C vs Scala: What are the differences?
Developers describe C as "One of the most widely used programming languages of all time". . On the other hand, Scala is detailed as "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.
C and Scala can be primarily classified as "Languages" tools.
"Performance" is the top reason why over 52 developers like C, while over 177 developers mention "Static typing" as the leading cause for choosing Scala.
Scala is an open source tool with 11.8K GitHub stars and 2.75K GitHub forks. Here's a link to Scala's open source repository on GitHub.
Twitter, 9GAG, and Asana are some of the popular companies that use Scala, whereas C is used by AdRoll, Twitch, and Redis Labs. Scala has a broader approval, being mentioned in 437 company stacks & 324 developers stacks; compared to C, which is listed in 64 company stacks and 251 developer stacks.
What is C?
What is Scala?
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One important decision for delivering a platform independent solution with low memory footprint and minimal dependencies was the choice of the programming language. We considered a few from Python (there was already a reasonably large Python code base at Thumbtack), to Go (we were taking our first steps with it), and even Rust (too immature at the time).
We ended up writing it in C. It was easy to meet all requirements with only one external dependency for implementing the web server, clearly no challenges running it on any of the Linux distributions we were maintaining, and arguably the implementation with the smallest memory footprint given the choices above.
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!
Why Uber developed H3, our open source grid system to make geospatial data visualization and exploration easier and more efficient:
We decided to create H3 to combine the benefits of a hexagonal global grid system with a hierarchical indexing system. A global grid system usually requires at least two things: a map projection and a grid laid on top of the map. For map projection, we chose to use gnomonic projections centered on icosahedron faces. This projects from Earth as a sphere to an icosahedron, a twenty-sided platonic solid. The H3 grid is constructed by laying out 122 base cells over the Earth, with ten cells per face. H3 supports sixteen resolutions: https://eng.uber.com/h3/
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.
At FlowStack we write most of our backend in Go. Go is a well thought out language, with all the right compromises for speedy development of speedy and robust software. It's tooling is part of what makes Go such a great language. Testing and benchmarking is built into the language, in a way that makes it easy to ensure correctness and high performance. In most cases you can get more performance out of Rust and C or C++, but getting everything right is more cumbersome.
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.
been programming in c for over a decade, since learning it in college. still use it for various low level projects. used it recently to develop an embedded application for a custom board.
The core of the arcapos applications is written in C, so are most of the Lua modules (bindings to various hardware or protocols).
The Sqreen PHP agent is both a PHP extension, built in C, and a daemon built in Python.