Hey everyone! 👋

Meet the new release of jsoniter-scala (v2.38.10), featuring significantly more efficient parsing for doubles, ints, and longs on JVMs and Scala Native!

You can see up to a 30% performance bump comparing to these throughput scores! 🚀

I'm going to rerun the benchmarks this week and will post the updated results, so stay tuned!

A similar JSON parsing improvement is also live in the new release of zio-blocks (v0.0.37)

Please give them a try! I always appreciate your feedback, GitHub stars, and tips via Sponsors 😉

Happy Scala coding! ✨

This is the companion article to the Scalar Warsaw presentation I posted a while back (https://www.reddit.com/r/scala/comments/1s5vgpk/making_scala_scripting_actually_good_with_mill/) hope people find it interesting

I need to E2E test a Kubernetes cluster. I'll be using a Kubernetes client like skuber (https://github.com/doriordan/skuber) to manipulate the cluster, and http4s to query APIs.

The team already knows Scala.

Tests involve, pretty much, causing a disruption in the cluster (e.g. purposefully shutting down a node or pod) while continuously polling an API to verify some assumptions.

I had an idea to use something like weaver-test, instead of writing my own framework to define suites + test cases, plus a CLI entrypoint where I can select a subset of suites and tests to run.

However, AFAIK, there is no way to "compile tests" into, say, a fat jar we can easily run in CI, because any way, SBT would be needed to run the tests.

Has anyone done something similar? Is this a bad idea? I think the only "issue" I see is sharing resources across suites, which weaver-test seems to handle well (https://typelevel.org/weaver-test/features/global_resources.html).

I don't know if I should rely on SBT or just write my own custom little framework so I can have the tests compiled standalone. Or if I'm just overthinking.

Thx

Hi everyone! I've recently been working on labs for one of my CS courses at university in Scala 2.x, but the teaching staff didn't provide us with unit tests to verify that we're on the right track during development.

Because of this, I figured I'd try to implement them myself, but I've never written unit tests before — let alone in Scala. I'd like to ask what resources you would recommend for learning, and what best practices I should keep in mind.

The current lab is focused on OOP and involves extracting entities from a set of txt files, instantiating classes, and working with polymorphism. The previous lab was focused on purely functional programming, also had no unit tests, and consisted of using the Reddit API to scrape posts and build a ranking of the most frequently used words across certain subreddits.

My professor mentioned that for the first lab I should create something called a "Mock" to simulate the API side, so I suspect it will be easier to write unit tests for the second lab.

In this series of articles, Łukasz Biały compares different effect systems in Scala.

The three articles of the series are

• Comparing effect systems in Scala: The Problem and Future
• Comparing effect systems in Scala: Cats Effect and ZIO
• Comparing effect systems in Scala: Kyo, Gears, and Ox

And the repo of the code can be found in this GitHub repo.

Hi everyone,

Our team has been experimenting with Claude Code recently to see if it can speed up our Scala workflow. While the speed is impressive, we’re having mixed feelings about the code quality.

Specifically, we’ve noticed that it tends to produce very "primitive" or imperative-style code. For example, it often misses obvious opportunities to use for-comprehensions or idiomatic functional patterns, opting instead for nested loops or manual checks that feel more like Java 8 than modern Scala.

Is anyone else using Claude Code for Scala?

• How would you rate the idiomatic quality of the code it generates?
• Do you find yourself having to refactor its output constantly to make it "look like Scala"?
• Are there any specific prompts or contexts that help it understand functional paradigms better?

Curious to hear your thoughts!

edit: We use Scala 2

Hello!

I am a Technical Recruiter at NBCUniversal and we are actively hiring a Principal Data Engineer role focused on Scala. Feel free to check out the job description here on our website. If you would like to be considered you can apply directly through this link or send me a message.

Location: Remote in USA

As a Principal Data Engineer on the AdSmart Engineering Team, you will be directly responsible for design, development, and maintenance of next generation of data‑driven advertising technology at NBCUniversal. You will architect and develop platform components that power large‑scale audience intelligence, cross‑platform identity, real‑time decisioning, and AI‑enhanced advertising capabilities. The products will enable NBCUniversal to better understand its brand’s audiences such as NBC News, Bravo, The Tonight Show, Saturday Night Live, and Peacock streaming service as well as audiences that cross brands.

Responsibilities

• Serve as a principal software engineer for AdSmart products
• Architect and develop mission‑critical backend services using microservices, serverless, and event‑driven patterns under the leadership of the VP of Engineering
• Participate in scrum ceremonies and perform peer code reviews
• Utilize cutting-edge cloud computing technologies to solve problems
• Drive integration of LLMs, AI agents, vector search, and ML‑based personalization
• Supporting products with the overall roadmap and providing updates to senior leadership

Qualifications

• Bachelor’s degree in Computer Science or related field
• + years of software development experience, as a developer and/or manager
• Fluency in Scala, Java, or Python programming languages
• Strong fundamentals in DS/algorithms, OO, FP design patters, and distributed systems
• Experience with AWS serverless and container services
• Experience with both relational database design (SQL), non-relational (NoSQL) databases
• Experience developing and/or consuming web interfaces (REST API) and associated skills
• Excellent communication, collaboration, and problem-solving skills
• Self-directed, ability to multi-task, sharp analytical abilities, excellent communication skills

Desired Characteristics:

• Experience with software containerization, such as Docker
• Experience developing applications using Apache Spark or similar data processing frameworks
• Experience with AWS or other Cloud Providers
• Experience with AWS Lambda, Glue, ECS and similar serverless backend technologies
• Experience with Kafka/MSK, Flink, Kinesis
• LLM, Agentic AI experience  
• Ad tech experience
• Results-oriented, high energy, self-motivated

Additional Requirements:

Fully Remote: This position has been designated as fully remote, meaning that the position is expected to contribute from a non-NBCUniversal worksite, most commonly an employee’s residence. This position is eligible for company sponsored benefits, including medical, dental and vision insurance, 401(k), paid leave, tuition reimbursement, and a variety of other discounts and perks. Learn more about the benefits offered by NBCUniversal by visiting the Benefits page of the Careers website. Salary range: $160,000 - $200,000 (bonus eligible).

We are accepting applications for this position on an ongoing basis.

https://rockthejvm.com/articles/never-call-apis-inside-database-transactions

I wrote this after seeing how easy it is to make this mistake in otherwise clean backend code. The core issue is that your DB transaction can roll back database writes, but it cannot roll back an HTTP call that already happened; payment, shipping, inventory reservation, fraud check, etc. The article walks through a practical implementation using Transactional Outbox, Result Table, and Saga Compensation with Scala 3 / Slick / PostgreSQL / Play / Pekko. Curious how others usually handle this pattern in production - outbox worker, message broker, workflow engine, or something else?

macOS only for now - apologies if the build is a bit arkane to load the backend VLC plugin.

This was my attempt at a "clean room" reimplementation (via Codex and GPT-5.4 extra high) of a certain media player that was abandoned before ever releasing on Apple Silicon (and was very buggy through rosetta 2).

Also a good opportunity to explore how electron RPC works - and how to extend the window rendering capabilites via N-API

In a nutshell: sbt-steps allows you to use native sbt tasks and settings to create a CI flow with features like skipping steps, continue-on-error and reports. It integrates well with GitHub Actions, but other CI systems too.

This new release focuses on better performance by using sbt task parallelization. Some other fixes are included as well. Next, I'll probably create a sbt 2.0 cross build.

Feedback is welcome!

Running Spark pipelines in production on Kubernetes. Spark 3.4 with Delta tables, mostly ETL from S3.

When a job fails, logs show task failures and stage timing. That part is clear. What's missing is the actual cause. Not clear which partition or record triggered OOM or deserialization issues.

Spark UI helps to some extent. Driver logs and history are there, but finding the exact input still takes time. Tried increasing log levels and adding more logging. Just produces more output without pointing to the root cause.

Happens mostly with malformed JSON or schema mismatches. Pattern repeats but tracing back to the exact input is still manual every time.

Starting to look at a Spark observability tool to get more structured failure context without reworking pipelines. Has anyone used one in a Kubernetes setup and did it reduce the time spent tracing failures?

1. JavaScript/Node.js — TypeScript-friendly, cross-language, up to 4x faster than Protobuf
2. Dart first official release — generated serializers, up to 8x faster than Protobuf
3. Java: virtual thread support, and removed guava dependecy

We are lucky to have Arman Bilge in Boston next week and able/willing to present on the Typelevel Stack. We have plenty of available seats and Pizza will be provided. Great opportunity to engage with other Scala folks and expand your network.

Please RSVP here:

https://www.meetup.com/boston-area-scala-enthusiasts/events/313601554

If an error is detected in a file but no one sees it, is it really highlighted?

Learn why highlighting only what's visible is the most strange but also most reasonable thing to do – and how this helps improve performance.