validator VS Grafana

In the Golang implementation, the input for the usecase already is the validator. Here we are using go-playground/validator to do this validations, and the implementation for this is simply usign the tag validate.

The nao1215/csv package was developed with inspiration from go-playground/validator and shogo82148/go-header-csv. I would like to express my gratitude here.

go-playground/validator

If you want to know all the available validation in Gin. Then you can look at this package because Gin uses this package under the hood. Package: https://github.com/go-playground/validator Specific-file: https://github.com/go-playground/validator/blob/master/baked_in.go#L73

Now it has most of the Playground validator's common checks and a few own tricks.

In Go I've found this package - https://github.com/go-playground/validator. It seems popular in the community, but it is tag-based. It looks like if I wanted to use it - I would have to basically duplicate structs.

so I am working on a ecommerce api as a hobby project which is mostly inspired by wtf dial project I like to use validator package to remove boilerplate over my domain package for example take a look https://github.com/mortezadadgar/ecommerce-api/blob/b0bf43d042d62fdca1c2d097ec51b05bc539cef2/domain/users.go#L33 I have to option either add validate.Struct() to my domain which is suggested to avoid by author of wtf peoject or add it to handler which I doubt is a good idea as it's not in business logic of handler and makes unit testing harder

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.