swagger-editor VS prometheus

I did the same for the other API and adjusted the spec using the Swagger Editor. Specifically, I added detailed descriptions that should help the agent understand the API usages. The resulting OpenAPI YAML file is as follows:

It has an online editor. You can easily play around with it and generate easy-to-use documentation.

After running the collection, I can see the API spec that was created and the mock server endpoint to test it. Looking at the rendered version on Swagger's OAS editor we can see pretty clearly this is a complete API that gets us exactly what we need.

While there’s not an officially supported Java SDK for Marqeta, building a Java client is quite straightforward, as the Core API is documented in both Swagger v2.0 and OpenAPI v3.0. The OpenAPI documentation is in beta, but it is generated directly from the API source code. To get a Java client, all we need to do is drop the OpenAPI yaml file into editor.swagger.io, modify the servers section to use the https://sandbox-api.marqeta.com/v3 as the URL, and tell it to generate a Java client.

Check out https://editor.swagger.io/ as a start point. In theory you should be able to generate a client for any swagger complient api and plug in your own auth and custom logic.

I would suggest using the swagger editor: https://editor.swagger.io/

Paste your json into https://editor.swagger.io and it will ask you if you want to convert it to yaml

Sure. You can use the editor from here for instance to define your endpoints and the data received and returned. By looking at the preloaded example you can figure out most of what you need to know about openapi. But if you need more info, the official documentation is pretty good.

Optional: If you make any changes to the plugin instructions or metadata models, you can also copy the contents of main.py into the main main.py file. This will allow you to access the openapi.json at http://0.0.0.0:8000/sub/openapi.json when you run the app locally. You can convert from JSON to YAML format with Swagger Editor. Alternatively, you can replace the openapi.yaml file with an openapi.json file.

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.