![Lessons learned
• Avoid wacky syntax!
– Some common Scala libraries have
atrocious syntax (scalaz)
– Does this make sense to you?
val p2c = ((_: Int) * (_: Int)).curried
some(5) <*> (some(3) <*> p2c.pure[Option]) should
equal(Some(15))](https://image.slidesharecdn.com/scalainpractice-120504143317-phpapp02/75/scala-in-practice-21-2048.jpg?cb=1666621326)
Scala in practice
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While the Java platform has gained notoriety in the last 15 years as a robust application platform with a thriving ecosystem and well-established practices, the Java language has had its share of criticism. Highly verbose, overly didactic, limited feature set; whichever flavor of criticism you prefer, it's patently obvious that Java is playing catch up to more modern languages with a less rigid evolution path. The language landscape today is vastly different than it had been five or ten years ago; a wide array of languages are available, designed to suit a variety of flavors: Groovy, Clojure, Scala, Gosu, Kotlin... which should you choose? This lecture focuses on one company's decision to focus on Scala, and presents a case study based on our experiences using Scala in practice, in the hope of providing much-needed real world context to assist your decision. This presentation was used for the Scala In Practice lecture at the Botzia Israeli Java User Group meeting, May 3rd 2012.
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Scala in practice
- 1. Scala In Practice: A Case Study Tomer Gabel, newBrandAnalytics April 2012
- 3. Some context circa 2011: • Startup with limited budget • Cloud hosted, Linux based • Complex prototype codebase – Lots of Java code – Serving paying customers
- 4. Strategy 101 Supplant Evolve Refactor Complete with new codebase and extend rewrite architecture
- 5. Data points • Complete system rewrite impractical – Too much code – Business continuity a priority – Cannot afford product stagnation • Team comfortable with Java platform • … but want a better language
- 6. Why Scala? • JVM-targeted, statically typed • Success stories (Twitter, Foursquare, LinkedIn) • Active community • Good (not perfect) tool ecosystem • Reasonable learning curve
- 7. Alternatives • Dynamic • LISP dialect • Dynamic • Java-like syntax • Script-oriented • Static • Java-like syntax • In its infancy
- 8. Scala in a nutshell • Pervasive type inference
- 9. Scala in a nutshell • Type aliases, closures, first class functions and comprehensions Output:
- 10. Scala in a nutshell • Imperative with functional-style constructs
- 11. Scala in a nutshell • Traits: static mixins
- 12. Scala in a nutshell • Pattern matching FTW!
- 13. Scala in a nutshell • Other goodies – Option wrappers (no more NPEs!) – Powerful collection framework – Implicit conversions – Concurrent, immutable maps (HAMT) – Built in actors
- 15. Caveat emptor • Scala is bleeding edge • You will get cut – Learning curve – Rough edges – Partial/outdated documentation • It’s well worth it, if you’re prepared!
- 16. Focal points • Learning curve • Tooling • In production
- 17. Learning curve • Risks: – Functional constructs need getting used to – Few best practices: • Coding conventions • Patterns • “Dangerous” features
- 18. Learning curve • Mitigations: – Relatively familiar syntax – Seamless Java integration • Evolve your codebase to include Scala • Reuse existing components and libraries – Lots of wow moments!
- 19. Lessons learned “New techniques are easy to explain. Old techniques are hard to maintain.” - an old Vulcan proverb • Encourage experimentation • Encourage rapid iteration • Document and share new techniques
- 20. Lessons learned • Get a good grasp of the basics: – Functions and closures – Option wrappers – Pattern matching – Traits, traits and traits • Each of these is immensely powerful • Together, they make ponies!
- 21. Lessons learned • Avoid wacky syntax! – Some common Scala libraries have atrocious syntax (scalaz) – Does this make sense to you? val p2c = ((_: Int) * (_: Int)).curried some(5) <*> (some(3) <*> p2c.pure[Option]) should equal(Some(15))
- 22. Lessons learned • Promote functional constructs: – Intent over implementation – Massive code savings – Immutability, correct code by design • Mentor junior developers!
- 23. Lessons learned • Don’t go overboard – Use implicits sparingly – Avoid tuple overload • x._1._2 * x._2 orElse x._3 == yuck • Use case classes and partial functions instead – Path-dependent types are scary!
- 24. Focal points • Learning curve • Tooling • In production
- 25. IDE Support • Eclipse support via TypeSafe’s own ScalaIDE • IntelliJ IDEA support via plugin
- 26. IDE support • Lower your expectations • Don’t skimp on dev hardware! • Use latest IDE/plugin builds • Take your time – Grok the tool-chain – Assign a “go-to guy” (or gal)
- 27. IDE support • Wonky debugging – Java debuggers work… – … with some caveats • Step into synthetic stack frames • Do not step over closures – TypeSafe are focusing on debugging – JetBrains will likely follow
- 28. IDE support • Except the unexpected – Spurious error highlighting • Especially with implicit conversions • Double-check with your build tool- chain – Rebuild the project occasionally – The situation is improving daily
- 29. Build tools • Native Scala build tool is sbt – Ivy2 (-> Maven repositories) – Scala syntax – Very powerful, sharp learning curve • Stay away from ant • Maven and buildr should work fine
- 30. Library ecosystem • Surprisingly mature landscape! • We use: – ScalaTest + ScalaMock – Squeryl – Scalatra • Use your favorite Java libraries natively!
- 31. Focal points • Learning curve • Tooling • In production
- 32. GC considerations • Lots of generated classes – Higher PermGen utilization – Increase -XX:MaxPermSize • Lots of intermediate objects – High eden space churn (normally OK) – Tune with –XX:NewRatio
- 33. Other considerations • Scalac emits lots of synthetic code – Deep call graph – Intermediate stack frames • Default stack often too small – -Xss=2m should do
- 34. Nasty surprises • Different compiler, different edge- cases – Stack overflow may segfault – Crash log may fail to generate (JDK 1.6.0_x) – Logs and thread dumps are your friends
- 36. Codebase evolution Import Java codebase Refactoring Tipping point Slow growth Initial Scala experimentation
- 37. Conclusion • I would recommend Scala for: – Startups willing to take the plunge – Small teams of senior developers – Large enterprises with tech advisory/leadership teams • But not (yet) for: – Contractors – Traditional enterprises
- 38. Questions? … answers may be forthcoming
- 39. Afterword • newBrandAnalytics for allowing the use of the company name and R&D evidence • TypeSafe and the amazing Scala community for making Scala what it is • … and SmileTemplate.com for the PowerPoint template • Get in touch! – tomer@tomergabel.com – http://www.tomergabel.com
Hinweis der Redaktion
- Evolve codebase (in other words, keep existing infrastructure and fix/extend as needed):Frustrating, legacy codebase written under tight time constraints is a maintenance nightmareNew engineering team needs a fresh start; keeping the team in maintenance mode is hazardous to team’s mental healthRefactor and extend (break down into smaller pieces; replace infrastructure incrementally, build extension points where needed):A highly practical solution. Existing code will continue to serve existing customers;Problematic bits can be replaced piecemeal, complex new functionality built separately and integrated at the lowest possible level (usually the database);A good compromise between risk mitigation and engineering sanitySupplant with new architecture (keep existing codebase in place and fix critical bugs only; where new functionality is required, interoperate with existing architecture):Impractical. Existing codebase had too many issues;In practice this is the same as evolving the codebase, because of the prohibitively high maintenance cost on the existing architecture.Complete rewrite: too risky, too slow. ‘nuff said.