swagger-editor VS m3

To test it out, you can copy the generated build file and paste it in Swagger live editor.

One of the powerful features of OpenAPI is the ability to generate code automatically. Using tools like Swagger, you can generate both client and server code using options "Generate Server" or "Generate Client" available on https://editor.swagger.io/

Install Swagger tools like Swagger Editor, Swagger UI, and Swagger Codegen.

Having docs as the JSON file is the most important, but can we also present it somehow? Sure we can. Enter Swagger UI! And I am not talking about the Swagger Editor, but our very own instance!

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/

M3DB.

> I can't find any other open source time series database except Mimir/Cortex which allows this much scale (clustering options in their open source version)

The following open source time series databases also can scale horizontally to many nodes:

- Thanos - https://github.com/thanos-io/thanos/

- M3 - https://github.com/m3db/m3

- Cluster version of VictoriaMetrics - https://docs.victoriametrics.com/Cluster-VictoriaMetrics.htm... (I'm CTO at VictoriaMetrics)

> Can we use Prometheus/Mimir as general purpose time series database?

This depends on what do you mean under "general purpose time series database". Prometheus/Mimir are optimized for storing (timestamp, value) series where timestamp is a unix timestamp in milliseconds and value is a floating-point number. Each series has a name and can have arbitrary set of additional (label=value) labels. Prometheus/Mimir aren't optimized for storing and processing series of other value types such as strings (aka logs) and complex datastructures (aka events and traces).

So, if you need storing time series with floating-point values, then Prometheus/Mimir may be a good fit. Otherwise take a look at ClickHouse [1] - it can efficiently store and process time series with values of arbitrary types.

[1] https://clickhouse.com/