CoffeeScript vs Go: What are the differences?
CoffeeScript and Go belong to "Languages" category of the tech stack.
"Easy to read", "Faster to write" and "Syntactic sugar" are the key factors why developers consider CoffeeScript; whereas "High-performance", "Simple, minimal syntax" and "Fun to write" are the primary reasons why Go is favored.
CoffeeScript and Go are both open source tools. It seems that Go with 60.4K GitHub stars and 8.36K forks on GitHub has more adoption than CoffeeScript with 15.2K GitHub stars and 1.99K GitHub forks.
Uber Technologies, Pinterest, and Square are some of the popular companies that use Go, whereas CoffeeScript is used by Code School, Zaarly, and thoughtbot. Go has a broader approval, being mentioned in 901 company stacks & 606 developers stacks; compared to CoffeeScript, which is listed in 364 company stacks and 170 developer stacks.
What is CoffeeScript?
What is Go?
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Choosing to add TypeScript has given us one more layer to rely on to help enforce code quality, good standards, and best practices within our engineering organization. One of the biggest benefits for us as an engineering team has been how well our IDEs and editors (e.g., Visual Studio Code ) integrate with and understand TypeScript . This allows developers to catch many more errors at development time instead of relying on run time. The end result is safer (from a type perspective) code and a more efficient coding experience that helps to catch and remove errors with less developer effort.
We've been a big fan of Python ever since we adopted it for my first startup, Ksplice. But it's been an absolutely ideal tool for Zulip, which is now one of the leading alternatives to Slack. Zulip is 100% open source software, with ~10K stars on GItHub. And being written in idiomatic Python has been really helpful for our open source project, because it's such an accessible language: Any programmer can learn Python quickly. And that means we're not restricted to e.g. "folks who are excited about contributing to Zulip and ALSO know Go".
I've linked to a blog post I wrote on Python's awesome new static type system, which fixes the main complaint one might have about using Python for a large codebase, which has a lot more perspective, as well as some commentary on our Python 3 migration.
Back at the start of 2017, we decided to create a web-based tool for the SEO OnPage analysis of our clients' websites. We had over 2.000 websites to analyze, so we had to perform thousands of requests to get every single page from those websites, process the information and save the big amounts of data somewhere.
Very soon we realized that the initial chosen script language and database, PHP, Laravel and MySQL, was not going to be able to cope efficiently with such a task.
By that time, we were doing some experiments for other projects with a language we had recently get to know, Go , so we decided to get a try and code the crawler using it. It was fantastic, we could process much more data with way less CPU power and in less time. By using the concurrency abilites that the language has to offers, we could also do more Http requests in less time.
Unfortunately, I have no comparison numbers to show about the performance differences between Go and PHP since the difference was so clear from the beginning and that we didn't feel the need to do further comparison tests nor document it. We just switched fully to Go.
There was still a problem: despite the big amount of Data we were generating, MySQL was performing very well, but as we were adding more and more features to the software and with those features more and more different type of data to save, it was a nightmare for the database architects to structure everything correctly on the database, so it was clear what we had to do next: switch to a NoSQL database. So we switched to MongoDB, and it was also fantastic: we were expending almost zero time in thinking how to structure the Database and the performance also seemed to be better, but again, I have no comparison numbers to show due to the lack of time.
As of now, we don't only use the tool intern but we also opened it for everyone to use for free: https://tool-seo.com
At Epsagon, we use hundreds of AWS Lambda functions, most of them are written in Python, and the Serverless Framework to pack and deploy them. One of the issues we've encountered is the difficulty to package external libraries into the Lambda environment using the Serverless Framework. This limitation is probably by design since the external code your Lambda needs can be usually included with a package manager.
In order to overcome this issue, we've developed a tool, which we also published as open-source (see link below), which automatically packs these libraries using a simple npm package and a YAML configuration file. Support for Node.js, Go, and Java will be available soon.
The GitHub respoitory: https://github.com/epsagon/serverless-package-external
We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!