several ways to solve this problem: update springdoc, change the order of BOM imports, but the best, in my opinion, is to avoid using the io.spring.dependency-management plugin.

I would suggest using Springdoc

The SpringDoc library comes with lots of annotations to tune your REST API specification precisely. Anyway, that's out of context of this article.

I found SpringDoc, a library that automates the generation of the spec from the source code. It relies on annotations for textual bits (like tags and descriptions), but it also infers stuff from Spring annotations.

This is an API made with Spring Web, uses springdoc-openapi-ui to expose a swagger-ui on http://localhost:8080/swagger-ui/index.html

Libraries like Springdoc or Springfox can do this. These libraries generate the OpenAPI documentation based on your controllers (+ you can apply the OpenAPI annotations on your controllers). This documentation is then exposed as a REST API, for Springdoc these can be found at /v3/api-docs.

Retrieving all endpoints of a service isn't the goal of a service registry like Eureka, so no, you can't get all endpoints of a service. You can use a library like Springfox or Springdoc to enable Swagger/OpenAPI for your project. These libraries generate a JSON REST API (and a user interface) to view all your endpoints. You can even provide additional information (eg. default values, descriptions, ...) by adding some additional annotations on your controllers.

The springdoc-openapi helps automating the generation of API documentation using Spring Boot projects GitHub - springdoc/springdoc-openapi

Our microservice accept http requests: For swagger used Swagger OpenAPI 3. The bank account REST controller, which accept requests, validate it using Hibernate Validator, then call command or query service. The main reason for CQRS gaining popularity is the ability to handle reads and writes separately due to severe differences in optimization techniques for those much more distinct operations.

Today, I’d like to touch on open-source intelligence, or OSINT. According to Wikipedia, open-source intelligence is the collection and analysis of data gathered from open sources (covert sources and publicly available information) to produce actionable intelligence. As you can infer from the definition, OSINT is a vast topic, and the best way to understand such broad topics is through concrete, narrow-scoped practical examples. In this blog post, I’d like to share one of the approaches on how OSINT techniques can be applied to bug bounty hunting for products with publicly hosted code on GitHub, using the awesome open-source project Grafana as an example. Read on!

Speaking of metrics and Grafana, this popular project gets a major update too. As shown in the image above, Grafana is a data visualisation and composable observability platform. With Grafana you can query, visualise, alert on, and understand your metrics wherever that data may sit. The latest update adds lots of new features and enhancements such as a slightly refreshed UI, reducing the set of fields that could trigger an alert state change, the removal of Loki's API restrictions on resource calls, and lots more. Check out all the changes in the changelog.

Implement health checks and monitoring to ensure the availability and performance of your microservices. Use tools like Prometheus, Grafana, or NestJS built-in health checks.

In this article, we’ll look at one of the ways to monitor the InterSystems IRIS data platform (IRIS) deployed in the Google Kubernetes Engine (GKE). The GKE integrates easily with Cloud Monitoring, simplifying our task. As a bonus, the article shows how to display metrics from Cloud Monitoring in Grafana.

Kubernetes Documentation: https://kubernetes.io/docs/home/ Kubernetes Tutorials: https://kubernetes.io/docs/tutorials/ Kubernetes Community: https://kubernetes.io/community/ Prometheus: https://prometheus.io/ Grafana: https://grafana.com/ Elasticsearch: https://www.elastic.co/elasticsearch/ Kibana: https://www.elastic.co/kibana Helm: https://helm.sh/ Prometheus Helm Chart: https://github.com/prometheus-community/helm-charts/tree/main/prometheus Grafana Helm Chart: https://github.com/grafana/helm-charts/tree/main/grafana Elasticsearch Helm Chart: https://github.com/elastic/helm-charts/tree/main/elasticsearch Kibana Helm Chart: https://github.com/elastic/helm-charts/tree/main/kibana RBAC: https://kubernetes.io/docs/reference/access-authn-authz/rbac/ Network Policies: https://kubernetes.io/docs/concepts/services-networking/network-policies/ StatefulSets: https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/ DaemonSets: https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/ Taints and Tolerations: https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/ Custom Resource Definitions (CRDs): https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/ Operators: https://kubernetes.io/docs/concepts/extend-kubernetes/operator/

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.