COBOL vs Ruby: What are the differences?
Developers describe COBOL as "Imperative, procedural and, since 2002, object-oriented programming language". COBOL was one of the first programming languages to be standardised: the first COBOL standard was issued by ANSI in 1968. COBOL is primarily used in business, finance, and administrative systems for companies and governments. On the other hand, Ruby is detailed as "A dynamic, interpreted, open source programming language with a focus on simplicity and productivity". Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.
COBOL and Ruby belong to "Languages" category of the tech stack.
Ruby is an open source tool with 15.9K GitHub stars and 4.25K GitHub forks. Here's a link to Ruby's open source repository on GitHub.
What is COBOL?
What is Ruby?
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By mid-2015, around the time of the Series E, the Digital department at WeWork had grown to more than 40 people to support the company’s growing product needs.
By then, they’d migrated the main website off of WordPress to Ruby on Rails, and a combination React, Angular, and jQuery, though there were efforts to move entirely to React for the front-end.
The backend was structured around a microservices architecture built partially in Node.js, along with a combination of Ruby, Python, Bash, and Go. Swift/Objective-C and Java powered the mobile apps.
These technologies power the listings on the website, as well as various internal tools, like community manager dashboards as well as RFID hardware for access management.
As the WeWork footprint continued to expand, in mid-2018 the team began to explore the next generation of identity management to handle the global scale of the business.
The team decided to vet three languages for building microservices: Go, Kotlin, and Ruby. They compared the three by building a component of an identity system in each, and assessing the performance apples-to-apples.
After building out the systems and load testing each one, the team decided to implement the new system in Go for a few reasons. In addition to better performance under heavy loads, Go, according to the team, is a simpler language that will constrain developers to simpler code. Additionally, the development lifecycle is simpler with Go, since “there is little difference between running a service directly on a dev machine, to running it in a container, to running clustered instances of the service.”
In the implementation, they the Go grpc framework to handle various common infrastructure patterns, resulting in “in a clean common server pattern that we can reuse across our microservices.”
Ruby NLP C++ Grammar #BNF
At FriendlyData we had a Ruby-based pipeline for natural language processing. Our technology is centered around grammar-based natural language parsing, as well as various product features, and, as the core stack of the company historically is Ruby, the initial version of the pipeline was implemented in Ruby as well.
As we were entering the exponential growth phase, both technology- and product-wise, we looked into how could we speed up and extend the performance and flexibility of our [meta-]BNF-based parsing engine. Gradually, we built the pieces of the engine in C++.
Ultimately, the natural language parsing stack spans three universes and three software engineering paradigms: the declarative one, the functional one, and the imperative one. The imperative one was and remains implemented in Ruby, the functional one is implemented in a functional language (this part is under the NDA, while everything I am talking about here is part of the public talks we gave throughout 2017 and 2018), and the declarative part, which can loosely be thought of as being BNF-based, is now served by the C++ engine.
The C++ engine for the BNF part removed the immediate blockers, gave us 500x+ performance speedup, and enabled us to launch new product features, most notably query completions, suggestions, and spelling corrections.
I chose Sqreen because it provides an out-of-the-box Security as a Service solution to protect my customer data. I get full visibility over my application security in real-time and I reduce my risk against the most common threats. My customers are happy and I don't need to spend any engineering resources or time on this. We're only alerted when our attention is required and the data that is provided helps engineering teams easily remediate vulnerabilities. The platform grows with us and will allow us to have all the right tools in place when our first security engineer joins the company. Advanced security protections against business logic threats can then be implemented.
Installation was super easy on my Node.js and Ruby apps. But Sqreen also supports Python , Java , PHP and soon Go .
It integrates well with the tools I'm using every day Slack , PagerDuty and more.
Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.
We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.
We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.
To find out more, read our 2017 engineering blog post about the migration!
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.
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:
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.
I needed to make stack decisions accept a subset of Markdown, similarly to sites like Reddit or Stack Overflow.
Problem solved! #StackDecisionsLaunch