Using Functional Programming to improve your code: A working example

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

Background Skills Time Acceptable Mainframe Coursera Habla Computing

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

Our company’s architecture 弊社のアーキテクチャ

Our company’s architecture 自然言語解析とかも Spark 経由で

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

Defining our data model tweet に対応するデータモデ

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

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

All the work is done now… これで仕事は終わ

Testing it • Deploy to some development environment and test. 開発環境にデプロイしてテスト

Testing it • Deploy to some development environment and test. • Refactor the code. リファクタリン

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

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、

Refactoring the Avro serialization Avro のシリアライズのリファクタリング

Separating the logic ビジネスロジックの分離

Enter for-comprehensions for- 内包表記登場

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。

Keep testing! • What about integration tests? 結合テストは？

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？

What happens if... • The framework changes? • Akka Streams Akka Streams の場合

Akka Streams • Processing Stages • Source, Flow, Sink

Akka Streams Source Sink Flow Output port Output port

Akka Streams Source Sink Flow Output port Output port Input port Input port

Running Asynchronously • Using Scala Futures 非同期に実行する場合

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。

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

Revisiting our first approach Monad モナド

Revisiting our first approach Monad Applicative Functor アプリカティブ・ファンクター

Revisiting our first approach Monad Applicative Functor

Revisiting our first approach • Using Monad Transformers to compose Monads.

Revisiting our first approach • Controlling the execution. • Using a different Monad: Monix Task, ScalaZ IO... 実行モデルを制御

Revisiting our first approach • Swapping Future with Task. Future をタスクに入れ替え

Adding a level of abstraction 抽象化層を追加

Running it with Future/Task Future/Task それぞれで実

And testing it それをテストしてみる

We can use Applicative!! Applicative も使えます

And run for Future, Task... Future とタスクそれぞれで走らせる

And test it! それもテスト

Summary • Error Handling • Testability • Readability (For-comprehensions) • Reusability • Monad / Applicative

All the work is done now *Disclaimer: No Cats or Trumps were harmed during the making of this presentation

ありがとうございます ! Thank you very much!

Using Functional Programming to improve your code: A working example

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