websocketd VS zap

Somewhat related: https://github.com/joewalnes/websocketd

> websocketd is a small command-line tool that will wrap an existing command-line interface program, and allow it to be accessed via a WebSocket.

I hadn't even considered collecting traces/spans in this way yet, and have taken the approach of "stuff outputting logs in JSON format to stderr/local file". I usually end up writing a (temporary, structured) log message with the relevant span tags, but wouldn't it would be much better to run the actual trace/span code and be able to verify it locally without the ad-hoc log message?

The prototype I built is a web application that creates websocket connections, and if those connections receive messages that are JSON, log lines are added. Columns are built dynamically as log messages arrive, and then you can pick which columns to render in the table. If you're curious here's the code, including a screenshot: https://github.com/corytheboyd-smartsheet/json-log-explorer

With websockets, it's very easy to use websocketd (http://websocketd.com), which will watch input files for new lines, and write them verbatim as websocket messages to listeners (the web app).

To make the idea real, would want to figure out how to not require the user to run websocketd out of band, but watching good ol' files is dead simple, and very easy to add to most code (add a new log sink, use existing log file, etc.)

* Additionally, client can stream data to the backend server (if bidirectional GRPC streams are used). I.e. client sends WebSocket messages, those will be transformed to GRPC messages by WebSocket server and delivered to the application backend.

As a result we have a system which allows to quickly create individual streams by using strict GRPC contract but terminating connections over WebSocket transport. So it works well in web browsers. After that no need to write WebSocket protocol, client implementation, handle WebSocket connection. This all will be solved by a suggested WebSocket server and its client SDKs.

The mechanics is similar to Websocketd (https://github.com/joewalnes/websocketd), but instead of creating OS processes we create GRPC streams. The difference from grpc-web (https://github.com/grpc/grpc-web) is that we provide streaming capabilities but not exposing GRPC contract to the client - just allowing to stream any data as payload (both binary and text) with some wrappers from our client SDKs side for managing subscriptions. I.e. it's not native GRPC streams on the client side - we expose just Connection/Subscription object to stream in both directions. GRPC streams used only for communication between WebSocket server and backend. To mention - grpc-web does not support all kinds of streaming now (https://github.com/grpc/grpc-web#streaming-support) while proposed solution can. This all should provide a cross-platform way to quickly write streaming apps due to client SDKs and language-agnostic nature of GRPC.

I personally see both pros and cons in this scheme (without concentrating on both too much here to keep the question short). I spent some time thinking about this myself, already have some working prototypes – but turned out need more opinions before moving forward with the idea and releasing this, kinda lost in doubts.

My main question - whether this seems interesting for someone here? Do you find this useful and see practical value?

I also used to use this (http://websocketd.com/) along with netcat(1) before just biting the bullet and writing my own websocket library for our server as we needed to scale up slightly.

Another option if you’re ready to implement the frontend part is https://github.com/joewalnes/websocketd which has the advantage of streaming the output of your script

Wanted to add websocketd [1]. It's an amazing tool to stream debugging logs to another system where you can build your webapps that accumulate alerts.

Use it only for debugging builds and not for production (obviously).

[1] https://github.com/joewalnes/websocketd

The project currently uses slog package from standard library for logging. But switching to a more advanced logger like zap could offer more flexibility and features.

It's nice to have this in the standard library, but it doesn't solve any existing pain points around structured log metadata and contexts. We use zap [0] and store a zap logger on the request context which allows different parts of the request pipeline to log with things like tenantid, traceId, and correlationId automatically appended. But getting a logger off the context is annoying, leads to inconsistent logging practices, and creates a logger dependency throughout most of our Go code.

[0] https://github.com/uber-go/zap

Kubebuilder, like much of the k8s ecosystem, utilizes zap for logging. Out of the box, the Kubebuilder zap configuration outputs a timestamp for each log, which gets formatted using scientific notation. This makes it difficult for me to read the time of an event just by glancing at it. Personally, I prefer ISO 8601, so let's change it!

What else would you expect from a structured logging package?

To me it absolutely makes sense as the default and standard for 99% of applications, and the API isn't much unlike something like Zap[0] (a popular Go structured logger).

The attributes aren't an "arbitrary" concept, they're a completely normal concept for structured loggers. Groups are maybe less standard, but reasonable nevertheless.

I'm not sure if you're aware that this is specifically a structured logging package. There already is a "simple" logging package[1] in the sodlib, and has been for ages, and isn't particularly fast either to my knowledge. If you want really fast you take a library (which would also make sure to optimize allocations heavily).

[0]: https://pkg.go.dev/go.uber.org/zap

[1]: https://pkg.go.dev/log

And finally for structured logging: https://github.com/uber-go/zap

For logging: I recommend using Uber Zap https://github.com/uber-go/zap It will log stack backtraces and makes it super easy to debug errors when deployed. I typically log in the business logic and not below. And log at the entry for failures to start the system. Maybe not necessary for this example, but it’s an essential piece of any API backend.