Go is a fancinating language for building backend service, while React is a modern framework for building frontend web app. I would like to share my experience on how you can put this two together to build a full stack web app.

The structure

The chance is you probably can find a template on github and develop your app based on that. But I recommend you pause for a second and think about the structure of the codebase. There are basically two options:

Grafana has developed really fast in the past few years. It has been the de facto standard for observability. I have been using grafana and loki for a while and I am really impressed by its performance and the quality of the code. Grafana builds most its backend components in Go. Meanwhile I am also using Go to build stuff. So it seems a good idea to learn some good practice from Grafana’s codebase. Then I found this https://github.com/grafana/grafana/blob/main/contribute/README.md which is a really good guide for building Go applications. I will put some of the points I found useful on coding style, database structure, error handling, etc.

The framework I am using to build this blog is Hugo. Under the hood, it uses Cobra to build the command line interface. Besides Hugo, Cobra is also used by many other popular projects such as kubectl, github cli, etc. I was curious about how it works so I decided to learn by building a simple command line app with it.

The app I am going to build is a regex tool that uses openAI’s API to generate a regex expression based on a list of strings or the semantic meaning of the given strings(e.g. email address). As this app needs to offer several options to the user, it is a nice fit for Cobra.

Goroutine is perhaps the main reason why people choose golang for their projects. It is a lightweight thread managed by the go runtime. It is also easy to use. You can simply use go keyword to start a goroutine. However, if you need to run a large number or cpu intensive goroutines, you might want to use a goroutine pool to manage them and make sure you don’t run out of memory or cpu resources. So how to implement one?

Task queue is used widely in software development. It is a mechanism to distribute work across threads or machines. It is also a way to coordinate workers to perform tasks. The scenario could be a web application that needs to process a large number of requests, a data pipeline that needs to process a large amount of data or a scheduled job that needs to run periodically. I have used task queues in different languages. In this post I will recall my experience with task queues that implemented in Python, Java, Node.js and Golang.

Background

I have been using feedly for years to read rss feeds. It is a great service however it grows more and more complicated and tries hard to sell me their premium features. I understand that they need to make money, but I decided to find a self-host solution for my simplest need: read self selected rss feeds on a clean UI. no AI recommendation, no social sharing, no fancy features. Just a simple rss reader. Then I found this miniflux. It is a very simple as they claimed they are minimalist. Yet they have all the essential features of an rss reader. The best part, It is open sourced and offers docker images for you to deploy freely, they do have a 15$/year paid hosted plan which I think is fair. I deployed one on my private k8s and it works like a charm. I then walked through the code and found it is even more enjoyable to read. I would like to share my using experience and findings here.

I recently found a nice project which offers iframe line to display stars of any github repo. I found it could be useful on my another building website which aims to track the trending repos on github. So I tried to put it on and it worked well…at first. How when I put several repos on one page the tool stopped working and don’t show the star number.

I checked its source code https://github.com/mdo/github-buttons and found it is just a simple proxy to github api which certainly has a rate limit. Before I gave up and turned to other solutions like building a backend service to fetch the data and caching, then I remembered my old friend Cloudflare. Can I do something on the edge with the help of cloudflare workers?

In my previous article I introduced how to build a sql chatbot with langchain and streamlit. I was then thinking if I can use streamlit to manage my image gallery on cloudflare. I found it was as easy as I thought, of course with a bit help of chatgpt..

Why I need this

I have been using cloudflare images service for a while and satisfied with its speed. However, it doesn’t have a good UI tool. It’s OK to login to cloudflare and upload images, but lazy as me I want to do it in a more convenient way. So I thought I could use its API to build a tool to do uploading and manangement. The solutions for this could be a web app or a desktop app. I prefer the former one because I can use it anywhere as long as I have a browser.
To build a web app, you can use framworks like react or vue, but I am not sure if I can build it in a short time. So I turned to streamlit again. It allows you to build a web app with only python and have a decent amount of built-in components.

I have a service written with nodejs when I found some requests took too long to process. so I decided to queue up the requests and process them one by one. I used Celery in python projects before, so I was looking for something similar in nodejs. Then I found Bull which is a Redis-based queue for Node. Here are some essential notes that would be helpful if you want to use Bull in your project.

I use several browser extensions in my daily work. One of the most useful one is youtube-playback-speed-co it is so simple yet so powerful and saved me a lot of time on adjusting the playback speed of youtube videos. Then I wonder how it works and if I can make one myself. So I did some research and here is what I found.