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Go vs Markdown: What are the differences?

Developers describe Go as "An open source programming language that makes it easy to build simple, reliable, and efficient software". Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language. On the other hand, Markdown is detailed as "Text-to-HTML conversion tool/syntax for web writers, by John Gruber". Markdown is two things: (1) a plain text formatting syntax; and (2) a software tool, written in Perl, that converts the plain text formatting to HTML.

Go and Markdown can be categorized as "Languages" tools.

"High-performance", "Simple, minimal syntax" and "Fun to write" are the key factors why developers consider Go; whereas "Easy formatting", "Widely adopted" and "Intuitive" are the primary reasons why Markdown is favored.

Go is an open source tool with 60.4K GitHub stars and 8.36K GitHub forks. Here's a link to Go's open source repository on GitHub.

Uber Technologies, Pinterest, and Square are some of the popular companies that use Go, whereas Markdown is used by Asana, Code School, and GoSquared. Go has a broader approval, being mentioned in 901 company stacks & 606 developers stacks; compared to Markdown, which is listed in 756 company stacks and 718 developer stacks.

- No public GitHub repository available -

What is Go?

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

What is Markdown?

Markdown is two things: (1) a plain text formatting syntax; and (2) a software tool, written in Perl, that converts the plain text formatting to HTML.
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What are some alternatives to Go and Markdown?
Python
Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
Java
Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!
Node.js
Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.
PHP
Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.
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Decisions about Go and Markdown
Antonio Sanchez
Antonio Sanchez
CEO at Kokoen GmbH · | 13 upvotes · 139.1K views
atKokoen GmbHKokoen GmbH
PHP
PHP
Laravel
Laravel
MySQL
MySQL
Go
Go
MongoDB
MongoDB
JavaScript
JavaScript
Node.js
Node.js
ExpressJS
ExpressJS

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.

We also decided to switch the website from PHP and Laravel to JavaScript and Node.js and ExpressJS since working with the JSON Data that we were saving now in the Database would be easier.

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

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Yashu Mittal
Yashu Mittal
Founder & CEO at CodeCarrot · | 1 upvotes · 10.2K views
atCodeCarrotCodeCarrot
Jekyll
Jekyll
Ruby
Ruby
Markdown
Markdown

Jekyll is an open source static site generator (SSG) with a Ruby at its core which transform your plain text into static websites and blogs.

It is simple means no more databases, comment moderation, or pesky updates to install—just your content. As said earlier SSG uses Markdown, Liquid, HTML & CSS go in and come out ready for deployment. Lastly it's blog-aware permalinks, categories, pages, posts, and custom layouts are all first-class citizens here.

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Nitzan Shapira
Nitzan Shapira
at Epsagon · | 12 upvotes · 148.6K views
atEpsagonEpsagon
Python
Python
Serverless
Serverless
npm
npm
Node.js
Node.js
Go
Go
Java
Java
GitHub
GitHub
AWS Lambda
AWS Lambda

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

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Johnny Bell
Johnny Bell
Senior Software Engineer at StackShare · | 14 upvotes · 299.4K views
atStackShareStackShare
Markdown
Markdown
React
React
GraphQL
GraphQL
Ruby
Ruby
Showdown
Showdown
Glamorous
Glamorous
Emotion
Emotion
styled-components
styled-components
#Frontend
#CssInJs
#StackDecisionsLaunch

For Stack Decisions I needed to add Markdown in the decision composer to give our users access to some general styling when writing their decisions. We used React & GraphQL on the #Frontend and Ruby & GraphQL on the backend.

Instead of using Showdown or another tool, We decided to parse the Markdown on the backend so we had more control over what we wanted to render in Markdown because we didn't want to enable all Markdown options, we also wanted to limit any malicious code or images to be embedded into the decisions and Markdown was a fairly large to import into our component so it was going to add a lot of kilobytes that we didn't need.

We also needed to style how the markdown looked, we are currently using Glamorous so I used that but we are planning to update this to Emotion at some stage as it has a fairly easy upgrade path rather than switching over to styled-components or one of the other cssInJs alternatives.

Also we used React-Mentions for tagging tools and topics in the decisions. Typing @ will let you tag a tool, and typing # will allow you to tag a topic.

The Markdown options that we chose to support are tags: a, code, u, b, em, pre, ul, ol, li.

If there are anymore tags you'd love to see added in the composer leave me a comment below and we will look into adding them.

#StackDecisionsLaunch

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Jerome Dalbert
Jerome Dalbert
Senior Backend Engineer at StackShare · | 5 upvotes · 12.9K views
atStackShareStackShare
Markdown
Markdown
Ruby
Ruby
Rails
Rails
#StackDecisionsLaunch

I needed to make stack decisions accept a subset of Markdown, similarly to sites like Reddit or Stack Overflow.

I used the redcarpet Ruby gem for parsing, and Rails' sanitize helper made it very easy to only allow certain tags: links, bold, italics, lists, code blocks, paragraphs.

Problem solved! #StackDecisionsLaunch

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Omar Mehilba
Omar Mehilba
Co-Founder and COO at Magalix · | 15 upvotes · 74.4K views
atMagalixMagalix
Kubernetes
Kubernetes
Microsoft Azure
Microsoft Azure
Google Kubernetes Engine
Google Kubernetes Engine
Amazon EC2
Amazon EC2
Go
Go
Python
Python
#Autopilot

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!

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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 19 upvotes · 1M views
atUber TechnologiesUber Technologies
Jaeger
Jaeger
Python
Python
Java
Java
Node.js
Node.js
Go
Go
C++
C++
Kubernetes
Kubernetes
JavaScript
JavaScript
OpenShift
OpenShift
C#
C#
Apache Spark
Apache Spark

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: