Rust vs Scala: What are the differences?
Developers describe Rust as "A safe, concurrent, practical language". 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. 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.
Rust and Scala belong to "Languages" category of the tech stack.
"Guaranteed memory safety" is the primary reason why developers consider Rust over the competitors, whereas "Static typing" was stated as the key factor in picking Scala.
Rust and Scala are both open source tools. Rust with 37.3K GitHub stars and 5.85K forks on GitHub appears to be more popular than Scala with 11.8K GitHub stars and 2.75K GitHub forks.
According to the StackShare community, Scala has a broader approval, being mentioned in 437 company stacks & 324 developers stacks; compared to Rust, which is listed in 39 company stacks and 105 developer stacks.
What is Rust?
What is Scala?
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To handle its growing observability needs, Postmates created and open sourced Cernan, a telemetry and logging aggregation server. Ceran is built on Rust and Lua, and can ingest data from many sources and then push or exposes what it’s collected to many destinations, or “sinks.” It can also create or manipulate in-flight data with programmable Lua filters.
Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.
For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).
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!
Initially, I wrote my text adventure game in C++, but I later rewrote my project in Rust. It was an incredibly easier process to use Rust to create a faster, more robust, and bug-free project.
One difficulty with the C++ language is the lack of safety, helpful error messages, and useful abstractions when compared to languages like Rust. Rust would display a helpful error message at compile time, while C++ would often display "Segmentation fault (core dumped)" or wall of STL errors in the middle. While I would frequently push buggy code to my C++ repository, Rust enabled me to only even submit fully functional code.
Along with the actual language, Rust also included useful tools such as rustup and cargo to aid in building projects, IDE tooling, managing dependencies, and cross-compiling. This was a refreshing alternative to the difficult CMake and tools of the same nature.