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.github/workflows/build.yml

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build:
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runs-on: ubuntu-20.04
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Ruby
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uses: ruby/setup-ruby@v1
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with:
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ruby-version: 3.0.5
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bundler-cache: true
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- name: Set up coursier
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uses: coursier/setup-action@v1.2.0-M2
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uses: coursier/setup-action@v1.3.5
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with:
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jvm: adopt:11
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- name: Run mdoc

_it/tutorials/scala-for-java-programmers.md

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danno l’accesso rispettivamente alla parte reale e a quella immaginaria
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del numero complesso.
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Da notare che il tipo di ritorno dei due metodi non è specificato esplicitamante.
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Da notare che il tipo di ritorno dei due metodi non è specificato esplicitamente.
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Sarà il compilatore che lo dedurrà automaticamente osservando la parte a destra
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del segno uguale dei metodi e deducendo che per entrambi si tratta di
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valori di tipo `Double`.
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}
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Questa funzione di valutazione lavora effettuando un *pattern matching*
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sull’albero `t`. Intuitivamente il significato della definizione precendente
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sull’albero `t`. Intuitivamente il significato della definizione precedente
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dovrebbe esser chiaro:
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1. prima controlla se l’albero `t` è un `Sum`; se lo è, esegue il bind del
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}
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Questa definizione crea un nuovo tipo chiamato `Ord` che ha lo stesso
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ruolo dell’interfaccia `Comparable` in Java e, fornisce l’implementazione
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ruolo dell’interfaccia `Comparable` in Java e fornisce l’implementazione
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di default di tre predicati in termini del quarto astraendone uno.
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I predicati di uguaglianza e disuguaglianza non sono presenti in questa
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dichiarazione poichè sono presenti di default in tutti gli oggetti.
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I programmatori Java hanno fatto ricorso all’uso di `Object`, che è il
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super-tipo di tutti gli oggetti. Questa soluzione è in ogni caso ben lontana
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dall’esser ideale perché non funziona per i tipi base (`int`, `long`, `float`,
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ecc.) ed implica che molto type casts dinamico deve esser fatto dal
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ecc.) ed implica che molto type cast dinamico deve esser fatto dal
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programmatore.
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Scala rende possibile la definizione delle classi generiche (e metodi) per

_overviews/parallel-collections/concrete-parallel-collections.md

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created in a similar way as the sequential
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[Range](https://www.scala-lang.org/api/{{ site.scala-212-version }}/scala/collection/immutable/Range.html):
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scala> 1 to 3 par
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scala> (1 to 3).par
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res0: scala.collection.parallel.immutable.ParRange = ParRange(1, 2, 3)
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scala> 15 to 5 by -2 par
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scala> (15 to 5 by -2).par
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res1: scala.collection.parallel.immutable.ParRange = ParRange(15, 13, 11, 9, 7, 5)
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Just as sequential ranges have no builders, parallel ranges have no
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scala> val phs = scala.collection.parallel.immutable.ParHashSet(1 until 1000: _*)
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phs: scala.collection.parallel.immutable.ParHashSet[Int] = ParSet(645, 892, 69, 809, 629, 365, 138, 760, 101, 479,...
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scala> phs map { x => x * x } sum
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scala> phs.map(x => x * x).sum
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res0: Int = 332833500
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Similar to parallel hash tables, parallel hash trie

_overviews/scala-book/sbt-scalatest-bdd.md

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For more information about sbt and ScalaTest, see the following resources:
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- [The main sbt documentation](http://www.scala-sbt.org/documentation.html)
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- [The ScalaTest documentation](http://www.scalatest.org/user_guide)
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- [The main sbt documentation](https://www.scala-sbt.org/1.x/docs/)
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- [The ScalaTest documentation](https://www.scalatest.org/user_guide)

_overviews/scala3-book/scala-features.md

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As you’ll see in the third section of this page, Scala libraries and frameworks like these have been written to power busy websites and work with huge datasets:
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1. The [Play Framework](https://www.playframework.com) is a lightweight, stateless, developer-friendly, web-friendly architecture for creating highly-scalable applications
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2. [Lagom](https://www.lagomframework.com) is a microservices framework that helps you decompose your legacy monolith and build, test, and deploy entire systems of reactive microservices
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3. [Apache Spark](https://spark.apache.org) is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing
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2. [Apache Spark](https://spark.apache.org) is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing
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The [Awesome Scala list](https://github.com/lauris/awesome-scala) shows dozens of additional open source tools that developers have created to build Scala applications.
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- [Finatra](https://twitter.github.io/finatra) is Scala services built on TwitterServer and Finagle
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- [Scala.js](https://www.scala-js.org) is a strongly-typed replacement for JavaScript that provides a safer way to build robust front-end web applications
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- [ScalaJs-React](https://github.com/japgolly/scalajs-react) lifts Facebook’s React library into Scala.js, and endeavours to make it as type-safe and Scala-friendly as possible
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- [Lagom](https://www.lagomframework.com) is a microservices framework that helps you decompose your legacy monolith and build, test, and deploy entire systems of Reactive microservices
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HTTP(S) libraries:
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_overviews/scala3-book/scala-for-java-devs.md

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<br>
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<br>// mix in the traits as DavidBanner
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<br>// is created
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<br>val hulk = new DavidBanner with Big,
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<br>&nbsp; Angry, Green</code>
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<br>val hulk = new DavidBanner with Big with Angry with Green
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</code>
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</td>
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</tr>
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</tbody>

_overviews/scala3-book/string-interpolation.md

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title: String Interpolation
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type: chapter
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description: This page provides more information about creating strings and using string interpolation.
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languages: [ru]
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languages: [ru, zh-cn]
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num: 18
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previous-page: first-look-at-types
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next-page: control-structures

_overviews/scala3-book/types-others.md

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- Refinement types
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- Kind polymorphism
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For more details on these types, see the [Scala 3 Reference documentation][reference].
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For more details on most of these types, refer to the [Scala 3 Reference documentation][reference].
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For singleton types see the [literal types](https://scala-lang.org/files/archive/spec/3.4/03-types.html#literal-types) section of the Scala 3 spec,
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and for refinement types, see the [refined types](https://scala-lang.org/files/archive/spec/3.4/03-types.html) section.
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_overviews/scala3-migration/options-lookup.md

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| 2.13.x | 3.3.x |
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|-|-|
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| `-Xasync` | <i class="fa fa-times fa-lg"></i> |
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| `-Xcheckinit` | `-Ycheck-init` |
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| `-Xcheckinit` | `-Ysafe-init` |
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| `-Xdev` | <i class="fa fa-times fa-lg"></i> |
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| `-Xdisable-assertions` | <i class="fa fa-times fa-lg"></i> |
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| `-Xelide-below` | <i class="fa fa-times fa-lg"></i> |

_overviews/tutorials/scala-on-android.md

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* [Log Viewer for Android Studio and IntelliJ](https://plugins.jetbrains.com/plugin/10015-log-viewer)
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* [Logcat plugin for VSCode](https://marketplace.visualstudio.com/items?itemName=abhiagr.logcat)
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Here's a [screenshot](https://github.com/makingthematrix/scalaonandroid/blob/main/helloscala/helloscala.png) of what the app looks like when you open it.
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And [from here](https://drive.google.com/file/d/1SV5waXjyEYSSlo-mfj28WF34mcMFxoG7/view?usp=sharing) you can download the already compiled APK.
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Here's a [screenshot](https://github.com/makingthematrix/scalaonandroid/blob/main/helloscala/helloscala.png) of what the app looks like when you open it.
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## Next Steps and Other Useful Reading
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_ru/scala3/book/scala-features.md

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были написаны для поддержки загруженных веб-сайтов и работы с огромными наборами данных:
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1. [Play Framework](https://www.playframework.com) — это легкая, без сохранения состояния, удобная для web, удобная для разработчиков архитектура для создания масштабируемых приложений
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2. [Lagom](https://www.lagomframework.com) — это инфраструктура микросервисов, которая помогает разделить устаревший монолит и создавать, тестировать и развертывать целые системы реактивных микросервисов
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3. [Apache Spark](https://spark.apache.org) — это унифицированный аналитический механизм для обработки больших данных со встроенными модулями для потоковой передачи, SQL, машинного обучения и обработки графиков
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2. [Apache Spark](https://spark.apache.org) — это унифицированный аналитический механизм для обработки больших данных со встроенными модулями для потоковой передачи, SQL, машинного обучения и обработки графиков
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В [списке Awesome Scala](https://github.com/lauris/awesome-scala) представлены десятки дополнительных инструментов
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с открытым исходным кодом, созданных разработчиками для создания приложений Scala.
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- [Finatra](https://twitter.github.io/finatra) — это сервисы Scala, построенные на TwitterServer и Finagle
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- [Scala.js](https://www.scala-js.org) — это строго типизированная замена JavaScript, обеспечивающая более безопасный способ создания надежных интерфейсных web-приложений
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- [ScalaJs-React](https://github.com/japgolly/scalajs-react) поднимает библиотеку Facebook React на Scala.js и пытается сделать ее максимально безопасной для типов и удобной для Scala
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- [Lagom](https://www.lagomframework.com) — это инфраструктура микросервисов, которая помогает разделить устаревший монолит и создавать, тестировать и развертывать целые системы реактивных микросервисов
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HTTP(S) библиотеки:

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