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Using Functional Programming to improve your code: A working example
- 1. Using Functional Programming to improve your code: A working example @Jorgesg1986 実例で解説する関数型プログラミングを用いたコードの改善
- 3. Our first requirement as a Scala developer • We work for an analytics company. • As a user I would like to see if bad or good things are being said about Trump and where. 最初の案件 : トランプ大統領の風評分
- 5. Our company’s architecture 自然言語解析とかも Spark 経由で
- 6. After some investigation... • Spark for stream processing. • Stanford CoreNLP for Sentiment Analysis. • Kafka for communication between servers. • Avro4s for Avro serialization. 各要素の役割
- 7. Defining our data model tweet に対応するデータモデ
- 10. Error handling • Scala Standard Library: – Option[T]: Some(T) or None – Try: Failure(Non-Fatal) or Success(T) – Either[A, B]: Left(A) or Right(B). Unbiased prior to 2.12 Scala 標準ライブラリのエラーハンドリン
- 11. Error handling • Cats: – Either: Same as standard. Right biased. – Validated: Useful to accumulate errors. • ScalaZ: – /: Either. Right biased. – Validation: Useful to accumulate errors. Cats, Scalaz の場合
- 12. Revisiting our first approach
- 14. Let’s see how that works...
- 15. All the work is done now… これで仕事は終わ
- 16. そんなはずはない
- 17. Testing it • Deploy to some development environment and test. 開発環境にデプロイしてテス ト
- 18. Testing it • Deploy to some development environment and test. • Refactor the code. リファクタリン
- 19. Improving our first approach • Try to look only for your Business Logic. • Separate from the specifics of your implementation. ビジネスロジックを分離
- 21. Our Business Logic • Filter the tweets that are not written in English. • Obtain the location from where the tweet was sent. • Analyze the text to extract the sentiment. • Return a new instance of type Tweet. • Extra: Refactor the Avro serialization. 英語の tweet のみ抽出、ロケーションの取得、テキストから感情を分 析、
- 22. Refactoring the Avro serialization Avro のシリアライズのリファクタリン グ
- 25. And test! • You can now test ONLY your Business Logic without caring about your implementation (In this case Spark). • Use your choice of testing framework. ScalaTest, Specs2, µTest, Puretest, minitest... 実装に依存しないビジネスロジックのテストができました。
- 26. Keep testing! • What about integration tests? 結合テストは？
- 27. What happens if... • The framework changes? フレームワークが変わった場合どうなるでしょ う？
- 28. What happens if... • The framework changes? • Akka Streams Akka Streams の場 合
- 29. Akka Streams • Processing Stages • Source, Flow, Sink
- 31. Akka Streams Source Sink Flow Output port Output port
- 32. Akka Streams Source Sink Flow Output port Output port Input port Input port
- 33. Akka Streams Source Sink Flow Output port Output port Input port Input port
- 34. Akka Streams
- 35. Running Asynchronously • Using Scala Futures 非同期に実行する場合
- 36. Running Asynchronously “It represents space: a value, detached from time” Viktor Klang それは空間、つまり時間から分離した値を表 す。
- 37. Revisiting our first approach • Run our business logic asynchronously. 最初の方法をおさらい 各ビジネスロジックを非同期に実行
- 38. Revisiting our first approach Monad モナド
- 39. Revisiting our first approach Monad Applicative Functor アプリカティブ・ファンクタ ー
- 40. Revisiting our first approach Monad Applicative Functor
- 41. Revisiting our first approach Monad Applicative Functor
- 42. Revisiting our first approach • Using Monad Transformers to compose Monads.
- 43. Revisiting our first approach • Controlling the execution. • Using a different Monad: Monix Task, ScalaZ IO... 実行モデルを制御
- 44. Revisiting our first approach • Swapping Future with Task. Future をタスクに入れ替え
- 45. Adding a level of abstraction 抽象化層を追加
- 46. Running it with Future/Task Future/Task それぞれで実
- 48. Wait a moment...
- 49. We can use Applicative!! Applicative も使えます
- 50. And run for Future, Task... Future とタスクそれぞれで走らせ る
- 52. Summary • Error Handling • Testability • Readability (For-comprehensions) • Reusability • Monad / Applicative
- 53. All the work is done now *Disclaimer: No Cats or Trumps were harmed during the making of this presentation
- 54. ありがとうございます ! Thank you very much!