Clojure vs Scala: What are the differences?
Clojure: A dynamic programming language that targets the Java Virtual Machine. Clojure is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language - it compiles directly to JVM bytecode, yet remains completely dynamic. Clojure is a dialect of Lisp, and shares with Lisp the code-as-data philosophy and a powerful macro system; 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.
Clojure and Scala belong to "Languages" category of the tech stack.
"It is a lisp", "Concise syntax" and "Persistent data structures" are the key factors why developers consider Clojure; whereas "Static typing", "Jvm" and "Pattern-matching" are the primary reasons why Scala is favored.
Clojure and Scala are both open source tools. It seems that Scala with 11.8K GitHub stars and 2.75K forks on GitHub has more adoption than Clojure with 7.85K GitHub stars and 1.25K GitHub forks.
According to the StackShare community, Scala has a broader approval, being mentioned in 437 company stacks & 324 developers stacks; compared to Clojure, which is listed in 95 company stacks and 80 developer stacks.
What is Clojure?
What is Scala?
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Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).
The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.
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!
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?