F# vs Scala: What are the differences?
What is F#? Strongly-typed, functional-first programming language for writing simple code to solve complex problems. F# is a mature, open source, cross-platform, functional-first programming language. It empowers users and organizations to tackle complex computing problems with simple, maintainable and robust code.
What is Scala? A pure-bred object-oriented language that runs on the JVM. Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
F# and Scala belong to "Languages" category of the tech stack.
"Pattern-matching" is the primary reason why developers consider F# over the competitors, whereas "Static typing" was stated as the key factor in picking Scala.
F# and Scala are both open source tools. Scala with 11.8K GitHub stars and 2.75K forks on GitHub appears to be more popular than F# with 2.09K GitHub stars and 341 GitHub forks.
Twitter, 9GAG, and Asana are some of the popular companies that use Scala, whereas F# is used by Olo, Huddle, and Property With Potential. Scala has a broader approval, being mentioned in 437 company stacks & 324 developers stacks; compared to F#, which is listed in 19 company stacks and 16 developer stacks.
What is F#?
What is Scala?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using F#?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
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!
I've used .NET for many years, but only in recent years, after Microsoft introduced .NET Core, I've found a new love and excitement for the technology again. The main driver for us using .NET Core is not that it is cross platform compatible, open source or blazingly fast (which it is!), but the fact that we can use (what we consider) the best programming languages (mainly F# and C#) to carry out our jobs without sacrificing the other benefits.
Today we run most of our web infrastructure on .NET Core in Docker containers, deployed into a Kubernetes cluster which spans across multiple time zones in the Google Cloud and we couldn't be happier. Due to the portability of the .NET Core platform we are even able to develop many new services as serverless functions with F# which has become an absolute game changer.
Some may wonder why did we choose Grails ? Really good question :) We spent quite some time to evaluate what framework to go with and the battle was between Play Scala and Grails ( Groovy ). We have enough experience with both and, to be honest, I absolutely in love with Scala; however, the tipping point for us was the potential speed of development. Grails allows much faster development pace than Play , and as of right now this is the most important parameter. We might convert later though. Also, worth mentioning, by default Grails comes with Gradle as a build tool, so why change?
Why I am using Haskell in my free time?
I have 3 reasons for it. I am looking for:
Improve functional programming skill.
Improve problem-solving skill.
Laziness and mathematical abstractions behind Haskell makes it a wonderful language.
It is Pure functional, it helps me to write better Scala code.
Highly expressive language gives elegant ways to solve coding puzzle.
Scala is the God of languages. A legend. The Mount Rushmore of hybrid OO/functional languages is Scala's face four times over.
Ok, honestly, we love Scala. We love(d) Java (and it's parents C and C++), and we love(d) all the languages that borrowed cough stole cough from Java over the years such as Groovy, Clojure, and C#.
It may not be perfect (it totally is, but since programming languages don't have egos of their own, we don't want to paint it too bright), but it is awesome. It runs on the JVM, you can utilize Spring, it works great for data processing (which is sorta kinda the thing we do here, folks), and it just makes sense at all levels.
Nearly our entire server codebase is written in Scala (if you haven't heard of it, it's a programming language that is basically what you would get if Java + ML had a baby). This has worked out super well. It enables us to write concise easy to deal with code that is typechecked at compile time. It's also been a big help with recruiting.
worked with scala for around 2 years. really enjoyed the language and getting back into the world of functional. unfortunately the community is heavily fragmented and the language itself broken and inconsistent. that with the various factions involved made it a put of for long term investment.
Scala, Akka and Spray (which became Akka-Http) provided the building blocks for the menu service.
Akka's actors and finite-state machine were a natural way to model a USSD menu (a series of stateful interactions between a subscriber and the USSD gateway).
Replaces entirely the Java Language to build a much more expressive and powerful code on the backend, while leveraging at the same time the Java Platform Tools and Frameworks, is a mixture of old and mature with new and sexy.