sponge VS Grafana

Install a scaffold named sponge (integrated with Gin + Gorm), which supports Windows, macOS, and Linux environments. Click to view the installation instructions for sponge.

Sponge can be installed on Windows, macOS, and Linux. Check the installation instructions for details.

Using the tool sponge, the business logic code and non-business logic code are automatically separated during the code generation process, so that the development only needs to focus on the business logic code.

With the help of a code generation tool, sponge, download here https://github.com/zhufuyi/sponge

Download Address：https://github.com/zhufuyi/sponge

Sponge is a quick creation of web services and microservice tools, but also a microservice framework, sponge has a wealth of generated code commands, commonly used repetitive code and scripts are automatically generated, coupled with code decoupling modular design, it is easy to build a complete code project from development to deployment, thereby improving development efficiency.

Dependency plugins and tools are automatically installed after executing the command: protoc-gen-go, protoc-gen-go-grpc, protoc-gen-validate, protoc-gen-gotag, protoc-gen-go-gin, protoc-gen-go-rpc-tmpl, protoc-gen-openapiv2, protoc-gen-doc, golangci-lint, swag, go-callvis.

sponge is a microservice framework, a tool for quickly creating microservice code. sponge has a rich generating code commands, a total of 12 different functional code, these functional code can be combined into a complete service (similar to artificially broken sponge cells can be automatically reorganized into a new sponge ). Microservice code features include logging, service registration and discovery, registry, rate limit, circuit breaker, trace, metrics monitoring, pprof performance analysis, statistics, caching, CICD. The code uses a decoupled layered structure and it's easy to add or replace functional code. As an efficiency-enhancing tool, commonly repeated code is basically generated automatically and only business logic code needs to be populated based on the generated template code examples.

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.

Prometheus: Open-source monitoring system. Often used together with Grafana.

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.