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+ +
+
+
+
+
+ + + + + + +
+

+ Коварный enum в .NET +

+

+ + #dotnet + + #csharp + + #wcf + +
+ +

Перечисления - это такой тип данных, который выглядит очень просто и его легко использовать. Вот только за этой простотой скрывается коварность. Почему? Потому что enum - это ValueType, а ValueType в C#, как известно, не наследуется. Необдуманное использование типов, которые нарушают принцип Open / Closed, может создать проблемы в вашем проекте в будущем.

+ +

Предположим, вы - разработчик, которому нужно сделать клиент-серверное приложение. Клиент должен отправлять данные о продажах на сервер. В теле передаваемого объекта должна содержатся цена товара и валюта за которую товар был продан. Как это можно реализовать? Например, через enum:

+ +
public enum CurrenciesEnum
+{
+    EUR,
+    USD
+}
+
+ +

Класс передаваемого объекта тогда будет выглядеть следующим образом:

+ +
public class SaleDetails
+{
+    public double Price { get; set; }
+
+    public CurrenciesEnum Currency { get; set; }
+}
+
+ +

Очень просто и элегантно, правда? А теперь представьте, что нужно добавить в CurrenciesEnum ещё одну валюту - фунт стерлингов. Как это можно сделать? Есть только один способ - изменить само перечисление:

+ +
public enum CurrenciesEnum
+{
+    EUR,
+    USD,
+    GBP
+}
+
+ +

Ничего сложного, но возможность вносить изменения в существующие типы есть далеко не всегда. Например, что, если перечисление находится в неком базовом проекте, у которого есть несколько кастомизаций под различных заказчиков? Изменение перечисления в базовом проекте может привести к так называемому shotgun surgery. Или что, если при этом свойство типа enum является членом контракта WCF? С таким случаем я столкнулся на одном из рабочих проектов. Это и побудило меня написать статью.

+ +

Enum курильщика

+ +

Я сделал упрощенный вариант приложения, которое передаёт суть проблемы. Рассмотрим код подробнее. Начнём с перечисления CurrenciesEnum и класса SaleDetails. Тут всё по старому, за исключением того, что добавились атрибуты. Они нам необходимы для работы WCF.

+ +
[DataContract]
+public enum CurrenciesEnum
+{
+    [EnumMember]
+    EUR,
+    [EnumMember]
+    USD
+}
+
+ +
[DataContract]
+public class SaleDetails
+{
+    [DataMember]
+    public double Price { get; set; }
+
+    [DataMember]
+    public virtual CurrenciesEnum Currency { get; set; }
+}
+
+ +

Перечисление CurrenciesEnum и класс SaleDetails я разместил в проекте Shared. Предположим, что мы не можем вносить изменения в проект напрямую, т.к. он используется во многих других приложениях, поэтому мы вынуждены использовать наследование.

+ +

project-tree

+ +

Проект Server - это WCF-служба. Она состоит из запроса и его колбэка. В этом проекте имитируется получение данных от клиента и преобразование в XML. Чтобы проверить корректность преобразования данных, будем отправлять строку с XML обратно клиенту.

+ +
public class Request : IRequest
+{
+    public void SendRequest(SaleDetails saleDetails)
+    {
+        var response = OperationContext.Current.GetCallbackChannel<IResponse>();
+
+        var responseString =
+            $"<SaleDetails>\n" +
+            $"\t<Price>{saleDetails.Price:N2}</Price>\n" +
+            $"\t<Currency>{saleDetails.Currency}</Currency>\n" +
+            $"</SaleDetails>";
+
+        response.SendResponse(responseString);
+    }
+
+}
+
+ +

Проект Client - это консольное приложение. В нём находится реализация колбэка и запрос к серверу. Полученный колбэк будет выведен в консоль.

+ +
class Callback : IRequestCallback
+{
+    public void SendResponse(string response) => Console.WriteLine(response);
+}
+
+ +
internal class Program
+{
+    static void Main(string[] args)
+    {
+        var server = new RequestClient(new InstanceContext(new Callback()));
+        server.Open();
+        try
+        {
+            server.SendRequest(new SaleDetails()
+            {
+                Price = 100,
+                Currency = CurrenciesEnum.USD
+            });
+            server.SendRequest(new SaleDetails()
+            {
+                Price = 100,
+                Currency = CurrenciesEnum.EUR
+            });
+        }
+        catch (Exception ex)
+        {
+            Console.WriteLine(ex.Message);
+        }
+        finally
+        {
+            server.Close();
+        }
+
+        Console.ReadLine();
+    }
+}
+
+ +

Результат работы вполне ожидаем. Клиент получил от сервера две XML-строки:

+ +

first-response

+ +

Теперь попробуем кастомизировать SaleDetails в клиенте и добавить новый тип валюты, например вот так:

+ +
[DataContract(Name = "CurrenciesEnum")]
+enum CustomCurrenciesEnum
+{
+    [EnumMember]
+    EUR = CurrenciesEnum.EUR,
+    [EnumMember]
+    USD = CurrenciesEnum.USD,
+    [EnumMember]
+    GBP = 2
+}
+
+ +
[DataContract]
+class CustomSaleDetails : SaleDetails
+{
+    [DataMember]
+    public new CustomCurrenciesEnum Currency { get; set; }
+}
+
+ +

И отправим 2 запроса: один с экземпляторм SaleDetails, а второй с CustomSaleDetails:

+ +
server.SendRequest(new SaleDetails()
+{
+    Price = 100,
+    Currency = CurrenciesEnum.USD
+});
+server.SendRequest(new CustomSaleDetails()
+{
+    Price = 100,
+    Currency = CustomCurrenciesEnum.GBP
+});
+
+ +

Конечно это не сработает. И хоть приложение скомпилируется без ошибок, но при выполнении, сервер вернет ответ только на первый запрос. На второй запрос ответа не последует и наш клиент выбросит исключение из-за таймаута.

+ +

Тот же результат будет, если попробовать явно преобразовать значение CustomCurrenciesEnum.GBP к типу CurrenciesEnum

+ +
server.SendRequest(new SaleDetails()
+{
+    Price = 100,
+    Currency = (CurrenciesEnum) CustomCurrenciesEnum.GBP
+});
+
+ +

Причина ошибки проста - контракты данных клиента и сервера не эквивалентны. Как решить такую проблему, если мы не можем изменить контракт в проекте Shared? В случае с рабочим проектом пришлось делать костыль. В перечислении было много неиспользуемых значений, одно из которых я условно принял за необходимое (т.е. если бы значение USD не использовалось, то я бы условно принял, что это GBP и на стороне клиента сам преобразовывал USD в строку GBP). Если вы столкнулись с такой проблемой и у вас нет неиспользуемых значений перечисления, то у вас проблемы. Этого можно было бы избежать, если бы использовались enum classes.

+ +

Enum здорового человека

+ +

Вместо классического enum можно использовать enum classes. Да, этой теме уже сто лет в обед, но судя по всему не все о ней знают. За основу я взял реализацию из статьи MSDN и оставил только то, что необходимо для демонстрации.

+ +
[DataContract]
+public abstract class Enumeration
+{
+    [DataMember]
+    public int Value { get; set; }
+
+    [DataMember]
+    public string DisplayName { get; set; }
+
+    public override string ToString() => DisplayName;
+}
+
+ +

Перепишем наше перечисление с использованием нового класса

+ +
[DataContract]
+public class CurrenciesClass : Enumeration
+{
+    [DataMember]
+    public static readonly CurrenciesClass EUR = new CurrenciesClass
+    {
+        Value = 0,
+        DisplayName = "EUR"
+    };
+
+    [DataMember]
+    public static readonly CurrenciesClass USD = new CurrenciesClass
+    {
+        Value = 1,
+        DisplayName = "USD"
+    };
+}
+
+ +

и примением его в классе SaleDetails.

+ +
[DataContract]
+public class SaleDetails
+{
+    [DataMember]
+    public double Price { get; set; }
+
+    [DataMember]
+    public CurrenciesClass Currency { get; set; }
+}
+
+ +

Обновим запросы в клиенте, запустим и посмотрим что получилось.

+ +
internal class Program
+{
+    static void Main(string[] args)
+    {
+        var server = new RequestClient(new InstanceContext(new Callback()   ));
+        server.Open();
+        try
+        {
+            server.SendRequest(new SaleDetails()
+            {
+                Price = 100,
+                Currency = CurrenciesClass.EUR
+            });
+            server.SendRequest(new SaleDetails()
+            {
+                Price = 200,
+                Currency = CurrenciesClass.USD
+            });
+        }
+        catch (Exception ex)
+        {
+            Console.WriteLine(ex.Message);
+        }
+        finally
+        {
+            server.Close();
+        }
+
+        Console.ReadLine();
+    }
+}
+
+ +

second-response

+ +

Как мы видим, всё работает отлично. Всё отлично и с возможностью расширения функционала класса CurrenciesClass? Создаём класс наследник в клиенте и добавляем новое статическое поле:

+ +
[DataContract]
+class CustomCurrenciesClass : CurrenciesClass
+{
+    [DataMember]
+    public static readonly CurrenciesClass GBR = new CurrenciesClass()
+    {
+        Value = 2,
+        DisplayName = "GBR"
+    };
+}
+
+ +

добавляем в тело метода Main ещё один запрос

+ +
server.SendRequest(new SaleDetails()
+{
+    Price = 300,
+    Currency = CustomCurrenciesClass.GBR
+});
+
+ +

Вуаля, всё работает

+ +

third-response

+ +

Резюме

+ +

Как мы видим, использование enum classes значительно упрощает кастомизацию уже существующих перечислений. Да, придётся писать чуть больше кода, но преимущества такого подхода очевидны. Конечно, не нужно использовать enum classes везде вместо классического enum. Если вы на 100% уверены, что ваше перечисление не будет меняться в будущем или если вы можете вносить изменения в коде на любом слое вашего приложения, то можно использовать enum. Просто в некоторых случаях, как например, описанный мною, использование enum classes - это единственное решение, которое бы не доставило вам проблем в будущем.

+
+
+ + + +
+
+ + diff --git a/2022/05/01/fsharp-01.html b/2022/05/01/fsharp-01.html new file mode 100644 index 0000000..c8b1be6 --- /dev/null +++ b/2022/05/01/fsharp-01.html @@ -0,0 +1,300 @@ + + + + + + + +Функциональное программирование на F# (часть 1) | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Функциональное программирование на F# (часть 1) +

+

+ + #dotnet + + #fsharp + + #csharp + + #functional_programming + +
+ +

Недавно прочитал книгу 1 “Unit Testing Principles, Practices, and Patterns” Владимира Хорикова. Книга классная, фундаментальная. В плане полезности для .NET backend разработчиков, по-моему, стоит на уровне CLR via C#, Dependency Injection in .NET, Patterns of Enterprise Application Architecture и т.д. Помимо вопросов тестирования, она также вкратце объясняет вопросы функциональной архитектуры и то, как такая архитектура положительно влияет на возможность тестирования кода. Ранее мне не приходилось сталкиваться с функциональными языками программирования, но после прочтения книги очень захотелось поработать с ними. Поскольку я C# разработчик, то проще всего было начать изучать функциональное программирование с другой разработкой Microsoft - языка F#. В этой статье мы попробуем реализовать один и тот же алгоритм в функциональном стиле на C# и F# и сравнить результат.

+ +

Объектно-ориентированное программирование

+ +

Предположим, нам нужно реализовать функционал, который будет вычислять n первых чисел ряда Фибоначчи и выводить значения в консоль. Такую задачу на C# можно было бы решить вот так 2:

+ +
public class FibonacciWriter
+{
+    public void Write(uint n)
+    {
+        uint prev = 0;
+        uint next = 1;
+
+        Console.WriteLine(prev);
+
+        uint i = 1;
+
+        while(i++ < n)
+        {
+            var next2 = prev + next;
+            prev = next;
+            next = next2;
+
+            Console.WriteLine(prev);
+        }
+    }
+}
+
+ +

Какие проблемы могут возникнуть у такой реализации? Например, если потребуется изменить вывод консоль на вывод в файл, то мы не можем этого сделать. Это явное нарушение принципа Open/Closed. Чтобы избежать таких проблем, можно добавить абстрактный метод Output:

+ +
public abstract class FibonacciWriter
+{
+    public void Write(uint n)
+    {
+        uint prev = 0;
+        uint next = 1;
+
+        Output(prev);
+
+        uint i = 1;
+
+        while(i++ < n)
+        {
+            var next2 = prev + next;
+            prev = next;
+            next = next2;
+
+            Output(prev);
+        }
+    }
+
+    protected abstract void Output(uint i);
+}
+
+public class FibonacciConsoleWriter : FibonacciWriter
+{
+    protected override void Output(uint i) =>
+        Console.Write($"{i} ");
+}
+
+ +

В целом, такая реализация более менее нормальная и часто встречается на практике, но она нарушает принцип Single Responsibility. К тому же, для такого класса трудно написать unit-тест, т.к. метод Write ничего не возвращает и для проверки результата придётся переопределять метод Output. Очевидно, что для решения этой проблемы нам нужно разделить расчёт ряда Фибоначчи и вывод значений. Для этого к нам на помощь придёт функциональный подход.

+ +

Функциональное программирование

+ +

Функциональное программирование основано на использовании чистых функций. Согласно Википедии, чистая функция - это функция, которая:

+ +
    +
  1. Возвращает один и тот же результат для одних и тех же аргументов.
    2. +
  2. Не имеет побочных эффектов (не выполняет изменение объектов, изменение файлов и т.д.)
    3. +
+ +

Вернёмся к классу FibonacciWriter. Является ли метод Write - чистой функцией? Конечно же нет. Во-первых, мы не можем сопоставить результат выполнения метода с входными аргументами (метод вообще ничего не возвращает). Во-вторых, метод имеет побочные эффекты в виде изменения переменных и вывода в консоль.

+ +

Давайте перепишем класс FibonacciWriter в функциональном стиле:

+ +
public class Fibonacci
+{
+    public IEnumerable<uint> Get(uint n) => Next(0, 1).Take(10);
+
+    private IEnumerable<uint> Next(uint previous, uint current)
+    {
+        yield return previous;
+
+        foreach (var item in Next(current, previous + current)) 
+        {
+            yield return item;
+        }
+    }
+}
+
+ +

Основные отличия функциональной реализации:

+ +
    +
  • Метод Get возвращает коллекцию типа IEnumerable<uint>. Это значительно упрощает тестирование.
    • +
  • Данные в классе иммутабельны. Если нет изменяющихся данных, то не требуется синхронизация потоков (для данного примера не актуально, но всё же).
    • +
  • Нет side-эффектов. При написании тестов не понадобится создавать mock-объект и проверять вызов метода Output.
    • +
  • Появляется возможность независимо друг от друга развивать класс Fibonacci и класс, отвечающий за вывод данных.
    • +
+ +

Получается, что на C# тоже можно писать в функциональном стиле. Зачем тогда использовать F#? Я вижу несколько причин:

+ +
    +
  • Данные в F# иммутабельны по-умолчанию. Язык не позволяет просто так поменять значение переменной (строго говоря, это и не переменные вовсе). Такой подход заставляет разработчика писать не так, как в классических ООП языках.
    • +
  • Синтаксис F# больше заточен под функциональное программирование (спасибо, кэп).
    • +
+ +

Перепишем класс Fibonacci на F#:

+ +
module Fibonacci =
+
+    let next (previous, current) = 
+        Some(previous, (current, previous + current))
+
+    let get n = Seq.unfold (next) (0, 1) |> Seq.take n
+
+ +

Как мы видим, код довольно сильно отличается от C#. Что тут изменилось:

+ +
    +
  • Написано меньше кода, нет фигурных скобок. Для разделения блоков кода используются отступы, как на Python.
    • +
  • Для генерации значений ряда Фибоначчи используется Seq.unfold. По-сути, эта конструкция является эквивалентом ключевого слова yield в C#. При помощи Seq.unfold можно создавать коллекции с неизвестным конечным размером.
    • +
  • Оператор |> или pipe-оператор. Предназначен для создания цепочки вызовов.
    • +
  • Нет объявлений типов. F# очень хорошо умеет выводить типы значений и функций.
    • +
+ +

C# + F#

+ +

Прелесть всех языков .NET в том, что код написанный на F#, можно вызывать в программе на C#. Поэтому, можно спокойно написать вот такой код:

+ +
Console.Write("F# Fibonacci: ");
+foreach (var item in FSharp.Fibonacci.get(10))
+{
+    Console.Write($"{item} ");
+}
+Console.WriteLine();
+
+ +

На C#, кстати, модуль Fibonacci будет в виде статического класса, а функции get и next станут статическими (что там происходит под капотом разберём в следующей статье):

+ +
public static class Fibonacci
+{
+    public static FSharpOption<Tuple<int, Tuple<int, int>>> next(int previous, int current)
+    {
+        // next implementation
+    }
+
+    public static IEnumerable<int> get(int n)
+    {
+        // get implementation
+    }
+}
+
+ +

Такой подход (использование C# в связке с F#), на мой взгляд, позволяет в приложениях эффективно разделить Business Logic Layer от Data Access Layer или Service Layer. На F# можно написать функциональное ядро для Business Logic, а всё остальное - на C#.

+ +

Резюме

+ +

Использование функционального подхода в программировании позволяет писать чистую архитектуру приложения, которая отвечает принципам SOLID. Для таких приложений проще писать тесты и их проще сопровождать. Поскольку для F# функциональное программирование является основной парадигмой, то писать в функциональный код на нём намного проще.

+ +

Ссылки

+ +
+
    +
  1. +

    “Unit Testing Principles, Practices, and Patterns” ↩

    +
    2. +
  2. +

    “Исходный код” ↩

    +
    3. +
+
+
+
+ + + +
+
+ + diff --git a/2022/05/08/fsharp-02.html b/2022/05/08/fsharp-02.html new file mode 100644 index 0000000..5bc05c0 --- /dev/null +++ b/2022/05/08/fsharp-02.html @@ -0,0 +1,359 @@ + + + + + + + +Функциональное программирование на F# (часть 2) | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Функциональное программирование на F# (часть 2) +

+

+ + #dotnet + + #fsharp + + #csharp + + #functional_programming + +
+ +

В прошлой статье мы написали модуль для расчёта ряда Фибоначчи практически не углубляясь в детали того, что из себя представляет F# и чем он отличается от кода на C#. В этой статье мы рассмотрим основные принципы функционального программирования, некоторые базовые языковые конструкции F# и исследуем скомпилированный код.

+ +

Определение значений (Simple values)

+ +

Начнём с самого простого - с определения значений простых типов1.

+ +
let hello = "Hello, world!"
+let mutable year = 2022
+
+ +

Как видно ниже, значение hello компилируется в статическое свойство с доступом только для чтения, т.е. в функцию, которая не принимает никаких аргументов и всегда возвращает одно и то же значение. Такое поведение является дефолтным для F# и таким образом достигается иммутабельность данных. Если же мы захотим изменять данные, то нужно добавить ключевое слово mutable. Тогда значение скомпилируется в свойство в котором будут и геттер и сеттер.

+ +
public static string hello => "Hello, world!";
+
+public static int year { get; set; }
+
+ +
+

Код на C#, эквивалентный коду на F#, из-за автоматической генерации кода декомпилятора занимает довольно много места. Здесь и далее я опустил некоторые несущественные детали, которые не влияют на смысл. Если интересна полная картина, то можно воспользоваться dotPeek 2 или SharpLab 3.

+
+ +

Определение функций (Function values)

+ +

Теперь сделаем из hello функцию, которая принимает один параметр и добавляет его в строку, но реализуем это 5 разными способами.

+ +
let hello1 x = $"Hello " + x + "!" 
+let hello2 x = $"Hello {x}!"
+let hello3 (x: string) = $"Hello {x}!" // explicit type 'string' for an argument 'x'
+let hello4 (x: int) = $"Hello {x}!" // explicit type 'int' for an argument 'x'
+let hello5 x : string = $"Hello {x}!" // explicit output type
+
+ +

Как мы видим, функции определяются точно также, как и значения - через ключевое слово let. Вообще, это одна из главных особенностей функциональных языков программирования - функции являются объектами первого класса. 4 Это значит, что функции сами являются значениями, т.е. могут быть входными аргументами или возвращаемыми параметрами.

+ +

Скомпилированный код hello1 - простой, куда интереснее код с интерполированными строками:

+ +
public static string hello1(string x) => string.Concat("Hello ", x, "!");
+
+// hello2 and hello5 have the same method body
+public static string hello2<a>(a x)
+{
+    object[] array = new object[1];
+    array[0] = x;
+    return PrintfModule.PrintFormatToStringThen(
+        new PrintfFormat<string, Unit, string, string, a>(
+            "Hello %P()!", array, null));
+}
+
+public static string hello3(string x)
+{
+    object[] array = new object[1];
+    array[0] = x;
+    return PrintfModule.PrintFormatToStringThen(
+        new PrintfFormat<string, Unit, string, string, string>(
+            "Hello %P()!", array, null));
+}
+
+public static string hello4(int x)
+{
+    object[] array = new object[1];
+    array[0] = x;
+    return PrintfModule.PrintFormatToStringThen(
+        new PrintfFormat<string, Unit, string, string, int>(
+            "Hello %P()!", array, null));
+}
+
+ +

Рассмотрим, что тут происходит:

+ +
    +
  • Функция hello2 компилируется в обобщённый метод, т.к. мы не указали явно тип входного параметра.
    • +
  • Методы hello2 и hello5 по-сути одинаковы (поэтому я их объединил), т.к. F# сам вывел тип для hello2.
    • +
  • Для методов hello3 и hello4 явно указан тип x, поэтому метод не обобщённый.
    • +
  • Тела методов hello2, hello3 и hello4 выглядят довольно объёмно. Если, например hello4, написать на C#, то скомпилированный код займёт меньше места:
    • +
+ +
public string hello3(int x) => string.Format("Hello, {0}!", x);
+
+ +

Типы и каррирование функций (Carrying)

+ +

Способов определения одних и тех же функций может быть множество. Например, рассмотрим вот эти две функции:

+ +
let sum1 x y = x + y 
+let sum2 x = fun y -> x + y
+
+ +

Если попытаться явно переписать их на C#, то получилось бы примерно следующее:

+ +
// Invocation: int result = sum1(1, 2);
+public int sum1(int x, int y) => x + y;
+
+// Invocation: int result = sum2(1)(2);
+public Func<int, int> sum2(int x) => (y) =>
+{
+    return x + y;
+};
+
+ +

Для F# функции sum1 и sum2 эквивалентны, но c точки зрения C# - нет, несмотря на то, что результат вызова в обоих случаях будет иметь тип int. Чтобы понять почему так, нужно разобраться, что значит тип в функциональных языках. В объектно-ориентированных языках программирования, когда мы говорим о типах, то мы подразумеваем классы или структуры. В функциональных языках же под типом понимается набор входных и выходных значений.

+ +

Сперва может показаться, что на F#, как и на C#, набор входных значений отличается - sum1 принимает два аргумента, а sum2 только один. На самом деле всё не совсем так. В F# одна функция, которая принимает множество параметров, преобразуются в множество функций, которые принимают по одному параметру. Таким образом, функция вида (a, b) -> c, превращается в a -> b -> c. Такой метод преобразования называется каррированием. 5

+ +

Благодаря каррированию, функции sum1 и sum2 можно описать одним типом:

+ +
type operation = int -> int -> int
+
+ +

Эта странная запись читается слева направо и значит, что функция принимает на вход значение типа int и возвращает функцию, которая, в свою очередь, тоже принимает на вход значение int и возвращает значение int.

+ +

Функции как входные и выходные параметры

+ +

Как упоминалось ранее, функции в F# - это объекты первого класса. Это значит, что можно записать вот так:

+ +
let operationAsInput (op: operation) x y = op x y
+let operationAsOutput : operation = fun x -> fun y -> x * y
+
+let result = operationAsInput operationAsOutput 10 5 // result = 50
+
+ +

Рассмотрим подробнее, что тут происходит:

+ +
    +
  1. Функция operationAsInput принимает на вход функцию op типа operation и значения x и y, а возвращает результат выполнения этой функции для x и y.
    2. +
  2. Функция operationAsOutput возвращает другую функцию типа operation. В данном случае умножение двух чисел.
    3. +
  3. Осуществляется вызов operationAsInput, в которую передаётся функция operationAsOutput и значения 10 и 5.
    4. +
+ +

Возможность использования функций как параметров позволяет осуществлять таким образом композицию, внедрение зависимостей, реализовывать паттерны проектирования. Об этом поговорим в следующей статье.

+ +

Кстати, при компиляции код будет оптимизирован, поэтому на C# наши декомпилированные функции выглядят немного по-другому:

+ +
public static int operationAsInput(
+    FSharpFunc<int, FSharpFunc<int, int>> op, int x, int y) => 
+        FSharpFunc<int, int>.InvokeFast(op, x, y);
+
+public static int operationAsOutput(int x, int y) => x * y;
+
+ +

Операторы как функции

+ +

Как мы знаем, операторы в языках .NET - это методы. Например, оператор + для типа int после компиляции в CIL выглядит примерно так:

+ +
.method public hidebysig specialname static int32 op_Addition (
+        int32 a,  int32 b) cil managed 
+{
+    // operator implementation
+}
+
+ +

С точки зрения функционального программирования op_Addition - это функция типа int -> int -> int, и это значит, что представленные ниже функции эквивалентны:

+ +
// int -> int -> int
+let sumInfix x y = x + y
+let sumPrefix x y = (+) x y
+
+// int -> int
+let incrementInfix x = x + 1
+let incrementPrefix = (+) 1
+
+ +

Если посмотреть декомпилированный код на C#, то первые три функции выглядят вполне обычно.

+ +
public static int sumInfix(int x, int y) => return x + y;
+
+public static int sumPrefix(int x, int y) => return x + y;
+
+public static int incrementInfix(int x) => return x + 1;
+
+ +

Чего нельзя сказать об incrementPrefix:

+ +
    +
  • Во-первых, incrementPrefix компилируется в свойство типа FSharpFunc<int, int>
    • +
  • Во-вторых, это свойство возвращает значение поля incrementPrefix@8 из дополнительного класса $_
    • +
  • В-третьих, полю $_.incrementPrefix@8 присваивается значение поля Functions.incrementPrefix@8.@_instance в конструкторе типа _$
    • +
+ +
public static class Functions
+{
+    internal sealed class incrementPrefix@8 : FSharpFunc<int, int>
+    {
+        internal static readonly incrementPrefix@8 @_instance = new incrementPrefix@8();
+        public override int Invoke(int y) => 1 + y;
+    }
+
+    public static FSharpFunc<int, int> incrementPrefix => $_.incrementPrefix@8;
+}
+
+internal static class $_
+{
+    internal static readonly FSharpFunc<int, int> incrementPrefix@8;
+
+    static $_()
+    {
+        incrementPrefix@8 = Functions.incrementPrefix@8.@_instance;
+    }
+}
+
+ +

Резюме

+ +

В этой статье рассмотрены ключевые принципы функциональных языков программирования:

+ +
    +
  • Функции - это объекты первого класса, т.е. могут быть входными аргументами или возвращаемыми параметрами.
    • +
  • Возможность использования функций как параметров позволяет осуществлять композицию.
    • +
  • Под типом понимаются не классы и структуры, а набор входных и выходных значений функций.
    • +
+ +

В следующей статье мы продолжим рассматривать синтаксис F#, а также рассмотрим как в F# осуществляется внедрение зависимостей и реализуем часто встречающиеся паттерны.

+ +

Ссылки и источники

+ +
+
    +
  1. +

    Исходный код ↩

    +
    2. +
  2. +

    JetBrains dotPeek ↩

    +
    3. +
  3. +

    SharpLab ↩

    +
    4. +
  4. +

    First-class function ↩

    +
    5. +
  5. +

    Currying ↩

    +
    6. +
+
+
+
+ + + +
+
+ + diff --git a/2022/07/14/how-to-create-blog.html b/2022/07/14/how-to-create-blog.html new file mode 100644 index 0000000..e45de68 --- /dev/null +++ b/2022/07/14/how-to-create-blog.html @@ -0,0 +1,472 @@ + + + + + + + +Как создать блог на GitHub | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Как создать блог на GitHub +

+

+ + #blogging + + #github + + #jekyll + + #markdown + + #html + +
+ +

Многие программисты имеют свой блог, в которых пишут свои заметки. Очень часто блог реализован на Jekyll, Hugo или Gatsby.js, а для хранения, деплоя и хостинга используется GitHub и GitLab. В этом гайде я расскажу, как реализован мой блог на Jekyll и GitHub.

+ +

Почему GitHub

+ +

Причин множество:

+ +
    +
  • GitHub Pages позволяет хостить статические сайты бесплатно.
    • +
  • GitHub Pages имеет встроенную поддержку Jekyll.
    • +
  • Фиксация изменений в постах производится привычными командами git commit и git push.
    • +
  • Хранение постов в удалённом репозитории.
    • +
  • GitHub Actions позволяет автоматически производить сборку и деплой проекта из репозитория.
    • +
+ +

На самом деле, под такую задачу подходит не только GitHub. Например, GitLab обладает такими же возможностями.

+ +

Требования

+ +

Для создания блога понадобится следующее:

+ +
    +
  • Ruby - сборка и запуск блога локально
    • +
  • Jekyll - конфигурирование блога
    • +
  • Markdown - создание постов с разметкой
    • +
  • HTML, CSS, JavaScript - редактирование макетов страниц, добавление скриптов
    • +
  • Git - фиксирование изменений
    • +
  • GitHub - хранение репозитория
    • +
  • GitHub Actions - сборка и деплой блога
    • +
  • GitHub Pages - хостинг блога
    • +
+ +

Не нужно бояться этого списка. Быть экспертом во всём, что тут перечислено, необязательно. Например, Ruby понадобится только для того, чтобы запускать блог локально на компьютере. Теоретически, можно обойтись и без установки Ruby с Jekyll, но в таком случае, чтобы посмотреть на изменения, придётся каждый раз пушить изменения и ждать, пока блог соберётся и задеплоится.

+ +

Ruby и Jekyll

+ +

Установка Ruby

+ +

Jekyll - это статический генератор сайтов. Вкратце, работает он так: создаётся пост с использованием разметки Markdown, а движок Jekyll преобразует их в статические HTML страницы. Поскольку Jekyll написан на Ruby, то сперва необходимо установить Ruby.1 В установке для Windows (сорян линуксоиды) нет ничего сложного - классическое “Далее, далее, далее, установить”.

+ +

Установка Jekyll

+ +

После установки Ruby устанавливается Jekyll. Делается это через пакетный менеджер для Ruby:

+ +
gem install jekyll bundler
+
+ +

Создание проекта

+ +

Создать проект блога можно из шаблона:

+ +
jekyll new myblog
+
+ +

После выполнения команды, должна появится папка проекта блога с названием myblog:

+ +

first-response

+ +

Структура проекта

+ +

Посмотрим подробнее на созданные файлы.

+ +

_config.yml

+ +

Конфигурация Jekyll. Параметры, которые задаются в этом файле, влияют на весь блог. Информация на сайте Jekyll 2 скудная, поэтому в качестве примера разберу конфигурацию моего блога: 3

+ +
# Задаёт название сайта, которое отображается в заголовке страницы браузера.
+title: alexeyfv.blog
+# Задаёт описание сайта, которое отображается в заголовке страницы браузера.
+description: Articles about .NET, C# and much more
+# Директория из которой будет загружаться блог. Можно оставить поле пустым.  
+baseurl: ""
+# Полный URL сайта
+url: "http://www.alexeyfv.xyz/"
+
+# Тема блога.
+theme: minima
+
+# Думаю, тут всё понятно.
+author:
+  name: © 2022 Alexey Fedorov
+
+# Параметры темы. Можно задать тёмный скин и добавить ссылки на свои социальные сети,
+# которые будут отображаться в футере блога.
+minima:
+  skin: dark
+  social_links:
+    github: alexeyfv
+    linkedin: alexeyfv
+    telegram: alexeyfv  
+
+# Страницы, которые будут отображаться в хедере блога.
+header_pages:
+  - _pages/tags.html
+  - _pages/projects.md
+#   - _pages/setup.md
+  - _pages/about.
+
+# Задаёт дополнительную папку, из которой будут загружаться страницы. 
+# По-умолчанию, страницы грузятся только из корневой папки.
+include: ['_pages']
+
+# Включает отображение первого абзаца поста на главной странице.
+show_excerpts: true
+
+ +

Gemfile и Gemfile.lock

+ +

Конфигурационные файлы для Ruby. В этих файлах указываются зависимости. Эти файлы редактировать не надо, т.к. Bundler (утилита для управления зависимостями) делает всё сам.

+ +

index.markdown, about.markdown, 404.html

+ +

Страницы блога. Тут можно написать всё что душе угодно, например создать кастомную заглушку для ошибки 404. Jekyll поддерживает как HTML страницы, так и страницы с разметкой Markdown. Для удобства, все страницы, кроме index.markdown можно вынести в отдельную папку.

+ +

При необходимости кастомизировать стили страниц или макеты, то можно поискать информацию в репозитории minima4.

+ +

Папка _posts

+ +

Папка для хранения постов. Посты, которые находятся в этой папке, будут отображаться на главной странице.

+ +

Запуск блога локально

+ +

Чтобы собрать и запустить блог локально необходимо выполнить команду:

+ +
bundle exec jekyll serve
+
+ +

Если будет появляться ошибка с текстом cannot load such file -- webrick (LoadError), то нужно добавить в Gemfile строку gem "webrick", "~> 1.7"

+ +

После запуска блог будет доступен по адресу http://localhost:4000/.

+ +

Настройка блога

+ +

Теперь можно приступить к созданию постов. Документация по созданию постов на сайте Jekyll5 достаточно подробная, поэтому не будем повторяться и рассмотрим только сложные моменты, с которыми я столкнулся при продумывании функционала блога.

+ +

Теги

+ +

В Jekyll по умолчанию нет функционала тегов, поэтому для её реализации придётся вносить изменения в проект.

+ +

Для начала о том, какой функционал мне хотелось видеть в своём блоге:

+ +
    +
  • Отображение тегов в посте.
    • +
  • Просмотр списка существующих тегов.
    • +
  • Подсчёт тегов.
    • +
  • Фильтрация постов по тегам.
    • +
+ +

Моя реализация тегов в Jekyll основана на двух инструкциях, 6 7 но с небольшими доработками.

+ +

Отображение тегов в посте

+ +

Необходимо отредактировать макет поста. Для этого нужно скопировать файл _layouts\post.html из проекта minima 8 в свой проект и добавить в конец элемента header следующий код9:

+ +
{% for tag in page.tags %}
+<a class="post" href="{{site.baseurl}}/tag/{{tag}}">#{{tag}}</a>
+{% endfor %}
+
+ +

Просмотр списка существующих тегов и подсчёт тегов

+ +

Во-первых, необходимо добавить страницу _pages\tags.html 10 на которой будет отображаться список тегов:

+ +
---
+permalink: /tags/
+layout: page
+title: Tags
+---
+
+<ul>
+    {% assign tags = site.posts | all_tags %}  
+    {% for tag in tags %}
+      <li>
+        <a class="tag-link"
+          href="{{site.baseurl}}/tag/{{tag['name']}}">
+          #{{ tag['name'] }} ({{ tag['count'] }})
+        </a>
+      </li>
+    {% endfor %}
+  </ul>
+
+ +

Во-вторых, необходимо добавить плагин в папку _plugins 11 , который будет подсчитывать теги по всем существующим постам:

+ +
module Jekyll
+    module TagsCounter
+    include Liquid::StandardFilters
+  
+    def all_tags(posts)
+      counts = {}
+  
+      posts.each do |post|
+        post['tags'].each do |tag|
+          if counts[tag]
+            counts[tag] += 1
+          else
+            counts[tag] = 1
+          end
+        end
+      end
+  
+      tags = counts.keys
+      tags.reject { |t| t.empty? }
+        .map { |tag| { 'name' => tag, 'count' => counts[tag] } }
+        .sort { |tag1, tag2| tag2['count'] <=> tag1['count'] }
+    end
+  end
+end
+
+Liquid::Template.register_filter(Jekyll::TagsCounter)
+
+ +

Фильтрация постов по тегам

+ +

Во-первых, необходимо добавить макет страницы на которой будет отображаться список постов с выбранным тегом _layouts\tag.html 12:

+ +
---
+layout: default
+---
+
+<h1>#{{page.tag}}</h1>
+<ul>
+  {% for post in site.posts %} {% if post.tags contains page.tag %}
+  <li>
+    <a class="post" href="{{post.url}}">{{ post.title }}</a>
+  </li>
+  {% endif %} {% endfor %}
+</ul>
+
+ +

Во-вторых, необходимо добавить плагин в папку _plugins 11 , который будет генерировать страницы для тегов:

+ +
module Jekyll
+    class TagPageGenerator < Generator
+      safe true
+  
+      def generate(site)
+        tags = site.posts.docs.flat_map { |post| post.data['tags'] || [] }.to_set
+        tags.each do |tag|
+          site.pages << TagPage.new(site, site.source, tag)
+        end
+      end
+    end
+  
+    class TagPage < Page
+      def initialize(site, base, tag)
+        @site = site
+        @base = base
+        @dir  = File.join('tag', tag)
+        @name = 'index.html'
+  
+        self.process(@name)
+        self.read_yaml(File.join(base, '_layouts'), 'tag.html')
+        self.data['tag'] = tag
+        self.data['title'] = "Tag: #{tag}"
+      end
+    end
+  end
+
+ +

Этот плагин при сборке блога создаст директорию site\tag в которой будут папки для каждого из тегов. Каждая из таких папок будет содержать страницу index.html, которая содержит список постов с этим тегом.

+ +

Комментарии

+ +

В Jekyll по умолчанию комментариев тоже нет, поэтому снова понадобится вносить изменения, правда не такие большие.

+ +

Вариантов виджетов 13 для комментирования много. Я использую utterances14 , потому что этот виджет основан на GitHub Issues и для его использования нужен только аккаунт GitHub. Инструкция на сайте utterances очень простая. Всё что нужно сделать - пройти по шагам для конфигурации JS-скрипта, а затем скопировать получившийся JS-скрипт в конец файла _layouts\post.html.

+ +

Сборка и деплой через GitHub

+ +

После того как проект готов, посты написаны, а изменения запушены, можно публиковать блог. Платформ для публикации блогов существует множество. Я выбрал GitHub Pages поскольку эта платформа имеет встроенную поддержку Jekyll и код блога лежит в моем профиле GitHub.

+ +

Политика безопасности GitHub Pages ограничивает использование кастомных плагинов для Jekyll, поэтому корректно собрать проект с дополнительными плагинами непосредственно при деплое не получится. Публикацию необходимо разбить на 2 шага:

+ +
    +
  1. Сборка проекта в отдельном воркфлоу GitHub Actions
    2. +
  2. Деплой собранного проекта в GitHub Pages.
    3. +
+ +

Сборка проекта в отдельном воркфлоу GitHub Actions

+ +

Существуют готовые решения для сборки Jekyll проектов. 15 Всё, что нужно сделать - добавить YAML файл в репозиторий блога. Это создаст новый воркфлоу в Actions. Как только изменения будут запушены в master, сработает этот воркфлоу. Когда сборка завершится, то собранный проект закоммитится в ветку gh-pages.

+ +

Деплой собранного проекта в GitHub Pages

+ +

Деплой настраивается на вкладке Settings репозитория. Для того чтобы деплой запускался после предыдущего шага, нужно выбрать ветку gh-pages.

+ +

first-response

+ +

Блог будет опубликован по адресу https://<username>.github.io/<projectname>/. При желании, можно использовать кастомный домен 16 , но для этого понадобится купить его у провайдера.

+ +

Ссылки и источники

+ +
+
    +
  1. +

    Download Ruby ↩

    +
    2. +
  2. +

    Конфигурация Jekyll ↩

    +
    3. +
  3. +

    Конфигурация Jekyll для моего блога ↩

    +
    4. +
  4. +

    Репозиторий темы minima ↩

    +
    5. +
  5. +

    Документация Jekyll по созданию постов ↩

    +
    6. +
  6. +

    Managing tags in Jekyll blog easily ↩

    +
    7. +
  7. +

    Tags in Jekyll ↩

    +
    8. +
  8. +

    Файл макета post.html из minima ↩

    +
    9. +
  9. +

    Файл макета _post.html из моего проекта ↩

    +
    10. +
  10. +

    Файл tags.html ↩

    +
    11. +
  11. +

    Папка с плагинами ↩ ↩2

    +
    12. +
  12. +

    Файл макета tag.html ↩

    +
    13. +
  13. +

    How to add comments in blog posts on GitHub-pages websites ↩

    +
    14. +
  14. +

    Виджет utterances ↩

    +
    15. +
  15. +

    Jekyll Deploy Action ↩

    +
    16. +
  16. +

    Managing a custom domain for your GitHub Pages site ↩

    +
    17. +
+
+
+
+ + + +
+
+ + diff --git a/2023/01/31/react-html-annotations.html b/2023/01/31/react-html-annotations.html new file mode 100644 index 0000000..5fc1d31 --- /dev/null +++ b/2023/01/31/react-html-annotations.html @@ -0,0 +1,320 @@ + + + + + + + +How to create text annotations using React and TypeScript | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ How to create text annotations using React and TypeScript +

+

+ + #typescript + + #react + +
+ +

I changed my job last November and became a Full Stack Developer in a new team. Now I have lots of work related with frontend, especially with React and TypeScript. A few weeks ago, I had a task to implement annotations support in our application that we are developing. This article briefly describes the approaches that can be used to create an application with text annotations.

+ +

What is required?

+ +

Let’s imagine that we have the following text content:

+ +

content

+ +

We need to implement functionality that allows the user to add text annotations on the selected part of content. For example, such functionality in Google Docs:

+ +

content

+ +

To do that we should understand how to get information about selected text, how to save and restore it and how to highlight selected part of the text if the user adds annotation to it.

+ +

Selection and Ranges

+ +

DOM standard describes an interface named Range, which represents part of the content between two boundary points. Using Range, we can easily get the information about selection. To do that, just call getSelection and then getRangeAt:

+ +
const s = window.getSelection();
+const range = s.getRangeAt(0);
+
+ +

Parameter range is of interface Range. This interface inherits another interface AbstractRange which looks like:

+ +
interface AbstractRange {
+  /** Returns true if range is collapsed, and false otherwise. */
+  readonly collapsed: boolean;
+  /** Returns range's end node. */
+  readonly endContainer: Node;
+  /** Returns range's end offset. */
+  readonly endOffset: number;
+  /** Returns range's start node. */
+  readonly startContainer: Node;
+  /** Returns range's start offset. */
+  readonly startOffset: number;
+}
+
+ +

The most important fields are startContainer, startOffset, endContainer, endOffset. These fields just determine selection. Let’s look at HTML code of the content that was shown above:

+ +
<div id="root"> 
+  <div>
+    <h5>What is Lorem Ipsum?</h5>
+    <p>
+      <strong>Lorem Ipsum</strong> is simply dummy text of the printing and
+      typesetting industry. Lorem Ipsum has been the industry's standard dummy
+      text ever since the 1500s, when an unknown printer took a galley of type
+      and scrambled it to make a type specimen book. It has survived not only
+      five centuries, but also the leap into electronic typesetting, remaining
+      essentially unchanged. It was popularised in the 1960s with the release of
+      Letraset sheets containing Lorem Ipsum passages, and more recently with
+      desktop publishing software like Aldus PageMaker including versions of
+      Lorem Ipsum.
+    </p>
+  </div>
+</div>
+
+ +

For the considered example, selection range will be:

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
FieldValue
startContainer#text (child of <h5>)
startOffset12
endContainer#text (child of <p>)
endOffset74
+ +

Now let’s look at startContainer and endContainer differently. The nodes that stored by these fields can be determined by their indexes in the child arrays of the DOM tree. It means, that we can determine the addresses of the start and end nodes relative to the root node by recursively traversing the DOM tree. Thus, the addresses will be [0, 0, 0, 0], [1, 1, 0, 0] for startContainer and endContainer respectively:

+ +
[0] <div id="root">
+ |
+ ╵--[0] <div>
+     |
+     |-- [0] <h5>
+     |    |
+     |    ╵--[0] #text <-- startContainer (startOffset: 12) 
+     |
+     ╵-- [1] <p>
+          |
+          |--[0] <strong>
+          |
+          |--[1] #text <-- endContainer (endOffset: 74) 
+          |
+         ...
+
+ +

This approach allows us to save selection data in the following class:

+ +
class SelectionData {
+  startNode: number[];
+  endNode: number[];
+  startTextOffset: number;
+  endTextOffset: number;
+
+  // other members
+}
+
+ +

Functions for serializing Range to SelectionData and vice versa you can find here.

+ +

Content highlighting

+ +

Now we know how to save and restore information about selection, but how we can highlight the selected content? Function getClientRects will help us with that. This function returns a list of DOMRect objects.

+ +
interface Range extends AbstractRange {
+  getClientRects(): DOMRectList;
+
+  // other members
+}
+
+ +

In our case, DOMRect describes the size and position of the selected area of content.

+ +
interface DOMRectReadOnly {
+  readonly bottom: number;
+  readonly height: number;
+  readonly left: number;
+  readonly right: number;
+  readonly top: number;
+  readonly width: number;
+  readonly x: number;
+  readonly y: number;
+  toJSON(): any;
+}
+
+interface DOMRect extends DOMRectReadOnly {
+  height: number;
+  width: number;
+  x: number;
+  y: number;
+}
+
+ +

Thus, using top, left, height and width we can create, for example, a <div> element with calculated position and size to highlight the content. In a React app, highlighting can be implemented something like this:

+ +
// Highlighting component
+export default function Highlighting(props: {
+  highlighting: HighlightingData;
+}) {
+  return <div style={styleHighlighting(props.highlighting)} />;
+}
+
+const styleHighlighting = (h: HighlightingData) => {
+  const s: CSSProperties = {
+    position: "absolute",
+    border: `1.5px solid darkgreen`,
+    borderRadius: "5px",
+    padding: "2px",
+    top: `${h.top}px`,
+    left: `${h.left - 2}px`,
+    width: `${h.width + 4}px`,
+    height: `${h.height}px`,
+  };
+  return s;
+};
+
+// Determines highlighted area
+class HighlightingData {
+  top: number = 0;
+  left: number = 0;
+  right: number = 0;
+  bottom: number = 0;
+  width: number = 0;
+  height: number = 0;
+}
+
+ +

Demo sandbox

+ +

The approaches described in this article were implemented in a demo application. You can find the source code in my GitHub profile as well as in the sandbox below.

+ + +
+
+ + + +
+
+ + diff --git a/2023/02/25/ef-inheritance.html b/2023/02/25/ef-inheritance.html new file mode 100644 index 0000000..e94ccbc --- /dev/null +++ b/2023/02/25/ef-inheritance.html @@ -0,0 +1,394 @@ + + + + + + + +Impact of EF mapping strategy on SQL queries performance | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Impact of EF mapping strategy on SQL queries performance +

+

+ + #csharp + + #efcore + + #sqlserver + +
+ +

Entity Framework (EF) is a popular object-relational mapping framework used to interact with databases in .NET applications. EF offers different mapping strategies, each of which affects the database schema and how EF interacts with the hierarchy of .NET types. In this article, we will examine how these mapping strategies affects performance in SQL Server.

+ +

Mapping strategies in Entity Framework

+ +

The official Microsoft documentation describes 3 mapping strategies:

+ +
    +
  • Table-per-hierarchy (TPH);
    • +
  • Table-per-class (TPC);
    • +
  • Table-per-type (TPT).
    • +
+ +

In my experience, I have also encountered another mapping strategy that I refer to as TPH with JSON. The main idea behind this strategy is to convert properties from the derived classes to a JSON payload and add a discriminator column, similar to the default TPH strategy.

+ +

Let’s have a look at the hierarchy below:

+ +
public class Root
+{
+    // Root properties
+}
+
+public class ChildA : Root
+{
+    // ChildA propertie
+}
+
+public class ChildB : Root
+{
+    // ChildB properties
+}
+
+public class ChildC : Root
+{
+    // ChildC properties
+}
+
+ +

The database schemas for this hierarchy will be as follows:

+ + + + + + + + + + + + + + + + + + +
TPHTPCTPTTPH with JSON
contentcontentcontentcontent
+ +

As shown below:

+ +
    +
  • TPH table contains all the properties from all the types.
    • +
  • TPC child tables are non related to the parent Roots table.
    • +
  • TPT child tables contains only type specific columns and related to the parent Roots table.
    • +
  • TPH with JSON looks very similar to plain TPH, except all the columns for child properties replaced by one JSON column Payload. Column PayloadType corresponds to Discriminator column in TPH.
    • +
+ +

Now let’s find out how all these mapping strategies affect on performance in SQL Server.

+ +

Benchmark

+ +

The benchmark project has 4 instances of DbContext for each kind of mapping strategy. TPH, TPC and TPT are pretty simple. TPH with JSON requires additional wrapper class to transform child DB objects to JSON payload:

+ +
public class RootWrapper : Root
+{
+    public string Payload { get; set; } = string.Empty;
+
+    [NotMapped]
+    public Root OriginalEntity
+    {
+        get
+        {
+            // code for deserializing JSON to ChildA, ChildB or ChildC
+        }
+        set
+        {
+            // code for serializing ChildA, ChildB or ChildC to JSON 
+        }
+    }
+
+
+    public PayloadTypes PayloadType { get; set; }
+
+    public enum PayloadTypes
+    {
+        Root,
+        ChildA,
+        ChildB,
+        ChildC,
+    }
+}
+
+ +

I also used Bogus library for generating fake data and BenchmarkDotNet library for benchmarking itself.

+ +

There are 2 benchmarks in the projects - for INSERT and SELECT data. I didn’t test UPDATE and DELETE because it requires to specify the filter predicate and it’s little bit complicated due to randomized fake data.

+ +

Results

+ +

Here are the benchmark results. As we can see from the figures below, TPT has the worst performance for SELECT as well as INSERT. This strategy is about 40-60% slower than TPH which is default for EF. TPC performs slightly better in selecting data, but slightly worse in inserting data compared to TPH. The most interesting fact is that TPH with JSON performance better than TPH performance.

+ +
+
+
    +
  • + Image 1 + Figure 1 - Execution time for inserting data +
    • +
  • + Image 2 + Figure 2 - Execution time for selecting data +
    • +
+
+
+ + +
+
+ +

TPH vs TPH with JSON

+ +

I was curious why TPH with JSON shows better performance that TPH, so I created another benchmark. This benchmark uses instances of DbContext with entities that have 3, 5, 8, 13 and 21 properties for each type of mapping strategy (10 in total).

+ +

As we can see from figure 3 and 4, TPH with JSON strategy has almost the same insertion time, while the TPH execution time increases with both the number of properties and the number of entities.

+ +
+
+
    +
  • + + Figure 3 - Dependence of inserting time on the number of entities and the number of properties +
    • +
  • + + Figure 4 - Ratio of `TPH` execution time to `TPH with JSON` execution time for inserting data +
    • +
  • + + Figure 5 - Ratio of `TPH` execution time to `TPH with JSON` execution time for inserting data +
    • +
+
+
+ + +
+
+ +

Apparently, TPH has worse performance due to two things:

+ +
    +
  1. EF Core creates separate INSERT queries for each of type in the hierarchy.
    2. +
  2. EF Core creates parameters for each property.
    3. +
+ +

In this particular example, EF Core created 4 INSERT queries and 82 parameters:

+ +
INSERT INTO [tph21].[Roots] ([ChildAProp1], [ChildAProp10], [ChildAProp11], [ChildAProp12], [ChildAProp13], [ChildAProp14], [ChildAProp15], [ChildAProp16], [ChildAProp17], [ChildAProp18], [ChildAProp19], [ChildAProp2], [ChildAProp20], [ChildAProp21], [ChildAProp3], [ChildAProp4], [ChildAProp5], [ChildAProp6], [ChildAProp7], [ChildAProp8], [ChildAProp9], [Discriminator], [RootProp1], [RootProp2], [RootProp3], [RootProp4])
+OUTPUT INSERTED.[Id]
+VALUES (@p0, @p1, @p2, @p3, @p4, @p5, @p6, @p7, @p8, @p9, @p10, @p11, @p12, @p13, @p14, @p15, @p16, @p17, @p18, @p19, @p20, @p21, @p22, @p23, @p24, @p25);
+
+INSERT INTO [tph21].[Roots] ([ChildBProp1], [ChildBProp10], [ChildBProp11], [ChildBProp12], [ChildBProp13], [ChildBProp14], [ChildBProp15], [ChildBProp16], [ChildBProp17], [ChildBProp18], [ChildBProp19], [ChildBProp2], [ChildBProp20], [ChildBProp21], [ChildBProp3], [ChildBProp4], [ChildBProp5], [ChildBProp6], [ChildBProp7], [ChildBProp8], [ChildBProp9], [Discriminator], [RootProp1], [RootProp2], [RootProp3], [RootProp4])
+OUTPUT INSERTED.[Id]
+VALUES (@p26, @p27, @p28, @p29, @p30, @p31, @p32, @p33, @p34, @p35, @p36, @p37, @p38, @p39, @p40, @p41, @p42, @p43, @p44, @p45, @p46, @p47, @p48, @p49, @p50, @p51);
+
+INSERT INTO [tph21].[Roots] ([ChildCProp1], [ChildCProp10], [ChildCProp11], [ChildCProp12], [ChildCProp13], [ChildCProp14], [ChildCProp15], [ChildCProp16], [ChildCProp17], [ChildCProp18], [ChildCProp19], [ChildCProp2], [ChildCProp20], [ChildCProp21], [ChildCProp3], [ChildCProp4], [ChildCProp5], [ChildCProp6], [ChildCProp7], [ChildCProp8], [ChildCProp9], [Discriminator], [RootProp1], [RootProp2], [RootProp3], [RootProp4])
+OUTPUT INSERTED.[Id]
+VALUES (@p52, @p53, @p54, @p55, @p56, @p57, @p58, @p59, @p60, @p61, @p62, @p63, @p64, @p65, @p66, @p67, @p68, @p69, @p70, @p71, @p72, @p73, @p74, @p75, @p76, @p77);
+     
+INSERT INTO [tph21].[Roots] ([Discriminator], [RootProp1], [RootProp2], [RootProp3], [RootProp4])
+OUTPUT INSERTED.[Id]
+VALUES (@p78, @p79, @p80, @p81, @p82);
+
+ +

For TPH with JSON EF Core created only one INSERT query because all types in hierarchy were converted into single wrapper class. The number of parameters is 23 which is also almost 4 times less:

+ +
MERGE [tphjson21].[Roots] USING (
+VALUES (@p0, @p1, @p2, @p3, @p4, @p5, 0),(@p6, @p7, @p8, @p9, @p10, @p11, 1),(@p12, @p13, @p14, @p15, @p16, @p17, 2),(@p18, @p19, @p20, @p21, @p22, @p23, 3)) AS i ([Payload], [PayloadType], [RootProp1], [RootProp2], [RootProp3], [RootProp4], _Position) ON 1=0
+WHEN NOT MATCHED THEN
+INSERT ([Payload], [PayloadType], [RootProp1], [RootProp2], [RootProp3], [RootProp4])
+VALUES (i.[Payload], i.[PayloadType], i.[RootProp1], i.[RootProp2], i.[RootProp3], i.[RootProp4])
+OUTPUT INSERTED.[Id], i._Position;
+
+ +

The situation for selecting data is a bit different. According to the benchmark results, TPH is slightly worse than TPH with JSON when properties count is less than 8. For all other cases, TPH has better execution time.

+ +
+
+
    +
  • + + Figure 6 - Dependence of selecting time on the number of entities and the number of properties +
    • +
  • + + Figure 7 - Ratio of `TPH` execution time to `TPH with JSON` execution time for selecting data +
    • +
  • + + Figure 8 - Ratio of `TPH` execution time to `TPH with JSON` execution time for selecting data +
    • +
+
+
+ + +
+
+ +

For both mapping strategies, EF Core created a single SELECT query. The only difference is in columns count. It seems that JSON serialization can only affect performance with a relatively large number of properties.

+ +
SELECT [r].[Id], [r].[Discriminator], [r].[RootProp1], [r].[RootProp2], [r].[RootProp3], [r].[RootProp4], [r].[ChildAProp1], [r].[ChildAProp10], [r].[ChildAProp11], [r].[ChildAProp12], [r].[ChildAProp13], [r].[ChildAProp14], [r].[ChildAProp15], [r].[ChildAProp16], [r].[ChildAProp17], [r].[ChildAProp18], [r].[ChildAProp19], [r].[ChildAProp2], [r].[ChildAProp20], [r].[ChildAProp21], [r].[ChildAProp3], [r].[ChildAProp4], [r].[ChildAProp5], [r].[ChildAProp6], [r].[ChildAProp7], [r].[ChildAProp8], [r].[ChildAProp9], [r].[ChildBProp1], [r].[ChildBProp10], [r].[ChildBProp11], [r].[ChildBProp12], [r].[ChildBProp13], [r].[ChildBProp14], [r].[ChildBProp15], [r].[ChildBProp16], [r].[ChildBProp17], [r].[ChildBProp18], [r].[ChildBProp19], [r].[ChildBProp2], [r].[ChildBProp20], [r].[ChildBProp21], [r].[ChildBProp3], [r].[ChildBProp4], [r].[ChildBProp5], [r].[ChildBProp6], [r].[ChildBProp7], [r].[ChildBProp8], [r].[ChildBProp9], [r].[ChildCProp1], [r].[ChildCProp10], [r].[ChildCProp11], [r].[ChildCProp12], [r].[ChildCProp13], [r].[ChildCProp14], [r].[ChildCProp15], [r].[ChildCProp16], [r].[ChildCProp17], [r].[ChildCProp18], [r].[ChildCProp19], [r].[ChildCProp2], [r].[ChildCProp20], [r].[ChildCProp21], [r].[ChildCProp3], [r].[ChildCProp4], [r].[ChildCProp5], [r].[ChildCProp6], [r].[ChildCProp7], [r].[ChildCProp8], [r].[ChildCProp9]
+FROM [tph21].[Roots] AS [r]
+
+ +
SELECT [r].[Id], [r].[Payload], [r].[PayloadType], [r].[RootProp1], [r].[RootProp2], [r].[RootProp3], [r].[RootProp4]
+FROM [tphjson21].[Roots] AS [r]
+
+ +

Conclusion

+ +

In conclusion, the benchmark conducted in this article sheds light on the differences between various mapping strategies in EF Core. The summarized results are presented in the table below.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PlaceMapping strategyProsCons
1TPH with JSONBetter performance when inserting and selecting data compared to other strategies almost in all cases- Requires additional coding to implement wrapper class
- Worse that TPH in selecting data from tables with relatively large number of columns
- Requires the creation of computed columns to create JSON indexes
1TPH- Default EF Core strategy that doesn’t require any additional configuration
- Relatively good performance almost in all cases		Performance degradation when there are many types in hierarchy
2TPCSlightly better than TPH in inserting dataSlightly worse than TPH in selecting data
3TPT???The worst performance in all cases
+
+
+ + + +
+
+ + diff --git a/2023/06/05/understanding-ddd-01.html b/2023/06/05/understanding-ddd-01.html new file mode 100644 index 0000000..20f2c01 --- /dev/null +++ b/2023/06/05/understanding-ddd-01.html @@ -0,0 +1,341 @@ + + + + + + + +Understanding Domain-Driven Design. From Anemic Domain Model to Rich Domain Model. | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Understanding Domain-Driven Design. From Anemic Domain Model to Rich Domain Model. +

+

+ + #csharp + + #ddd + +
+ +

I recently read the book Learning Domain-Driven Design on the advice of my mate, who is more experienced in development than I am. I’ve already written a small review about this book in my Telegram channel (it’s in Russian). Now, I’m going to write a series of articles about how Domain-Driven Design (DDD) can improve your code and software development process. This is the first article in this series.

+ +

Anemic Domain Model (ADM)

+ +

I’m not sure if I can describe what ADM is better that Martin Fowler. Firstly and foremost, I highly recommend reading his article about this antipattern.

+ +

In essence, ADM represents a Domain Model that lacks behavior, consisting solely of a bunch of getters and setters. I created a simple CLI application which uses ADM. The code I have written is based on what I saw during my career as a software developer, and we’ll try to improve it.

+ +

The application simulates a system for tracking tickets. It has the following models:

+ +
    +
  1. Worker - a person responsible for resolving the tickets.
    2. +
  2. Ticket - an entity that describes a problem and can be assigned to any worker.
    3. +
+ +

The functionality of the application allows the user to:

+ +
    +
  1. Add workers.
    2. +
  2. Update workers info.
    3. +
  3. Fire workers.
    4. +
  4. Get workers info.
    5. +
  5. Open tickets for workers.
    6. +
  6. Close tickets for workers.
    7. +
+ +

Imagine that we have a ticket with Id = 1 and user tries to update Description of the ticket. To do that, user executes:

+ +
dotnet run -- ticket update -id 1 -c "Some new content"
+
+ +

In the result, the ticket will be updated, but what’s going on inside the application? The sequence diagram for this workflow is below:

+ +

content +Figure 1 - Sequence diagram for ticket updating

+ +

Seems quite complicated, doesn’t it? What exactly are the disadvantages of this architecture? Here is the list:

+ +
    +
  1. Domain Models and Database Objects are pretty much the same. The DRY principle is violated.
    2. +
  2. If you need to implement new functionality, you’ll need to write at least twice as much code.
    3. +
  3. If you need to support existing code, you’ll have to spend at least twice as much time on maintanance.
    4. +
  4. For each pair of Domain Model - Database Object, you have to create a mapper. More code increases the chances of creating bugs.
    5. +
  5. Unnecessary calls to the DB.
    6. +
  6. If you need to process hundreds or thousands of entities, you have to load all these DBOs into memory and map them to Domain Models. This will significantly degrade application performance.
    7. +
+ +

How we can improve this existing code? Take a look at the sequence diagram for opening a new ticket:

+ +

content +Figure 2 - Sequence diagram for opening ticket

+ +

This example doesn’t have unnecessary requests to the DB, but we still have all other problems that were mentioned earlier. Moreover, you may be wondering where is the BLL? That is one of the problem of such an approach. The business logic is spread across by different kinds of handler. In this particular case, the logic of ticket assigning has leaked to the DAL.

+ +

Is it possible to further improve the code? Yes, and this is where a Rich Domain Model (RDM) comes into play.

+ +

Rich Domain Model (RDM)

+ +

As I mentioned earlier, ADM is simply a class with a collection of getters and setters. However, it’s evident that an RDM is more that just a bunch of getters and setters. It also contains functionality that is directly related to the domain.

+ +

To refactor the code from ADM to RDM, we should follow these steps:

+ +
    +
  1. Get rid of duplicated models and mappers by combining Domain Models with DBO.
    2. +
  2. Replace Requests and Queries with Commands and Events.
    3. +
  3. Consolidate all database-related operations from different handlers into a single command handler.
    4. +
  4. Transfer domain functionality from handlers to Domain Models.
    5. +
+ +

Get rid of duplications

+ +

The first step will be is combine the duplicated models. Lets’do it for Worker entity. In the initial project, we had Worker class and WorkerDbo class as a Domain Model and Database Object, respectively:

+ +
public record Worker
+{
+    public int Id { get; set; }
+    public string Name { get; set; } = string.Empty;
+    public string Email { get; set; } = string.Empty;
+    public string Position { get; set; } = string.Empty;
+    public bool Fired { get; set; }
+    public DateTimeOffset Created { get; set; }
+    public DateTimeOffset Updated { get; set; }
+}
+
+public record WorkerDbo
+{
+    public int Id { get; set; }
+    public string Name { get; set; } = string.Empty;
+    public string Email { get; set; } = string.Empty;
+    public string Position { get; set; } = string.Empty;
+    public bool Fired { get; set; }
+    public DateTimeOffset Created { get; set; }
+    public DateTimeOffset Updated { get; set; }
+    public ICollection<TicketDbo> AssignedTickets { get; set; } = new List<TicketDbo>();
+}
+
+ +

After refactoring, Domain Model for the Worker will be:

+ +
public record Worker
+{
+    public int Id { get; protected set; }
+    public string Name { get; protected set; } = string.Empty;
+    public string Email { get; protected set; } = string.Empty;
+    public string Position { get; protected set; } = string.Empty;
+    public bool Fired { get; protected set; }
+    public DateTimeOffset Created { get; protected set; }
+    public DateTimeOffset Updated { get; protected set; }
+    public ICollection<Ticket> AssignedTickets { get; protected set; } = new List<Ticket>();
+}
+
+ +

This is a crucial and straightforward step. By removing duplicates, we significantly simplified the maintenance of the code. Now, there is no reason to write the same code multiple times and write mappers. Note that all properties have protected set modifier. Consequently, Domain Model mutations can only be done from within of the class.

+ +

Commands and Events

+ +

The next step is to replace Requests and Queries by Commands and Events.

+ +

A command is a structure which encapsulates an action data that will cause the mutation of the Domain Model. According to the naming conventions in DDD, Commands should be named in an imperative mood plus the Domain Model name. For example, CreateWorker, UpdateWorker, OpenTicketForWorker.

+ +

An event is a structure which encapsulates the result of Domain Model mutation. According to the naming conventions in DDD, Events should be named with the Domain Model name plus the verb in past tense. For example, WorkerCreated, WorkerUpdated, TicketForWorkerOpened.

+ +

Single command handler

+ +

We already have updated Domain Models and also Commands and Events. Now it’s time to implement a Command Handler which will be a single point for all commands in the application. Depends on the type of the Command, Command Handler will call appropriate method for Domain Models.

+ +
public class CommandHandler
+{
+    public EventBase Handle(CommandBase command)
+    {
+        var @event = command switch
+        {
+            TicketCommand cmd => Handle(cmd),
+            WorkerCommand cmd => Handle(cmd),
+            _ => throw new NotSupportedException(),
+        };
+        return @event;
+    }
+}
+
+ +

Private Handle methods implemented according to one pattern:

+ +
    +
  1. Extract entity from the DB.
    2. +
  2. Call appropriate Handle method in the Domain Model
    3. +
  3. Save changes in the DB.
    4. +
  4. Return result event.
    5. +
+ +

For example, Handler method for Worker model:

+ +
private EventBase Handle(WorkerCommand command)
+{
+    var db = new DatabaseAdapter();
+
+    var @event = command switch
+    {
+        CreateWorker cmd => db.Workers
+            .Add(new Worker()).Entity
+            .Handle(cmd),
+        UpdateWorker cmd => db.Workers
+            .First(x => x.Id == cmd.Id)
+            .Handle(cmd),
+        FireWorker cmd => db.Workers
+            .First(x => x.Id == cmd.Id)
+            .Handle(cmd),
+        GetWorkerInfo cmd => db.Workers
+            .First(x => x.Id == cmd.Id)
+            .Handle(cmd),
+        OpenTicketForWorker cmd => db.Workers
+            .First(x => x.Id == cmd.Id)
+            .Handle(cmd),
+        CloseTicketForWorker cmd => db.Workers
+            .Include(x => x.AssignedTickets)
+            .First(x => x.Id == cmd.Id)
+            .Handle(cmd),
+        _ => throw new NotSupportedException(),
+    };
+    db.SaveChanges();
+    return @event;
+}
+
+ +

Handlers in Domain Model

+ +

Last, but not least, we need to move domain functionality from old handlers to handlers in the Domain Model. The new handlers will be much simpler because now they only contain domain logic without functionality to database. For example, the handler for the UpdateWorker command looks like:

+ +
public EventBase Handle(UpdateWorker request)
+{
+    if (request.Name != null) Name = request.Name;
+    if (request.Email != null) Email = request.Email;
+    if (request.Position != null) Position = request.Position;
+    if (request.Name != null || 
+        request.Email != null || 
+        request.Position != null) Updated = DateTimeOffset.Now;
+    return new WorkerUpdated(this);
+}
+
+ +

After refactoring

+ +

Now, let’s examine the sequence diagram for the ticket updating functionality and compare with Figure 1 and 2. You will notice that it has become much simpler:

+ +

content +Figure 3 - Sequence diagram for ticket updating using RDM

+ +

Actually, the diagram will remain the same for all types of commands, not just for for updating ticket. The only difference will be in Business Logic part.

+ +

Conclusion

+ +

In this article, I tried to show how DDD patterns and RDM can improve the code base and the architecture of your application. The advantages of using DDD and RDM are the following:

+ +
    +
  1. Encapsulation of data and behavior. Domain Models are no longer just a bunch of getters and setters.
    2. +
  2. Understandable naming conventions, e.g. for Commands and Events.
    3. +
  3. There is no need to have several classes with the same set of properties and create mappers for them.
    4. +
  4. Database calls are kept to a minimum.
    5. +
+
+
+ + + +
+
+ + diff --git a/2023/07/30/secure-app-with-keycloak.html b/2023/07/30/secure-app-with-keycloak.html new file mode 100644 index 0000000..37eae11 --- /dev/null +++ b/2023/07/30/secure-app-with-keycloak.html @@ -0,0 +1,676 @@ + + + + + + + +Create secure React SPA + ASP.NET Web API with Keycloak OAuth2 | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Create secure React SPA + ASP.NET Web API with Keycloak OAuth2 +

+

+ + #csharp + + #aspnet + + #typescript + + #react + + #docker + + #oauth2 + + #keycloak + +
+ +

This is a step-by-step guide how to create a full stack secure application with OAuth2 authentication.

+ +

Table of contents

+ + + +

Introduction

+ +

Keycloak is an open-source identity and access management (IAM) solution. It provides user authentication, authorization, and user management. Keycloak allows you to secure web applications by providing a centralized authentication and authorization mechanism. It supports various authentication methods, including username/password, social login (e.g., Google, Facebook), and more. Keycloak is based on standard protocols such as OAuth 2.0 and OpenID Connect, making it compatible with a wide range of applications and services.

+ +

Prerequisites

+ +

Since we are going to create a full stack application I assume that you have knowledge of:

+ +
    +
  1. C# and ASP.NET Web API.
    2. +
  2. TypeScript and React.
    3. +
  3. Docker.
    4. +
+ +

You also have to have the following software on your machine:

+ +
    +
  1. .NET SDK
    2. +
  2. Node.js with npm
    3. +
  3. Docker
    4. +
+ +

Initialize the app

+ +

Create ASP.NET Web API service

+ +

Let’s start with the backend part. We’re going to create an ASP.NET Web API service using the following command:

+ +
dotnet new webapi --no-openapi --use-minimal-apis
+
+ +

I used 2 options to simplify the source code:

+ +
    +
  • –-no-openapi disables OpenAPI specification generation.
    • +
  • –-use-minimal-apis generates minimalistic version of HTTP requests handlers.
    • +
+ +

The result is below.

+ +
var builder = WebApplication.CreateBuilder(args);
+var app = builder.Build();
+app.UseHttpsRedirection();
+
+var summaries = new[]
+{
+    "Freezing", "Bracing", "Chilly", "Cool", "Mild", "Warm", "Balmy", "Hot", "Sweltering", "Scorching"
+};
+
+app.MapGet("/weatherforecast", () =>
+{
+    var forecast = Enumerable.Range(1, 5).Select(index =>
+        new WeatherForecast
+        (
+            DateOnly.FromDateTime(DateTime.Now.AddDays(index)),
+            Random.Shared.Next(-20, 55),
+            summaries[Random.Shared.Next(summaries.Length)]
+        ))
+        .ToArray();
+    return forecast;
+});
+
+app.Run();
+
+record WeatherForecast(DateOnly Date, int TemperatureC, string? Summary)
+{
+    public int TemperatureF => 32 + (int)(TemperatureC / 0.5556);
+}
+
+ +

The service has only one endpoint /weatherforecast. Right now it is not secured, so anyone can call it and receive the data.

+ +

content +The endpoint /weatherforecast is not secured

+ +

Create React SPA

+ +

Create the client via:

+ +
npx create-react-app client -—template typescript
+
+ +

This command generates a bunch of files we don’t need, so I removed them to simplify the code.

+ +

Now project looks like:

+ +

content +Initial project

+ +

Connect client with server

+ +

Right now, the project contains two different applications (client and server) which don’t communicate with each other. Let’s connect them, so that the client will be able to call /weatherforecast endpoint:

+ +
    +
  1. +

    At the server side a CORS policy should be added. The policy allows any header, any method and any origin. Don’t do it in production, but for development purposes it’s ok.

    + + 
     builder.Services.AddCors();
+
+ // other code
+
+ if (builder.Environment.IsDevelopment())
+ {
+     app.UseCors(config =>
+     {
+         config.AllowAnyHeader();
+         config.AllowAnyMethod();
+         config.AllowAnyOrigin();
+     });
+ }
+
    +
    2. +
  2. +

    At the client side WeatherForecastView component should be created. This component calls /weatherforecast endpoint and renders the received data.

    + + 
     // App.tsx
+ export default function App() {
+     return (
+         <div className="App">
+             <WeatherForecastView />
+         </div>
+     );
+ }
+
+ // WeatherForecast.ts
+ export interface WeatherForecast {
+     date: Date;
+     temperatureC: number;
+     summary: string;
+ }
+
+ // WeatherForecastView.tsx
+ import { useEffect, useState } from "react";
+ import { useApi } from "./hooks/useApi";
+ import { WeatherForecast } from "./types/WeatherForecast";
+
+ export default function WeatherForecastView() {
+     const [get] = useApi();
+     const [forecast, setForecast] = useState<WeatherForecast[]>([]);
+
+     useEffect(() => {
+         get().then((wf) => setForecast(wf));
+     }, []);
+
+     return (
+         <table>
+         <thead>
+             <tr>
+             <th>Date</th>
+             <th>Temperature</th>
+             <th>Summary</th>
+             </tr>
+         </thead>
+         <tbody>
+             {forecast.map((f) => (
+             <tr>
+                 <td>{new Date(f.date).toDateString()}</td>
+                 <td>{f.temperatureC}</td>
+                 <td>{f.summary}</td>
+             </tr>
+             ))}
+         </tbody>
+         </table>
+     );
+ }
+
    +
    3. +
+ +

Dockerize the app

+ +

Now we are going to put both applications in a single Docker container. The dockerfile is pretty straightforward. It uses Node.js and .NET SDK images to build both of the applications and then makes the final container using an ASP.NET image. Note that built client application will be copied to the wwwroot folder.

+ +
# Stage 1: Frontend - React SPA (https://hub.docker.com/_/node)
+FROM node:18-alpine AS frontend-build
+WORKDIR /
+COPY ./client ./
+RUN npm i && npm run build
+
+# Stage 2: Backend - ASP.NET Core Web API (https://hub.docker.com/_/microsoft-dotnet-sdk/)
+FROM mcr.microsoft.com/dotnet/sdk:7.0-alpine AS backend-build
+WORKDIR /
+
+# Copy projects and restore dependencies
+COPY ./server/*.csproj ./server/
+RUN dotnet restore ./server/
+
+# Copy everything else
+COPY . .
+
+# Build app
+WORKDIR /server
+RUN dotnet publish -c release -o /app
+
+# Stage 3: Create final container
+FROM mcr.microsoft.com/dotnet/aspnet:7.0-alpine
+WORKDIR /app
+COPY --from=frontend-build /build ./wwwroot
+COPY --from=backend-build /app ./
+ENTRYPOINT ["dotnet", "server.dll"]
+
+
+ +

I also created docker-compose.yml file to quickly run the container.

+ +
version: "3.9"
+
+services:
+  webapp:
+    container_name: secure-app
+    image: secure-app
+    ports:
+      - 5000:80
+    environment:
+      - ASPNETCORE_URLS=http://+:80
+
+ +

To make the client and the server work together we should do a small change in Program.cs file. This сhange allows the server to return a built client application i.e. static HTML-page.

+ +
if (builder.Environment.IsDevelopment())
+{
+    // other code
+}
+else
+{
+    app.UseStaticFiles(); // Enables static files serving from wwwroot folder
+    app.MapFallbackToFile("index.html"); // Maps to index.html from wwwroot folder
+}
+
+// other code
+
+ +

To build and run the dockerized application execute the following commands.

+ +
docker build -t secure-app .
+docker compose up 
+
+ +

Secure the app

+ +

Setup Keycloak

+ +

The easiest way to run Keycloak instance is run it in docker container using the following command.

+ +
docker run -p 8080:8080 -e KEYCLOAK_ADMIN=admin -e KEYCLOAK_ADMIN_PASSWORD=admin quay.io/keycloak/keycloak:21.1.2 start-dev
+
+ +

Or, what is easier, using docker compose up command with docker-compose.yml file.

+ +
version: "3.9"
+
+services:
+  webapp:
+    container_name: secure-app
+    image: secure-app
+    ports:
+      - 5000:80
+    environment:
+      - ASPNETCORE_URLS=http://+:80
+  keycloak:
+    container_name: keycloak
+    image: quay.io/keycloak/keycloak:21.1.2
+    ports:
+      - "8080:8080"
+    environment:
+      - KEYCLOAK_ADMIN=admin
+      - KEYCLOAK_ADMIN_PASSWORD=admin
+    command: ["start-dev"]
+
+ +

Basic Keycloak setup consist of the following steps:

+ +
    +
  1. Go to http://localhost:8080/admin and log in as admin.
    2. +
  2. +

    Create realm.

    + +

    content

    +
    3. +
  3. +

    Put some name for realm.

    + +

    content

    +
    4. +
  4. +

    Go to Users, press Create new user and create a new user.

    + +

    content

    +
    5. +
  5. +

    Go to Credentials tab, press Set password and create a creadentials for the user. You can uncheck Temporary so that you don’t have to change the password the first time you log in.

    + +

    content

    +
    6. +
  6. +

    Go to Clients and select account.

    + +

    content

    +
    7. +
  7. +

    On the Settings tab, add a new valid redirect URI and enter a value in the Web Origin field.

    + +

    content

    +
    8. +
  8. +

    Go to Client scopes, press Create client scope button and create audience scope. Note, that the type should be Default.

    + +

    content

    +
    9. +
  9. +

    In the new audience scope go to the Mappers tab, press Configure a new mapper and select Audience mapper.

    + +

    content

    +
    10. +
  10. +

    Fill in the fields and press Save.

    + +

    content

    +
    11. +
  11. +

    Return to the Client, select account scope, go to the Client scopes tab and press Add client scope. Select from the list the audience scope and add it as a default.

    + +

    content

    +
    12. +
+ +

You can export and import realms. For example, a configuration for the realm above can be found here.

+ +

Change the client

+ +
    +
  1. +

    Install react-oidc-context npm package. This library simplifies the process of OAuth2 authentication for React applications. More info can be found here.

    + + 
     npm i react-oidc-context
+
    +
    2. +
  2. +

    After installation we should wrap App by AuthProvider and add an authentication check to the App. The client shows a Log In button if the user is not authenticated. Otherwise it shows a WeatherForecast component. Note, that I also added bootstrap just to make the UI more fancy.

    + + 
     // index.tsx
+ import "bootstrap/dist/css/bootstrap.min.css";
+ import { AuthProvider } from "react-oidc-context";
+
+ const root = ReactDOM.createRoot(
+ document.getElementById("root") as HTMLElement
+ );
+ root.render(
+ <React.StrictMode>
+     <AuthProvider
+     authority={"http://localhost:8080/realms/secure-app"}
+     client_id={"account"}
+     redirect_uri={window.location.origin}
+     scope="openid profile"
+     automaticSilentRenew={true}
+     loadUserInfo={true}
+     >
+     <App />
+     </AuthProvider>
+ </React.StrictMode>
+ );
+
+ // App.tsx
+ import WeatherForecastView from "./WeatherForecastView";
+ import { useAuth } from "react-oidc-context";
+
+ export default function App() {
+ const auth = useAuth();
+
+ return (
+     <div className="container mt-5 mb-5">
+         {auth.isAuthenticated ? (
+             <div className="App">
+             <WeatherForecastView />
+             </div>
+         ) : (
+             <div className="row">
+             <div className="col text-center">
+                 {auth.isLoading ? (
+                 <div className="spinner-border" role="status">
+                     <span className="visually-hidden">Loading...</span>
+                 </div>
+                 ) : auth.error ? (
+                 <div className="alert alert-danger" role="alert">
+                     Error
+                 </div>
+                 ) : (
+                 <button
+                     type="button"
+                     className="btn btn-primary"
+                     onClick={() => void auth.signinRedirect()}
+                 >
+                     Log in
+                 </button>
+                 )}
+             </div>
+             </div>
+         )}
+         </div>
+     );
+ }
+
    +
    3. +
  3. +

    The last thing to change in the client is to update useApi hook. The hook will handle the authentication and authorization aspects by including the user’s access token in the request headers.

    + + 
     // useApi.tsx
+ import { useAuth } from "react-oidc-context";
+ import { WeatherForecast } from "../types/WeatherForecast";
+
+ export function useApi() {
+ const auth = useAuth();
+ const get = (): Promise<WeatherForecast[]> => {
+     const url = "http://localhost:5000/weatherforecast";
+     const options = {
+     method: "GET",
+     headers: {
+         Authorization: `Bearer ${auth.user?.access_token}`,
+         "Content-Type": "application/json",
+     },
+     };
+     const request = async (): Promise<WeatherForecast[]> => {
+     const resp = await fetch(url, options);
+     const response = (await resp.json()) as WeatherForecast[];
+     return response;
+     };
+     return request();
+ };
+
+ return [get];
+ }
+
    +
    4. +
+ +

Change the server

+ +
    +
  1. +

    Install NuGet package Microsoft.AspNetCore.Authentication.JwtBearer. This library enables support for JWT bearer based authentication.

    + + 
     dotnet add package Microsoft.AspNetCore.Authentication.JwtBearer --version 7.0.9
+
    +
    2. +
  2. +

    Set up JWT Bearer authentication, configure the necessary options for token validation, and add authorization support to the application. The application will now be able to authenticate users using JWT tokens and enforce authorization rules based on the received tokens.

    + + 
     // other code 
+
+ services
+     .AddAuthentication(JwtBearerDefaults.AuthenticationScheme)
+     .AddJwtBearer(options =>
+     {
+         options.ConfigurationManager =
+             new ConfigurationManager<OpenIdConnectConfiguration>("http://keycloak:8080/realms/secure-app/.well-known/openid-configuration",
+                 new OpenIdConnectConfigurationRetriever(),
+                 new HttpDocumentRetriever(new HttpClient()) { RequireHttps = false });
+         options.RequireHttpsMetadata = false;
+         options.SaveToken = true;
+         options.Authority = "";
+         options.TokenValidationParameters = new TokenValidationParameters
+         {
+             ValidateIssuer = true,
+             ValidateAudience = true,
+             ValidateLifetime = true,
+             ValidIssuer = "http://localhost:8080/realms/secure-app",
+             ValidAudience = "account"
+         };
+     });
+ services.AddAuthorization();
+
+ // other code
+
    +
    3. +
  3. +

    Enable authentication and authorization support using UseAuthentication() and UseAuthorization() middlewares.

    + + 
     // other code 
+
+ app.UseAuthentication();
+ app.UseAuthorization();
+
+ // other code
+
    +
    4. +
  4. +

    Apply authorization to the /weatherforecast endpoint using RequireAuthorization() method. By using RequireAuthorization() at the endpoint, you are making sure that authorization is enforced for the endpoint.

    + + 
     app.MapGet("/weatherforecast", () => 
+ {
+     // some code
+ }).RequireAuthorization();
+
    +
    5. +
+ +

Run it all together

+ +

The secured application now can be launched. Execute docker compose up and go to localhost:5000.

+ +

content

+ +

content

+ +

Source code

+ +

The code can be found here.

+ +

Summary

+ +

This step-by-step guide demonstrates how to create a secure full-stack application with OAuth2 authentication using Keycloak. Of course the provided example is pretty basic and lacks a lot of things that should be done for a production-level application. For example, the following things can be improved:

+ +
    +
  1. Hardcoded parameters should be extracted into .env file for the client or to the environment variables for the server.
    2. +
  2. Automatic realm configuration export to avoid manual configuration of Keycloak instance.
    3. +
+
+
+ + + +
+
+ + diff --git a/2023/08/10/top-level-statements.html b/2023/08/10/top-level-statements.html new file mode 100644 index 0000000..6250db5 --- /dev/null +++ b/2023/08/10/top-level-statements.html @@ -0,0 +1,172 @@ + + + + + + + +How to call Program.Main method if you’re using top-level statements | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ How to call Program.Main method if you're using top-level statements +

+

+ + #csharp + +
+ +

Lately, I’ve been writing a lot of services that use top-level statements. The code with this kind of syntax sugar simplifies the Main method and improves readability. However, on the other hand, it makes it more difficult to create integration tests for the services. There isn’t simply way to call the Main method and pass the necessary arguments.

+ +

Since Microsoft introduced top-level statements in C# 9, you no longer need to write a weird Java-like public static void Main(string[] args) method. So the code

+ +
using System;
+
+namespace Application
+{
+    public class Program
+    {
+        public static void Main(string[] args)
+        {
+            Console.WriteLine($"args: {string.Join(",", args)}");
+        }
+    }
+}
+
+ +

can be written just in one line:

+ +
System.Console.WriteLine($"args: {string.Join(",", args)}");
+
+ +

But what if we want to call this code and pass some arguments? By default, the C# compiler will convert the code above to the following code:

+ +
internal class Program
+{
+    private static void <Main>$(string[] args)
+    {
+        Console.WriteLine(string.Concat("args: ", string.Join(",", args)));
+    }
+}
+
+ +

The class is internal, and the method is private. You cannot call the <Main> method directly, especially from another assembly. How can you overcome this?

+ +

First of all, you should declare the Program class as public partial. This makes the Program class visible in external code. If you can’t change the code, you can load the type from the assembly using Assembly.GetType.

+ +
System.Console.WriteLine($"args: {string.Join(",", args)}");
+
+public partial class Program { }
+
+ +

Now that the Program class has become visible from other assemblies, the <Main> method is still private. Using reflection, we can retrieve the <Main> method from the DeclaredMethods collection and call it using Invoke:

+ +
var typeInfo = typeof(Program) as TypeInfo ?? throw new InvalidOperationException();
+var main = typeInfo.DeclaredMethods.First(m => m.Name == "<Main>");
+var args = new[] { "arg1", "arg2" };
+main.Invoke(null, new object[] { args });
+
+ +

That’s it. Reflection provides a powerful mechanism which helps us with writing tests for programs that use top-level statements.

+
+
+ + + +
+
+ + diff --git a/2023/10/18/method-as-a-parameter.html b/2023/10/18/method-as-a-parameter.html new file mode 100644 index 0000000..23cf0ff --- /dev/null +++ b/2023/10/18/method-as-a-parameter.html @@ -0,0 +1,201 @@ + + + + + + + +Performance issues when using a method as a parameter in C#. Are they real? | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Performance issues when using a method as a parameter in C#. Are they real? +

+

+ + #csharp + + #benchmark + +
+ +

There is an article on Habr about performance issues when passing a method as a parameter in C#. The author showed that passing an instance method as a parameter inside for loops may degrade performance and increase memory consumption due to unnecessary object allocations in the heap. In this short article, I want to repeat the original benchmark and compare how things have changed since the .NET 7 release.

+ +

Benchmark

+ +

In the original article, the author compared only two possible declarations of a method: a predefined delegate and an instance method. In addition to that, I decided to check other ways of method declaration. So, the final list is as follows:

+ +
    +
  • predefined delegate;
    • +
  • lambda expression;
    • +
  • lambda expression that calls instance method;
    • +
  • lambda expression that calls static method;
    • +
  • static anonymous method;
    • +
  • static anonymous method that calls static method;
    • +
  • anonymous method;
    • +
  • anonymous method that calls instance method;
    • +
  • anonymous method that calls static method;
    • +
  • instance method;
    • +
  • static method.
    • +
+ +

For benchmarking, I used the BenchmarkDotNet library. The whole code of the benchmark class can be found here.

+ +

Results

+ +

The benchmark results are in the diagram below. As we can see, .NET 7 shows better results for everything related with static methods (except anonymous method + static method).

+ +

content +The benchmark results

+ +

To understand why this happens, we have to go deeper and look at IL code. Basically, all these possible ways of method calling can be divided into two groups:

+ +
    +
  1. Which causes creation of a new object in the for loop on each iteration.
    2. +
  2. Which doesn’t cause creation of a new object in a for loop on each iteration
    3. +
+ +

For example, let’s compare the code for calling a static method (4th row in the diagram):

+ +
for (int i = 0; i < n; i++) {
+    CallAdd(StaticAdd, i, i);
+}
+
+ +

In .NET 6, such code after compilation will look like:

+ +
for (int i = 0; i < 10000; i++) {
+    // A new Func instance created on each iteration.
+    CallAdd(new Func<int, int, int>(
+      (object) null, 
+      __methodptr(StaticAdd)
+    ), i, i);
+}
+
+ +

In .NET 7 the situation is different:

+ +
for (int i = 0; i < 10000; ++i) {
+    CallAdd(
+        // A new Func instance created only once
+        BenchmarkableClass.<>O.<1>__StaticAdd ?? (
+            BenchmarkableClass.<>O.<1>__StaticAdd = new Func<int, int, int>(
+                (object) null, 
+                __methodptr(StaticAdd)
+                )), i, i
+      );
+}
+
+ +

The compiler created a new hidden static class <>O with public field <1>__StaticAdd of type Func<int, int, int> and this field will be initialized only once at the first iteration. The same situation and for other cases with static method.

+ +

Suboptimal .NET 6 code that creates new objects on each iteration causes unnecessary heap allocations. Therefore, it also causes garbage collection, which also affects performance.

+ +

Conclusion

+ +

Answering to the question in the title of this article: yes, the performance issues are still real, but the .NET platform becomes better.

+ +

If you’re using .NET 6 for development, it’s better to predefine a delegate instance before for loop or use lambda expression with static method.

+ +

For .NET 7 there are more options. You still can use predefined delegate or lambda expressions with static methods. But you can also pass a static method directly or pass an anonymous static method.

+
+
+ + + +
+
+ + diff --git a/2023/10/28/substring.html b/2023/10/28/substring.html new file mode 100644 index 0000000..7eebbe0 --- /dev/null +++ b/2023/10/28/substring.html @@ -0,0 +1,319 @@ + + + + + + + +Fastest way to extract a substring in C# | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Fastest way to extract a substring in C# +

+

+ + #csharp + + #benchmark + +
+ +

Today, we’ll dive back into a microbenchmarking and a concise article about performance in C#. Our focus will be on strings and the most effective way for extracting a substring from the original string.

+ +

Benchmark

+ +

In this benchmark, we’ll consider the following ways of extracting substring:

+ +
    +
  1. Substring method.
    2. +
  2. Range operator.
    3. +
  3. Split method.
    4. +
  4. ReadOnlySpan<T> Struct.
    5. +
  5. Regex Class;
    6. +
  6. SkipWhile method.
    7. +
+ +

For benchmarking, I used the BenchmarkDotNet library. The whole code of the benchmark class can be found here.

+ +

Results

+ +

As usual, I run the benchmark on both .NET 6 and .NET 7 platforms. The results show minimal variation between the two.

+ +

Execution time

+ +

content +The benchmark results

+ +

We observe that ReadOnlySpan<T>, Substring and Range operator show fairly similar performance results. Split, Regex and SkipWhile are notably slower, being 2.5, 8.5 and 23.5 times respectively.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MethodMean, nsPercent
ReadOnlySpan<T>687.6100
Substring698.5102
Range710.5103
Split1696.3247
Regex5830.4848
SkipWhile16211.72358
+ +

If we’ll look at decompiled C# code, it becomes apparent that Range operator’s implementation is very similar to the implementation of Substring.

+ +
// Range Operator after decompiling
+string text = data[num];
+int num2 = text.IndexOf(_symbol);
+string text2 = text;
+int num3 = num2;
+list.Add(text2.Substring(num3, text2.Length - num3));
+num++;
+
+ +

The only difference is that Substring implementation has fewer local variables.

+ +
// Substring after decompiling
+string text = data[num];
+int startIndex = text.IndexOf(_symbol);
+list.Add(text.Substring(startIndex));
+num++;
+
+ +

ReadOnlySpan<T> shows better results. It looks like getting memory span and creating a new string from it is slightly faster, than getting substring by string.Substring method. I’m assuming that the reason of that is index bounds checks inside internal implementation of Substring method.

+ +
// ReadOnlySpan<T> after decompiling
+string obj = data[num];
+int start = obj.IndexOf(_symbol);
+ReadOnlySpan<char> value = MemoryExtensions.AsSpan(obj, start);
+list.Add(new string(value));
+num++;
+
+ +

Split is slower because its internal implementation and use of this method to obtain a substring is incorrect.

+ +
// Split after decompiling
+string text = data[num];
+list.Add(text.Split(':')[1]);
+num++;
+
+ +

Regex is a good option when you need to get a substring with a more complex pattern rather than a single char. But in this particular case it’s like breaking a butterfly on a wheel.

+ +
// Regex after decompiling
+string input = data[num];
+list.Add(Regex.Match(input, _pattern).Groups[1].Value);
+num++;
+
+ +

SkipWhile is super slow because:

+ +
    +
  1. It creates a new delegate Func<char, bool>.
    2. +
  2. Enumerable.SkipWhile calls this delegate for each char in the string.
    3. +
  3. Enumerable.ToArray converts IEnumerable<char> to char[].
    4. +
+ +
// SkipWhile after decompiling
+string source = data[num];
+list.Add(new string(
+    Enumerable.ToArray(
+        Enumerable.SkipWhile(
+            source,
+            new Func<char, bool>(<SkipWhile>b__5_0)))));
+num++;
+
+ +

Memory

+ +

Speaking about memory allocations, ReadOnlySpan<T>, Substring and Range shows the same results. Other implementations require more memory.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MethodGen0Gen1AllocatedPercent
ReadOnlySpan<T>0.39010.00574.79 KB100
Substring0.39010.00574.79 KB100
Range0.39010.00574.79 KB100
Split0.73620.01149.03 KB188
Regex1.91500.030523.5 KB490
SkipWhile2.28880.030528.23 KB589
+ +

Conclusion

+ +

The most efficient methods for extracting a substring in C# are ReadOnlySpan<T>, Substring and Range. I favor the Range operator due to its cleaner appearance compared to other implementations. However, it is worth noting that it is 1-3% slower than ReadOnlySpan<T> and Substring.

+
+
+ + + +
+
+ + diff --git a/2023/12/02/readonlyspan-vs-string.html b/2023/12/02/readonlyspan-vs-string.html new file mode 100644 index 0000000..30f769c --- /dev/null +++ b/2023/12/02/readonlyspan-vs-string.html @@ -0,0 +1,656 @@ + + + + + + + +What is ReadOnlySpan in C# and how fast is it comparing to strings? | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ What is ReadOnlySpan<T> in C# and how fast is it comparing to strings? +

+

+ + #csharp + + #benchmark + +
+ +

I already wrote an article about the fastest way of extracting substrings in C#. Now I want to investigate Span structures more. Recently, Microsoft released the .NET 8 platform which has several new extension methods for ReadOnlySpan<T>. So I want to compare the performance of MemoryExtensions methods with counterparts in string class.

+ +

Benchmark

+ +

All the benchmarks use a JSON-file with a string array of size 135 892 elements. Each array element represents the different permissions for different folders. Strings has the following template:

+ +
<permission> for Folder: \\server-name\path\to\folder\
+
+ +

For example:

+ +
DENY Permission for Folder: \\server-name\path\to\folder\
+
+ +

My employer won’t be happy if I share the file, so you should trust me that this file contains such data. :)

+ +

In this benchmark, we’ll consider the following 8 methods:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
stringReadOnlySpan<T>
ContainsContains
StartsWithStartsWith
IndexOfIndexOf
ReplaceReplace
SplitSplit
ToLowerInvariantToLowerInvariant
TrimTrim
SubstringSlice
+ +

In the context of Span-based methods, we also take into account the "Span + Allocation" scenario, which involves string allocation using the ToString method where applicable. This scenario arises when Span-based methods are initially used, but the ultimate outcome includes the allocation of a string through the ToString method.

+ +

As usual, for benchmarking, I used the BenchmarkDotNet library. The whole code of the benchmark class can be found here.

+ +

Results

+ +

Contains

+ +

The benchmark assesses whether each string in the provided collection contains the backslash character.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String615.7–01–
ReadOnlySpan<T>624.1-2%01–
+ +

StartsWith

+ +

The benchmark evaluates whether each string in the given collection starts with the specified substring.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String56 681.1–082–
ReadOnlySpan<T>329.6-99.4%00-100%
+ +

IndexOf

+ +

The benchmark retrieves the index of the substring "Folder" within each string in the given collection.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String224 182.4–0245–
ReadOnlySpan<T>1 131.6-99.5%01-99.6%
+ +

Split

+ +

This benchmark, designed as a synthetic test, utilizes the Split method to determine the maximum number of substrings between separators in each string within the provided collection.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String16 241.3–4468.7556 127 351–
ReadOnlySpan<T>9 977.2-39%01060-100%
+ +

Replace

+ +

These benchmarks focus on replacing backslashes with forward slashes in each string within the provided collection, using different methods for string manipulation.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String3 156.8–2019.5325 353 195–
ReadOnlySpan<T>2 663.0-16%03-100%
ReadOnlySpan<T> with allocation16 701.5+430%2015.6225 353 204-0%
+ +

ToLowerInvariant

+ +

These benchmarks focus on converting each string within the provided collection to lowercase, utilizing different methods for case transformation.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String3 771.3–2019.5325 353 195–
ReadOnlySpan<T>3 884.3+3%03-100%
ReadOnlySpan<T> with allocation17 938.6+374%2015.6225 353 204-0%
+ +

Trim

+ +

These benchmarks focus on trimming trailing backslashes from the specified substring in each string within the provided collection, utilizing various approaches to achieve the desired result.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String687.4––553–
ReadOnlySpan<T>469.1-32%––-99.8%
ReadOnlySpan<T> with allocation2 968.1+332%2019.5325 353 195+4 584 565%
+ +

Substring

+ +

These benchmarks focus on extracting a substring between specific markers in each string within the provided collection, using different methods to achieve the desired substring extraction.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkMean execution time, μsExecution time ratioGen 0 collections per 1000 operationsAllocated memory, bytesAllocated ratio
String1 523.8–779.39784225–
ReadOnlySpan<T>347.9-77%––-100%
ReadOnlySpan<T> with allocation1 694.5+11%779.39784225+0%
+ +

Conclusion

+ +

ReadOnlySpan<T> without allocation

+ +

content +ReadOnlySpan execution ratio

+ +

As we can see from the figure above, almost all extension methods for ReadOnlySpan<T> are faster than analogues from string class. The only exception is the ReadOnlySpan<T>.ToLower method. I assume that It is because this method copies the characters from the source span into the destination.

+ +

ReadOnlySpan<T> with allocation

+ +

content +ReadOnlySpan with allocation execution ratio

+ +

The string.Replace, string.ToLower, string.TrimEnd, string.Substring methods outperform the combination of ReadOnlySpan<T> methods and ReadOnlySpan<T>.ToString. This difference is likely due to efficient implementations of these methods. For example, invoking the string.TrimEnd leads to a call of the Buffer.Memmove method. It appears that the string allocation process using Buffer.Memmove is more efficient than the implementation of ReadOnlySpan<T>.ToString.

+ +

Memory consumption

+ +

Span-based methods exhibit superior memory efficiency, with zero memory allocations and no observed Gen 0 collections. String methods, particularly in operations like Split, Replace, and ToLower, tend to incur more significant memory allocations and, in some cases, Gen 0 collections. Therefore, for memory-conscious applications, utilizing Span-based methods may offer performance advantages in terms of reduced memory footprint and improved garbage collection behavior.

+ +

Notice almost the same or even worse results for the “Span + Allocation” column. Despite the utilization of Span-based methods in the intermediate steps, the inclusion of string allocation in the final outcome appears to negate some of the memory efficiency gained by using Span. This indicates that, in this specific context, the allocation of strings during or after Span-based operations may mitigate the potential memory benefits associated with using Span.

+ +

Generation 0 collections per 1000 operations

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CategoriesStringSpanSpan + Allocation
Contains00-
StartsWith00-
IndexOf00-
Split4468.750-
Replace2019.531302015.625
ToLower2019.531302015.625
Trim1333.333302019.5313
Substring779.30779.3
+ +

Memory allocated, Mb

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CategoriesStringSpanSpan + Allocation
Contains0.000.00-
StartsWith0.000.00-
IndexOf0.000.00-
Split53.530.00-
Replace24.180.0024.18
ToLower24.180.0024.18
Trim0.000.0024.17
Substring9.330.009.33
+ +

Further reading

+ +
    +
  1. MemoryExtensions Class.
    2. +
  2. Spanification.
    3. +
  3. Pro .NET Memory Management.
    4. +
+
+
+ + + +
+
+ + diff --git a/2024/01/08/whats-wrong-with-options-pattern.html b/2024/01/08/whats-wrong-with-options-pattern.html new file mode 100644 index 0000000..75662e2 --- /dev/null +++ b/2024/01/08/whats-wrong-with-options-pattern.html @@ -0,0 +1,287 @@ + + + + + + + +What’s wrong with the Options pattern in C#? | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ What's wrong with the Options pattern in C#? +

+

+ + #csharp + +
+ +

In .NET, there is a so-called Options pattern that simplifies the way we handle application configuration. To use it, developers just need to follow three steps. First, add the necessary configuration provider. Second, set up Service Provider using Configure extension method. Third, inject either IOptions<T>, or IOptionsMonitor<T>, or IOptionsSnapshot<T> into the target class through constructor injecting.

+ +

Microsoft provides default configuration providers, like the JSON configuration provider with the AddJsonFile extension method. However, there are no providers for obtaining the configuration from database. In this article, we will consider several approaches to how to do it.

+ +

The problem

+ +

Let’s imagine the implementation of the following application scenario. We have a Configuration Updater responsible for updating configuration in the database. On the other side, there’s a Configuration Consumer that retrieves configuration when needed.

+ +

content

+ +

Below is AppConfig class that defines configuration.

+ +
public class AppConfig
+{
+    public int Id { get; init; }
+    public int Version { get; private set; }
+    public required string Guid { get; set; }
+
+    public void Update(string guid)
+    {
+        Guid = guid;
+        Version++;
+    }
+}
+
+ +

The configuration in the database is updated by the Configuration Updater service every second.

+ +
var db = new DatabaseContext("DataSource=./../Database/db.sqlite");
+
+while (true)
+{
+    var guid = Guid.NewGuid().ToString();
+
+    var appConfig = db.AppConfigs.FirstOrDefault();
+
+    if (appConfig is null)
+    {
+        db.AppConfigs.Add(new AppConfig() { Guid = guid, });
+    }
+    else
+    {
+        appConfig.Update(guid);
+    }
+
+    db.SaveChanges();
+
+    Console.WriteLine("Configuration updated: {0}", guid);
+
+    await Task.Delay(1000);
+}
+
+ +

In the same time, the Configuration Consumer service reads the configuration every second.

+ +
public class ConsumerClass(IOptionsMonitor<AppConfig> _optionsMonitor)
+{
+    public async Task DoWork()
+    {
+        while (true)
+        {
+            var config = _optionsMonitor.CurrentValue;
+            Console.WriteLine("Consume config: {0}", config.Guid);
+            await Task.Delay(1000);
+        }
+    }
+}
+
+ +

Now we should implement the functionality that obtains configuration from the database. There are examples, that show how to achieve this using custom configuration providers, for instance:

+ +
    +
  1. Implement a custom configuration provider - .NET | Microsoft Learn
    2. +
  2. Implement a Custom Configuration Provider in .NET 7 | by Goker Akce | Medium
    3. +
  3. A Refreshable SQL Server Configuration Provider for .NET Core | Morteza Mousavi
    4. +
+ +

The main idea of all these examples is to implement 2 custom classes: configuration source and configuration provider. Let’s take a look at the possible implementation.

+ +

The DatabaseConfigurationProvider class retrieves the configuration from the database, converts it to a JSON string, and loads into the Data dictionary using the internal implementation of JsonConfigurationProvider.

+ +
public class DatabaseConfigurationProvider(string _connectionString, JsonConfigurationSource source) : JsonConfigurationProvider(source)
+{
+    public static string Prefix => nameof(AppConfig);
+
+    public override void Load()
+    {
+        using var db = new DatabaseContext(_connectionString);
+
+        var appConfig = db.AppConfigs.FirstOrDefault() ?? throw new InvalidOperationException("Configuration");
+
+        var json = JsonSerializer.Serialize(new { AppConfig = appConfig });
+
+        if (string.IsNullOrWhiteSpace(json)) return;
+
+        var bytes = Encoding.UTF8.GetBytes(json);
+
+        using var stream = new MemoryStream(bytes);
+
+        Load(stream);
+    }
+}
+
+ +

The DatabaseConfigurationSource class serves for building the provider. It should be added to the ConfigurationBuilder instance when defining dependencies in the composition root.

+ +
public class DatabaseConfigurationSource(string _connectionString) : JsonConfigurationSource
+{
+    public override IConfigurationProvider Build(IConfigurationBuilder builder)
+    {
+        Console.WriteLine("Build configuration source");
+        EnsureDefaults(builder);
+        return new DatabaseConfigurationProvider(_connectionString, this);
+    }
+}
+
+public static class DatabaseConfigurationExtensions
+{
+    public static IConfigurationBuilder AddDatabaseConfiguration(this IConfigurationBuilder builder, string connectionString) =>
+        builder.Add(new DatabaseConfigurationSource(connectionString));
+}
+
+ +

This solution compiles and works well, but only once. The reason is that the DatabaseConfigurationSource will read the configuration only on application startup. The examples we mentioned earlier propose a solution to this problem based on Timer or ChangeToken class. The idea is to periodically update configuration. However, there are several issues with this approach:

+ +
    +
  1. Unnecessary database calls: The configuration provider makes a call to the database even if the database state hasn’t changed.
    2. +
  2. Data consistency: The configuration in the database may be updated, but the timer is not triggered yet, and the configuration provider hasn’t obtained the updated version. Therefore, the Consumer Service may receive an outdated version of the configuration.
    3. +
+ +

Proposed solution

+ +

If the Consumer Service has to have the latest version of the configuration, a simpler solution is just to implement IOptionsMonitor<AppConfig> and register it as a singleton:

+ +
public class DatabaseConfigurationMonitor(string _connectionString) : IOptionsMonitor<AppConfig>
+{
+    public AppConfig CurrentValue => Get();
+
+    public AppConfig Get(string? name) => Get();
+
+    private AppConfig Get() =>
+        new DatabaseContext(_connectionString)
+            .AppConfigs
+            .FirstOrDefault() ?? throw new InvalidOperationException("Configuration");
+
+    public IDisposable? OnChange(Action<AppConfig, string?> listener)
+    {
+        throw new NotImplementedException();
+    }
+}
+
+public static class DatabaseConfigurationExtensions
+{
+    public static IServiceCollection ConfigureDatabaseOptionsMonitor(this IServiceCollection collection, string connectionString) =>
+        collection.AddSingleton<IOptionsMonitor<AppConfig>>(provider => new DatabaseConfigurationMonitor(connectionString));
+}
+
+ +

In this scenario, accessing _optionsMonitor.CurrentValue will consistently provide the most up-to-date configuration from the database.

+ +

But this approach also has a drawback: if you decide to switch from IOptionsMonitor<T> to IOptions<T>, you will also need to implement IOptions<T>.

+ +

Conclusion

+ +

In applications using the Options pattern, there’s a missing feature for fetching configuration directly from databases. Developers often need to create custom solutions, typically through custom classes based on the ConfigurationProvider class. However, this approach can lead to issues such as unnecessary database calls and data inconsistency.

+ +

For a straightforward solution that always fetches the latest configuration, implementing the IOptionsMonitor<T> or IOptions<T> interfaces proves to be simpler and more effective.

+
+
+ + + +
+
+ + diff --git a/2024/01/15/class-vs-struct.html b/2024/01/15/class-vs-struct.html new file mode 100644 index 0000000..01d797d --- /dev/null +++ b/2024/01/15/class-vs-struct.html @@ -0,0 +1,240 @@ + + + + + + + +The 16-Byte Rule: Unraveling the Performance Mystery of C# Structures | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ The 16-Byte Rule: Unraveling the Performance Mystery of C# Structures +

+

+ + #csharp + + #benchmark + +
+ +

Many C# developers are familiar with the following statements. By default, when passing to or returning from a method, an instance of a value type is copied, while an instance of a reference type is passed by reference. This has led to a belief that utilizing structures may degrade the overall app performance, especially if the structure has a size greater than 16 bytes. The discussion about this is still ongoing. In this article, we will try to find the truth.

+ +

Disclaimer

+ +

The information from this article is а true only under specific conditions. I admit that the benchmark may yield different results on a machine with a different CPU or using different structures and classes. Always verify your code on your specific hardware and don’t rely solely on only articles from the internet. :)

+ +

Benchmark

+ +

The benchmark is very straightforward. It contains structures and classes with sizes ranging 4 to 160 bytes, incrementing by 4 bytes (int value).

+ +
public record struct Struct04(int Param);
+
+// other structures
+
+public record struct Struct160(
+int Param1, int Param2, int Param3, int Param4, 
+int Param5, int Param6, int Param7, int Param8, 
+int Param9, int Param10, int Param11, int Param12, 
+int Param13, int Param14, int Param15, int Param16, 
+int Param17, int Param18, int Param19, int Param20, 
+int Param21, int Param22, int Param23, int Param24, 
+int Param25, int Param26, int Param27, int Param28,
+int Param29, int Param30, int Param31, int Param32, 
+int Param33, int Param34, int Param35, int Param36, 
+int Param37, int Param38, int Param39, int Param40);
+
+public record struct Class04(int Param);
+
+// other classes
+
+public record class Class160(
+int Param1, int Param2, int Param3, int Param4, 
+int Param5, int Param6, int Param7, int Param8, 
+int Param9, int Param10, int Param11, int Param12,
+int Param13, int Param14, int Param15, int Param16, 
+int Param17, int Param18, int Param19, int Param20, 
+int Param21, int Param22, int Param23, int Param24, 
+int Param25, int Param26, int Param27, int Param28,
+int Param29, int Param30, int Param31, int Param32, 
+int Param33, int Param34, int Param35, int Param36, 
+int Param37, int Param38, int Param39, int Param40);
+
+ +

For each structure and class, there is an appropriate method that creates an instance from a single int value.

+ +
// other methods
+
+public static Struct20 GetStruct20(int value) => new(value, value, value, value, value);
+
+// other methods
+
+public static Class20 GetClass20(int value) => new(value, value, value, value, value);
+
+// other methods
+
+ +

In the end, there are benchmark methods, each creating a list of structures or classes with a size of 1000.

+ +
public int Iterations { get; set; } = 1000;
+
+private static void Add<T>(List<T> list, T value) => list.Add(value);
+
+[Benchmark(Baseline = true)]
+public List<Struct04> GetStruct4()
+{
+    var list = new List<Struct04>(Iterations);
+    for (int i = 0; i < Iterations; i++) Add(list, GetStruct04(i));
+    return list;
+}
+
+// other benchmark methods
+
+[Benchmark(Baseline = true)]
+public List<Class04> GetClass4()
+{
+    var list = new List<Class04>(Iterations);
+    for (int i = 0; i < Iterations; i++) Add(list, GetClass04(i));
+    return list;
+}
+
+// other benchmark methods
+
+ +

As usual, for benchmarking, I used the BenchmarkDotNet library. The whole code of the benchmark class can be found here.

+ +

Results

+ +

Time measurements

+ +

The plot indicates that structures yield better results when their size is 64 bytes or fewer.

+ +

content

+ +

If we take a closer look at the execution time for entities up to 64 bytes, we see, that using structures can be 40-70% faster than using classes.

+ +

content

+ +

To understand why .NET behaves in this way, we need to examine the complied code. Let’s take a look at GetStruct64 and GetStruct128 methods, for example. At the screenshot below, we can observe that these methods are compiled to the same IL code. The only difference is in names.

+ +

content

+ +

It means that we have to delve deeper and examine the assembly code generated by the JIT compiler. Hopefully, BenchmarkDotNet provides such functionality.

+ +

Comparing both methods shows that there is no invocation of the GetStruct64(int value) method. Instead, multiple mov operations (mov operation moves data between registers and memory) are present. These operations actually initialize Struct64. But where is GetStruct64(int value)? The JIT compiler optimized the code and replaced the method invocation by its body. This optimization is known as function or method inlining.

+ +

content

+ +

Another interesting observation is that there are no stack allocations for structures. The JIT compiler optimized the code in such a way that only registers are used, even for structures of 128 bytes or more (thanks to AVX 256-bit registers).

+ +

Memory measurements

+ +

The memory usage graph displays a smooth pattern without sudden spikes. It indicates that the difference in memory consumption between structures and classes remains constant. Thus, as the size of the entity increases, the use of structures becomes less advantageous in relative terms, but still advantageous. For structures with a size less or equal to 64 bytes, memory usage less from 27% to 87%.

+ +

content

+ +

Conclusion

+ +

This article proved, that utilizing structures not always degrades application performance. JIT compiler is smart enough to optimize the code during runtime, so structs may behave better than classes. Using structures up to 64 bytes more efficient in terms of both execution time and memory consumption.

+
+
+ + + +
+
+ + diff --git a/2024/02/12/testcontainers.html b/2024/02/12/testcontainers.html new file mode 100644 index 0000000..064422d --- /dev/null +++ b/2024/02/12/testcontainers.html @@ -0,0 +1,311 @@ + + + + + + + +Simplify Integration Testing with Testcontainers | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Simplify Integration Testing with Testcontainers +

+

+ + #csharp + + #testing + +
+ +

Integration tests play an important role in software development. They help us see how the system works with volatile dependencies, such as databases. To run the integration test with the database, we need somewhere to create this database. It can be deployed on a virtual machine or even on a local host. However, it’s best to use Testcontainers framework.

+ +

What is Testcontainers?

+ +

Testcontainers is a library that provides easy and lightweight APIs for bootstrapping local development and test dependencies with real services wrapped in Docker containers. Using Testcontainers, you can write tests that depend on the same services you use in production without mocks or in-memory services (stolen from this page).

+ +

Usage example

+ +

Consider the UserInfoService class below. Basically, the service tries to get UserInfo from the memory cache. If this fails, the service tries to extract the data from the database. If this also fails, the service returns null.

+ +
public record UserInfo(string Id, string Name, string Address);
+
+public class UserInfoService(IRepository _repository)
+{
+    private readonly ConcurrentDictionary<string, UserInfo> _cache = new();
+
+    public async Task<UserInfo?> Get(string id, CancellationToken ct)
+    {
+        // The fastest way - get userinfo from memory cache
+        if (_cache.TryGetValue(id, out var userInfo)) return userInfo;
+
+        // Slower way - get userinfo from database
+        userInfo = await _repository.Get(id, ct);
+
+        if (userInfo is not null)
+        {
+            _cache.TryAdd(id, userInfo);
+            return userInfo;
+        }
+
+        return null;
+    }
+}
+
+ +

Now let’s consider the following scenario. If the key doesn’t exist in the cache, the service calls a database. Knowing that information, we can implement the integration tests with Testcontainers. But before that, we also need implementation of IRepository and migrations.

+ +

Repository implementation

+ +

We’re going to use PostgreSQL, so let’s add it to the project.

+ +
dotnet add package Npgsql --version 8.0.2
+
+ +

I also prefer to use Dapper to avoid the boilerplate code with SqlCommand.

+ +
dotnet add package Dapper --version 2.1.28
+
+ +

The implementation of the repository is pretty simple.

+ +
public interface IRepository
+{
+    public Task<UserInfo?> Get(string id, CancellationToken ct);
+
+    public Task Add(UserInfo userInfo);
+}
+
+public class Repository(string _connectionString) : IRepository
+{
+    private readonly NpgsqlConnection connection = new(_connectionString);
+
+    private readonly string _selectQuery = @"SELECT ""Id"", ""Name"", ""Address"" FROM ""Users"" WHERE ""Id"" = @Id";
+    private readonly string _insertQuery = @"INSERT INTO ""Users"" (""Id"", ""Name"", ""Address"") VALUES (@Id, @Name, @Address)";
+
+    public async Task Add(UserInfo userInfo)
+    {
+        await connection.ExecuteAsync(_insertQuery, new { userInfo.Id, userInfo.Name, userInfo.Address });
+    }
+
+    public async Task<UserInfo?> Get(string id, CancellationToken ct)
+    {
+        var row = await connection.QueryFirstOrDefaultAsync<UserInfo>(_selectQuery, new { Id = id });
+        return row;
+    }
+}
+
+ +

Database migrations

+ +

For migrations, we’re going to use FluentMigrator library.

+ +
dotnet add package FluentMigrator --version 5.0.0
+dotnet add package FluentMigrator.Runner.Postgres --version 5.0.0
+
+ +

The initial migration creates a Users table.

+ +
[Migration(20240212, "Creates Users table")]
+public class InitialMigration : Migration
+{
+    public override void Up()
+    {
+        Create.Table("Users")
+            .WithColumn("Id").AsString().PrimaryKey()
+            .WithColumn("Name").AsString()
+            .WithColumn("Address").AsString();
+    }
+
+    public override void Down()
+    {
+        Delete.Table("Users");
+    }
+}
+
+ +

To run the migration, we need to execute MigrateUp method.

+ +
public static class Migrator
+{
+    public static void MigrateUp(string connectionString)
+    {
+        var serviceProvider = new ServiceCollection()
+            // Add common FluentMigrator services
+            .AddFluentMigratorCore()
+            .ConfigureRunner(rb => rb
+                // Add SQLite support to FluentMigrator
+                .AddPostgres()
+                // Set the connection string
+                .WithGlobalConnectionString(connectionString)
+                // Define the assembly containing the migrations
+                .ScanIn(typeof(InitialMigration).Assembly).For.Migrations())
+            // Enable logging to console in the FluentMigrator way
+            .AddLogging(lb => lb.AddFluentMigratorConsole())
+            // Build the service provider
+            .BuildServiceProvider(false);
+
+        var migrationRunner = serviceProvider.GetRequiredService<IMigrationRunner>();
+        migrationRunner.MigrateUp();
+    }
+}
+
+ +

Integration tests

+ +

Now, we’re ready to write integration tests. Let’s create a new MSTest project.

+ +
dotnet new mstest
+
+ +

We use PostgreSQL, so it’s necessary to install the Testcontainers.PostgreSql nuget package.

+ +
dotnet add package Testcontainers.PostgreSql
+
+ +

Here is the integration test code:

+ +
[TestClass]
+public class IntegrationTests
+{
+    // Build a Docker image of PostgreSQL instance
+    private static readonly PostgreSqlContainer _postgres = new PostgreSqlBuilder()
+        .WithImage("postgres:16-alpine")
+        .Build();
+
+    // Run the container before testing
+    [ClassInitialize]
+    public static Task Init(TestContext _) => _postgres.StartAsync();
+
+    // Stop the container when all the tests within this class have completed
+    [ClassCleanup]
+    public static Task Cleanup() => _postgres.DisposeAsync().AsTask();
+
+    [TestMethod]
+    public async Task TestMethod()
+    {
+        // Arrange
+        var connectionString = _postgres.GetConnectionString();
+        Migrator.MigrateUp(connectionString);
+        var repository = new Repository(connectionString);
+        var expected = new UserInfo("1", "John Doe", "Some address");
+        await repository.Add(expected);
+        var sut = new UserInfoService(repository);
+
+        // Act
+        var actual = await sut.Get("1", CancellationToken.None);
+
+        // Assert
+        Assert.IsNotNull(actual);
+        Assert.AreEqual(expected.Id, actual.Id);
+        Assert.AreEqual(expected.Name, actual.Name);
+        Assert.AreEqual(expected.Address, actual.Address);
+    }
+}
+
+ +

Conclusion

+ +

That’s it. As we can see, the basic usage of Testcontainers is very easy.

+
+
+ + + +
+
+ + diff --git a/2024/04/12/collections-marshal.html b/2024/04/12/collections-marshal.html new file mode 100644 index 0000000..bd21399 --- /dev/null +++ b/2024/04/12/collections-marshal.html @@ -0,0 +1,304 @@ + + + + + + + +Ускоряем Dictionary в C# при помощи структур и CollectionsMarshal | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Ускоряем Dictionary в C# при помощи структур и CollectionsMarshal +

+

+ + #csharp + + #benchmark + + #hashtable + +
+ +

Если вы C# разработчик, то наверняка вам знаком класс Dictionary. В качестве значений вы, скорее всего, использовали классы. Но что если я скажу, что в Dictionary можно использовать структуры? Не стоит бояться того, что структуры копируются при передаче в метод или возврате из него. Есть способ этого избежать, и это работает быстро.

+ +

Дисклеймер

+ +

Информация в этой статье верна только при определённых условиях. Я допускаю, что бенчмарк может показать другие результаты на другом железе, с другим компилятором, во время другой фазы луны или при другом сценарии использования рассматриваемого функционала языка. Всегда проверяйте ваш код и не полагайтесь лишь на статьи из интернета.

+ +

Сценарий использования

+ +

Представим, что есть некоторый массив данных data. От нас требуется реализовать следующий функционал:

+ +
    +
  1. Преобразовать data в словарь для последующего поиска.
    2. +
  2. По некоторому ключу найти в словаре объект и изменить его.
    3. +
  3. Повторить п. 2 столько раз, сколько требуется.
    4. +
+ +

Напишем код, который имитирует такое поведение:

+ +
// Инициализация словаря  
+var dict = new Dictionary<int, SomeClass>(Length);
+
+// Заполнение словаря  
+foreach (var (i, obj) in data) {  
+    dict.Add(i, obj);  
+}
+
+// Поиск значения и изменение данных  
+for (int i = 0; i < Length; i++) {  
+    dict[i].DoWork(i);  
+}
+
+ +

Код выше работает как задумано. Давайте попробуем его ускорить. Заменим класс SomeClass на структуру SomeStruct и сравним производительность обоих вариантов.

+ +
// Инициализация словаря  
+var dict = new Dictionary<int, SomeClass>(Length);
+
+// Заполнение словаря  
+foreach (var (i, obj) in data) {  
+    dict.Add(i, obj);  
+}
+
+// Поиск значения и изменение данных  
+for (int i = 0; i < Length; i++) {  
+    var obj = dict[i];  
+    obj.DoWork(i);  
+    dict[i] = obj;  
+}
+
+ +

Бенчмарк

+ +

Замер производительности осуществлялся на массиве данных в 100 000 элементов. Размер классов (без заголовка) и структур менялся от 4 до 128 байт. Для замеров производительности я использовал библиотеку BenchmarkDotNet. Код бенчмарка и результаты можно найти в GitHub.

+ +

content +Среднее время выполнения бенчмарка в зависимости от размера сущности

+ +

Результаты бенчмарка показывают ухудшение производительности при использовании структур размером больше 20 байт. В реализации со структурами происходит их многократное копирование, а поиск в словаре осуществляется дважды. Это негативно сказывается на производительности. Давайте разобьем замеры кода на части, чтобы понять, что можно улучшить.

+ +

Инициализация словаря

+ +

Бенчмарк показал ожидаемые результаты. Время инициализации и размер словаря со структурами линейно возрастают с увеличением размера структур.

+ +

content + +Среднее время инициализации словаря в зависимости от размера сущности +

+ +

Связано это с тем, что массив entries в таком случае хранит непосредственно значения, а не ссылки. Соответственно, для хранения такого словаря нужно банально больше памяти.

+ +

Справедливости ради нужно отметить, что для классов CLR выделила памяти даже больше, просто это произошло ранее – во время инициализации массива data. Если замерять время, затраченное на инициализацию массива классов и структур, то результаты будут не в пользу классов. Но это выходит за рамки статьи.

+ +

Заполнение словаря

+ +

И снова ожидаемые результаты. Время копирования структур, происходящее при заполнении словаря, линейно зависит от размера структур. Хотя разница между структурами и классами практически не заметна вплоть до 20 байт.

+ +

content + +Среднее время заполнения словаря в зависимости от размера сущности +

+ +

Поиск значения и его изменение

+ +

И в третий раз результаты не в пользу структур.

+ +

content + +Среднее время поиска значения и его изменения в зависимости от размера сущности +

+ +

Связано это с тем, что поиск по ключу и копирование структур осуществляется дважды:

+ +
SomeStruct s = dict[i]; // 1-й поиск по ключу и копирование структуры  
+s.DoWork(i);  
+d[i] = s; // 2-й поиск по ключу и копирование структуры
+
+ +

Вот тут нам и поможет класс CollectionsMarshal.

+ +

Кто такой этот ваш CollectionsMarshal?

+ +

Если кратко, то это класс с четырьмя extension-методами:

+ +
    +
  1. AsSpan<T> – возвращает Span<T> для элементов List<T>.
    2. +
  2. GetValueRefOrAddDefault<TKey, TValue> – по ключу возвращает из словаря ссылку на элемент TValue, создавая default значение если элемента не существует.
    3. +
  3. GetValueRefOrNullRef<TKey, TValue> – по ключу возвращает из словаря ссылку на элемент TValue или ссылку на null, если элемента не существует.
    4. +
  4. SetCount<T> – устанавливает значение Count для List<T>.
    5. +
+ +

Нас интересуют только GetValueRefOrAddDefault и GetValueRefOrNullRef. При помощи этих методов можно извлечь значения из словаря по ссылке, что позволит избежать двойного поиска по ключу и двойного копирования структуры. Например, код выше можно переписать следующим образом:

+ +
ref SomeStruct s = ref CollectionsMarshal.GetValueRefOrNullRef(dict, i);  
+s.DoWork(i);
+
+ +

Ещё немного бенчмарков

+ +

Сделаем замеры реализации с GetValueRefOrNullRef и сравним с предыдущими результатами:

+ +

content + +Среднее время поиска значения и его изменения в зависимости от размера сущности +

+ +

Время выполнения кода с CollectionsMarshal даже быстрее, чем с классами. Чтобы компенсировать потери производительности при инициализации и заполнении словаря, количество операций поиска должно быть многократно больше, чем размер массива.

+ +

content + +Время выполнения бенчмарка. Графики разбиты по количеству операций поиска. +

+ +

Особенности CollectionsMarshal

+ +

Проверка на default и null

+ +

Как упоминалось ранее, методы GetValueRefOrAddDefault и GetValueRefOrNullRef возвращают ссылку на default структуру и ссылку на null.

+ +

Проверить, дефолтная ли структура, т.е. все поля имеют дефолтное значение, довольно просто – нужно проверить флаг exists:

+ +
ref var element = ref CollectionsMarshal.GetValueRefOrAddDefault(  
+    dict, key, out bool exist);
+
+if (exist) {  
+    // some code here  
+} 
+
+ +

Со ссылкой на null ситуация другая. Булевого флага нет, а при сравнении с null будет выброшено исключение NullReferenceException. Лучше воспользоваться методом Unsafe.IsNullRef<T>(ref T source).

+ +
ref var element = ref CollectionsMarshal.GetValueRefOrNullRef(dict, key);  
+if (Unsafe.IsNullRef<T>(ref element)) {  
+    // some code here  
+}
+
+ +

Изменение словаря после получения ссылки на структуру

+ +

В документации к методам GetValueRefOrAddDefault и GetValueRefOrNullRef прямым текстом указано, что нельзя изменять словарь после того, как была получена ссылка на структуру. Почему так делать не надо продемонстрировано на примере ниже. После изменения словаря, любые изменения структуры, полученной по ссылке, не повлияют на значение в словаре.

+ +
ref var element = ref CollectionsMarshal.GetValueRefOrNullRef(dict, key);
+
+Console.WriteLine($"ref element: {element.Item1}"); // 30  
+Console.WriteLine($"dict[key]: {dict[key].Item1}"); // 30
+
+element.Item1 = 50; // change #1
+
+Console.WriteLine($"ref element: {element.Item1}"); // 50  
+Console.WriteLine($"dict[key]: {dict[key].Item1}"); // 50
+
+dict.Add(100, new (100)); // add a new element  
+element.Item1 = 60; // change #2
+
+Console.WriteLine($"ref element: {element.Item1}"); // 60  
+Console.WriteLine($"dict[key]: {dict[key].Item1}"); // 50
+
+ +

Выводы

+ +

Структуры – недооценённые элементы C#, которые, при определённых условиях, способны ускорить ваше приложение. При использовании структур в качестве значений для Dictionary лучше воспользоваться классом CollectionsMarshal. Методы этого класса GetValueRefOrAddDefault и GetValueRefOrNullRef позволяют получать элементы словаря по ссылке. Это, в свою очередь, может положительно сказаться на производительности кода при относительно большом количестве операций поиска в словаре.

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+ + diff --git a/2024/08/22/frozen-dictionary.html b/2024/08/22/frozen-dictionary.html new file mode 100644 index 0000000..3715f9d --- /dev/null +++ b/2024/08/22/frozen-dictionary.html @@ -0,0 +1,453 @@ + + + + + + + +Заглядываем под капот FrozenDictionary: насколько он быстрее Dictionary и почему | alexeyfv + + + + + + + + + + + + + + + + +
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+ Заглядываем под капот FrozenDictionary: насколько он быстрее Dictionary и почему +

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+ + #csharp + + #benchmark + + #hashtable + + #algorithms + +
+ +

С релизом .NET 8 в арсенале C# разработчиков появилась новая коллекция – FrozenDictionary. Особенность этого словаря в том, что он неизменяемый, но при этом обеспечивает более быстрое чтение по сравнению с обычным Dictionary. Я неспроста разбил результаты на обложке по типам – используемые во FrozenDictinoary алгоритмы сильно зависят от типа ключа, размера словаря или даже, например, количества строковых ключей одинаковой длины. В этой статье подробно разберем, насколько FrozenDictionary быстрее и почему.

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content

+ +

English version is here.

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Оглавление

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Dictionary, ты один приходи. Мы тоже один придём

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Прежде чем начать битву сравнение с Dictionary, важно заметить, что FrozenDictionary<TKey, TValue> – это абстрактный класс с множеством реализаций. Точнее, их 18. Вместо объяснений, когда какая реализация используется, проще показать на схеме, поэтому смотрим рисунок 1.

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content +Рисунок 1 – Выбор реализации FrozenDictionary

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Пугаться не стоит, реализации можно разделить на 5 групп по принципу работы:

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    +
  1. В DefaultFrozenDictionary и ValueTypeDefaultComparerFrozenDictionary используется структура FrozenHashTable.
    2. +
  2. В Int32FrozenDictionary тоже используется FrozenHashTable, но нет расчёта хэш-кода, поскольку значение ключа и есть хэш-код.
    3. +
  3. LengthBucketsFrozenDictionary использует алгоритм, похожий на блочную сортировку.
    4. +
  4. Реализации OrdinalStringFrozenDictionary тоже используют FrozenHashTable, но в них особенный алгоритм расчёт хэш-кода.
    5. +
  5. SmallValueTypeComparableFrozenDictionary, SmallValueTypeDefaultComparerFrozenDictionary и SmallFrozenDictionary используют линейный поиск, так как размер словарей не превышает 10 элементов.
    6. +
+ +

Выбор подходящей реализации зависит от множества параметров и происходит в методе CreateFromDictionary статического класса FrozenDictionary. Теперь подробно рассмотрим каждую группу по отдельности, изучим их алгоритмы и сделаем замеры.

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Дисклеймер

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Результаты бенчмарков в этой статье очень условны и верны только при определённых условиях. Допускаю, что бенчмарк может показать другие результаты на другом ПК, с другим ЦП, с другим компилятором или при другом сценарии использования рассматриваемого функционала языка. Всегда проверяйте ваш код на конкретно вашем железе и не полагайтесь лишь на статьи из интернета.

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Исходный код бенчмарков и сырые данные результатов можно найти в этом репозитории.

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Группа 1. Словари по умолчанию

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Как уже было сказано ранее, в DefaultFrozenDictionary и ValueTypeDefaultComparerFrozenDictionary используется структура FrozenHashTable. Эта структура, как можно догадаться из названия, представляет собой реализацию хэш-таблицы. Чтобы лучше понять, чем алгоритм в FrozenHashTable отличается от Dictionary, кратко вспомним, как устроен поиск в Dictionary. Если вы это помните, то можете пропустить объяснение.

+ +

Предположим, у нас есть следующий словарь:

+ +
var dictionary = new Dictionary<Fruit, string>()  
+{  
+    [new("apple")] = "APPLE",  
+    [new("grape")] = "GRAPE",  
+    [new("lemon")] = "LEMON",  
+    [new("fig")] = "FIG",  
+    [new("lime")] = "LIME",  
+    [new("kiwi")] = "KIWI",  
+};
+
+public record Fruit(string Value);
+
+ +

Когда, например, мы ищем значение для ключа Fruit("fig"), в Dictionary происходит следующее (рисунок 2):

+ +
    +
  1. Вычисляется хэш код ключа hashCode.
    2. +
  2. Рассчитывается индекса бакета bucketIndex.
    3. +
  3. Если ключ в бакете равен искомому, то возвращается значение. Иначе переходим к следующему ключу с таким же хэш-кодом и повторяем п. 3.
    4. +
+ +

content +Рисунок 2 – Пример поиска в Dictionary

+ +

Алгоритм поиска

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Иммутабельность FrozenDictionary позволяет иначе реализовать работу с бакетами в FrozenHashTable. Поскольку количество пар ключ-значение не меняется, то можно:

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    +
  1. Подобрать количество бакетов так, что количество коллизий будет не более 5% от количества уникальных хэшей.
    2. +
  2. Разместить ключи и значения последовательно в массивах _keys и _values, вместо связного списка в Dictionary. Это сделает поиск более эффективным за счет более высокой локальности данных.
    3. +
+ +

При использовании FrozenDictionary поиск значения для ключа Fruit("fig") выглядел бы следующим образом (рисунок 3):

+ +
    +
  1. Вычисляется хэш код ключа hashCode.
    2. +
  2. Рассчитывается индекса бакета bucketIndex.
    3. +
  3. В массиве bucket получаем значения start и end, задающие границы в массиве HashCodes.
    4. +
  4. Итерируем массив HashCodes от start до end, в поисках искомого ключа и возвращаем значение при нахождении.
    5. +
+ +

content + +Рисунок 3 – Пример поиска в DefaultFrozenDictionary +

+ +

Бенчмарк

+ +

Результаты бенчмарков для DefaultFrozenDictionary и ValueTypeDefaultComparerFrozenDictionary на рисунках 4 и 5.

+ +

content + +Рисунок 4 – Скорость чтения из ValueTypeDefaultComparerFrozenDictionary по сравнению с Dictionary +

+ +

content + +Рисунок 5 – Скорость чтения из DefaultFrozenDictionary по сравнению с Dictionary +

+ +

Высокая скорость поиска в Dictionary по сравнению с ValueTypeDefaultComparerFrozenDictionary при размере словаря до 1000 элементов, вероятно, связана с агрессивным инлайном методов Dictionary. Почему граница именно в 1000 элементов я понять не смог, т.к. в исходном коде ничего об этом нет. Возможно это детали реализации JIT-компилятора. Если у вас есть идеи на этот счет, поделитесь в комментариях.

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В остальных случаях, FrozenDictionary быстрее на 31-32% для значимых типов и 17-18% для ссылочных типов.

+ +

Группа 2. Словарь для ключей типа Int32

+ +

Int32FrozenDictionary также использует FrozenHashTable. Особенность этой реализации в том, что если тип ключа – целое число, то его хэш равен его значению и коллизии в таком словаре не возможны в принципе. Нельзя, например, добавить 2 элемента с ключом 123 – будет выброшено исключение.

+ +
var dict = new Dictionary<int, int>();  
+dict.Add(123, 1);  
+dict.Add(123, 2); // System.ArgumentException: An item with the same key has already been added.
+
+ +

Алгоритм поиска

+ +

Такая особенность Int32FrozenDictionary позволяет при чтении пропустить расчёт хэша и использовать значение ключа напрямую. В итоге, поиск значения выглядит так (рисунок 6):

+ +
    +
  1. Индекса бакета bucketIndex рассчитывается сразу по значению ключа.
    2. +
  2. В массиве bucket получаем значения start и end, задающие границы в массиве HashCodes.
    3. +
  3. Итерируем массив HashCodes от start до end, в поисках искомого ключа и возвращаем значение при нахождении.
    4. +
+ +

content + +Рисунок 6 – Пример поиска в Int32FrozenDictionary +

+ +

Бенчмарк

+ +

Благодаря оптимизациям, чтение из Int32FrozenDictionary быстрее на 34-42% (рисунок 7).

+ +

content + +Рисунок 7 – Скорость чтения из Int32FrozenDictionary по сравнению с Dictionary +

+ +

Группа 3. Словарь с алгоритмом распределения строк по длине

+ +

При создании «замороженных» словарей со строковым ключом, FrozenDictionary в первую очередь попытается создать класс LengthBucketsFrozenDictionary. Этот класс оптимизирован для ситуаций, когда ключи имеют разную длину. Достигается это распределением ключей по бакетам: для каждой уникальной длины ключа создаётся бакет вместимостью MaxPerLength = 5 элементов. По сути, это реализация блочной сортировки. Чтобы стало понятнее, рассмотрим пример:

+ +
var dictionary = new Dictionary<Fruit, string>()  
+{  
+    ["apple"] = "APPLE",  
+    ["grape"] = "GRAPE",  
+    ["lemon"] = "LEMON",  
+    ["fig"] = "FIG",  
+    ["lime"] = "LIME",  
+    ["kiwi"] = "KIWI",  
+}  
+var frozenDictionary = dictionary.ToFrozenDictionary();
+
+ +

В словаре есть ключи длиной 3, 4 и 5 символов. Следовательно, их можно распределить в 3 бакета (рисунок 8):

+ +
    +
  1. Бакет для ключей длиной 3: fig.
    2. +
  2. Бакет для ключей длиной 4: lime и kiwi.
    3. +
  3. Бакет для ключей длиной 5: apple, grape и lemon.
    4. +
+ +

content + +Рисунок 8 – Распределение строк по бакетам на основе их длины +

+ +

Поскольку известна минимальная (3) и максимальная (5) длина ключей, нет смысла создавать 3 отдельных бакета. Можно всё хранить в одном массиве _lengthBuckets. В таком случае индекс рассчитывается так: (key.Length - minLength) * MaxPerLength.

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Алгоритм поиска

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Поиск осуществляется в 3 шага (рисунок 9):

+ +
    +
  1. Определяется бакет в массиве _lengthBuckets.
    2. +
  2. Линейным поиском в бакете определяется индекс искомого ключа в _keys.
    3. +
  3. Возвращается значение.
    4. +
+ +

content + +Рисунок 9 – Пример поиска в LengthBucketsFrozenDictionary +

+ +

У LengthBucketsFrozenDictionary есть 2 ограничения:

+ +
    +
  1. Количество ключей с одинаковой длиной не должно превышать MaxPerLength (принцип Дирихле). Нельзя разместить 6 строк с одинаковой длиной в бакет вместимостью 5 элементов.
    2. +
  2. Количество пустых бакетов должно быть < 20%. Иначе реализация становится неэффективна с точки зрения использования памяти.
    3. +
+ +

Если одно из этих условий не выполняется, то будет выбрана одна из реализаций OrdinalStringFrozenDictionary (о ней далее).

+ +

Бенчмарк

+ +

Результаты бенчмарка показывают, что чтение из LengthBucketsFrozenDictionary может быть до 99% быстрее обычного Dictionary. Но если в словаре количество ключей с одинаковой длиной достигает 5, то производительность небольших словарей (до 100 элементов) может быть хуже (рисунок 10).

+ +

content + +Рисунок 10 – Скорость чтения из LengthBucketsFrozenDictionary по сравнению с Dictionary +

+ +

Группа 4. Словари с ключом типа string

+ +

Как мы уже знаем, у LengthBucketsFrozenDictionary есть ограничения. Поэтому, если невозможно распределить ключи по бакетам, используется одна из 11 реализаций абстрактного класса OrdinalStringFrozenDictionary. Все они используют FrozenHashTable, но отличаются алгоритмом расчёта хэш-кода строки.

+ +

Выбор оптимальной реализации OrdinalStringFrozenDictionary зависит от результата анализа ключей классом KeyAnalyzer. На результат влияет длина ключей, наличие не-ASCII символов, заданные правила сравнения строк (StringComparison) и наличие в ключах уникальных подстрок.

+ +

Очевидно, что чем длиннее строка, тем медленнее выполняется расчёт хэш-кода. Поэтому KeyAnalyzer пытается найти подстроки наименьшей длины, позволяющие однозначно идентифицировать ключ. Для лучшего понимания снова рассмотрим пример с фруктами: apple, grape, fig, lime, lemon и kiwi.

+ +

Сперва KeyAnalyzer анализирует подстроки длиной в 1 символ при левостороннем выравнивании ключей (рисунок 11).

+ +

content + +Рисунок 11 – Подстроки длиной в 1 символ при левостороннем и правостороннем выравнивании ключей +

+ +

В нашем примере при левостороннем выравнивании есть повторяющиеся подстроки. Например, 0-й символ lime и lemon, 1-й символ fig и lime и 2-й символ в lime и lemon. То есть невозможно при таком выравнивании однозначно идентифицировать ключ по одному символу. Поэтому поиск подстроки продолжается при правостороннем выравнивании. В этом случае подстроки будут уникальны при использовании 2-го или 1-го символа с конца. Зная выравнивание, индекс начала и длину подстроки можно однозначно идентифицировать строку рассчитав хэш-код её подстроки.

+ +

Если уникальных подстрок длиной в 1 символ нет, то поиск продолжится для подстрок в 2 символа, 3 символа, вплоть до максимальной длины подстроки. Это значение рассчитывается как минимальное между minLength (минимальная длина ключа) и MaxSubstringLengthLimit = 8. Такое ограничение сделано специально, чтобы не анализировать слишком длинные подстроки, так как их использование не даёт прироста в производительности.

+ +

Если уникальных подстрок нет вообще, то расчёт хэш-кода будет производиться для всей строки.

+ +

Помимо наличия уникальных подстрок на реализацию также влияют заданные параметры сравнения строк (StringComparison) и наличие не-ASCII символов. В зависимости от этих параметров будет выбран более оптимальный компаратор.

+ +

Алгоритм поиска

+ +

Поиск в словарях, основанных на OrdinalStringFrozenDictionary, происходит следующим образом:

+ +
    +
  1. Проверяется, находится ли длина ключа в пределах допустимого диапазона. Это нужно для быстрого определения ключей, которые явно не подходят по длине.
    2. +
  2. Далее, выполняются те же шаги, что мы ранее видели в других словарях с FrozenHashTable. Рассчитывается хэш-код подстроки и осуществляется поиск в хэш-таблице. В случае коллизии осуществляется линейный поиск.
    3. +
+ +

Бенчмарк

+ +

По результатам бенчмарка, FrozenDictionary размером до 75 тыс. элементов быстрее обычного Dictionary. Однако при дальнейшем увеличении размера словаря скорость поиска снижается (рисунок 12).

+ +

content + +Рисунок 12 – Скорость чтения из OrdinalStringFrozenDictionary_LeftJustifiedSubstring по сравнению с Dictionary +

+ +

Высокая скорость FrozenDictionary обусловлена быстрым расчётом хэш-кода ключей. Алгоритм, используемый в FrozenDictionary, на 75% – 90% быстрее алгоритма обычного Dictionary (рисунок 13).

+ +

content + +Рисунок 13 – Сравнение скорости расчёта хэша +

+ +

Падение производительности в словарях размером 75 тыс. элементов и более вызвано возрастающим количеством коллизий хэша при увеличении размера словаря (рисунок 14).

+ +

content + +Рисунок 14 – Количество коллизий при расчёте хэшей +

+ +

Как видно из графиков, алгоритм, используемый в FrozenDictionary, позволяет ускорить расчёт хэш-кода строки, улучшая производительность до 70%. Однако такой подход негативно сказывается на производительности поиска в относительно больших словарях.

+ +

Группа 5. Небольшие словари

+ +

SmallValueTypeComparableFrozenDictionary, SmallValueTypeDefaultComparerFrozenDictionary используются, когда в исходном словаре не более 10 элементов, а SmallFrozenDictionary – не более 4-х элементов. При этом, SmallValueTypeComparableFrozenDictionary применяется, если тип ключа – это встроенный примитивный значимый тип (int, long, double, enum и т.д.). Если же тип ключа, к примеру, некоторая кастомная структура, то будет использован тип SmallValueTypeDefaultComparerFrozenDictionary. Такое разделение разработчики .NET объясняют тем, что у встроенных типов 100% реализован интерфейс IComparable и поэтому можно немного оптимизировать поиск, предварительно отсортировав массивы ключей и значений:

+ +

Алгоритм поиска

+ +

Строго говоря, классы SmallValueTypeComparableFrozenDictionary, SmallValueTypeDefaultComparerFrozenDictionary и SmallFrozenDictionary – это не хэш-таблицы. Поиск значения в них осуществляется простым линейным поиском через for (рисунок 15).

+ +

content + +Рисунок 15 – Пример поиска в SmallValueTypeComparableFrozenDictionary +

+ +

В SmallValueTypeComparableFrozenDictionary, поскольку массивы _keys и _value отсортированы, можно осуществлять поиск пока искомое значение ключа больше текущего значения _keys[i].

+ +

Реализации SmallValueTypeDefaultComparerFrozenDictionary и SmallFrozenDictionary похожи на предыдущую, с тем лишь отличием, что в них не используется сортировка. Соответственно, линейный поиск по массиву ключей _keys будет осуществлён всегда.

+ +

Бенчмарк

+ +

Несмотря на все оптимизации в этих классах, результаты бенчмарков не выглядят впечатляющими (рисунок 16). Даже то небольшое ускорение, которое могут дать эти классы, составляет всего лишь несколько десятков наносекунд. +content + +Рисунок 16 – Скорость чтения из SmallValueTypeComparableFrozenDictionary, SmallValueTypeDefaultComparerFrozenDictionary и SmallFrozenDictionary по сравнению с Dictionary +

+ +

Резюме

+ +

В этой статье я постарался разобрать основные моменты, связанные с FrozenDictionary. Мы убедились, что FrozenDictionary в большинстве случае действительно быстрее Dictionary.

+ +

На самом деле, в FrozenDictionary применяется ещё множество алгоритмов и оптимизаций. Например, использование пула массивов ArrayPool, оптимизированный алгоритм расчёта остатка от деления, использование массива целых чисел с битовыми сдвигами, вместо массива bool и т.д. Разбор всех деталей не получилось бы сделать в рамках одной статьи. Но я периодически делюсь подобными техническими тонкостями в коротких постах на своём Telegram-канале. Если вам интересно, буду рад видеть вас среди читателей.

+
+
+ + + +
+
+ + diff --git a/2024/08/29/fast-mod-algorithm.html b/2024/08/29/fast-mod-algorithm.html new file mode 100644 index 0000000..c09f4cd --- /dev/null +++ b/2024/08/29/fast-mod-algorithm.html @@ -0,0 +1,150 @@ + + + + + + + +Быстрый расчёт остатка от деления | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Быстрый расчёт остатка от деления +

+

+ + #csharp + + #benchmark + + #hashtable + + #algorithms + +
+ +

В процессе работы над статьёй про FrozenDictionary заметил немало интересных деталей, о которых хочется рассказать. Начнём с простого, поэтому сегодня о быстром алгоритме расчёта остатка от деления.

+ +

Как известно, индекс бакета в словаре рассчитывается как остаток от деления hashCode на bucketsCount. В C# и многих других языках программирования для этого используется оператор %:

+ +
var bucketNum = hashCode % bucketsCount;
+
+ +

В общем случае, эта операция включает в себя деление, которое выполняется медленнее, чем другие арифметические операции. Поэтому была придумана следующая формула:

+ +
uint FastModLemier(uint hashcode, ulong multiplier, uint bucketsNum) => 
+    (uint)((((UInt128)(hashcode * multiplier)) * bucketsNum) >> 64);
+
+ +

Идея этого подхода в том, что если количество бакетов известно заранее, то можно вычислить значение multiplier по формуле ulong.MaxValue / bucketsNum + 1. Это позволяет заменить деление на умножение и битовые сдвиги. Если интересны подробности и математическое обоснование формулы, то можете ознакомиться со статьёй Дэниела Лемира, разработчика данного метода. Для общего понимания достаточно прочитать главы 1 и 3.2.

+ +

В FrozenDictionary и Dictionary используется другой вариант формулы, который, по словам разработчиков Microsoft немного быстрее, что подтверждается результатами бенчмарка (рисунок 1):

+ +
uint FastModDotNet(uint hashcode, ulong multiplier, uint bucketsNum) => 
+        (uint)(((((multiplier * hashcode) >> 32) + 1) * bucketsNum) >> 32);
+
+ +

Кроме того, JIT-компилятор способен оптимизировать код и заменить деление на несколько операций умножения и битовый сдвиг, если значение bucketsCount известно на этапе компиляции. В результате ASM-код для DefaultModConst будет идентичен коду FastModDotNet (рисунок 2).

+ +

Код бенчмарка и результаты тут.

+
+
+ + + +
+
+ + diff --git a/2024/09/05/create-array.html b/2024/09/05/create-array.html new file mode 100644 index 0000000..53f1776 --- /dev/null +++ b/2024/09/05/create-array.html @@ -0,0 +1,179 @@ + + + + + + + +Создание массивов в C#: способы и производительность | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Создание массивов в C#: способы и производительность +

+

+ + #csharp + + #benchmark + + #array + +
+ +

Как создать массив в C#? Вопрос кажется простым. Но с каждой новой версией .NET появляются всё новые и новые фичи. На данный момент я знаю аж 7 способов создания массивов.

+ +
    +
  1. new T[]. Самый очевидный, простой и популярный способ создания объектов и массивов в частности. Этот оператор используется повсеместно и является основным для большинства сценариев.
    2. +
+ +

content

+ +
    +
  1. stackalloc T[]. Выделяет память в стеке для массива заданного размера и возвращает указатель. Когда-то давно оператор stackalloc требовал использования небезопасного (unsafe) контекста. С появлением Span<T> его можно применять и без unsafe. Этот способ ограничен размером доступной памяти в стеке и при необдуманном использовании может привести к StackOverflowException.
    2. +
+ +

content

+ +

Для небольших массивов stackalloc T[] быстрее new T[] в среднем на 20% и на 88% для больших массивов. Под небольшими массивами я подразумеваю массивы размером меньше 85 000 байт, т.е. размещаемые в куче малых объектов (SOH), а под большими – в куче больших объектов (LOH).

+ +
    +
  1. ArrayPool<T>. ArrayPool выделяет массив из пула, что минимизирует количество аллокаций памяти и уменьшает нагрузку на сборщик мусора. Массивы, возвращаемые из пула, могут быть больше запрошенного размера, например, для массива из 700 элементов будет возвращён массив длиной 1024. Использование пула полезно, для относительно больших временных массивов, которые часто создаются и уничтожаются.
    2. +
+ +

ArrayPool<T> быстрее new T[] в среднем на 30% для небольших массивов и на 89% для больших массивов. Однако для массивов размером менее 300–400 байт использование пула может быть медленнее.

+ +

content

+ +
    +
  1. GC.AllocateUninitializedArray<T>. Этот метод создаёт массив без инициализации его элементов значениями по умолчанию. Использование этого метода может быть небезопасным, так как массив содержит неинициализированные данные. Обязательно перезаписывайте все значения в созданном массиве.
    2. +
+ +

GC.AllocateUninitializedArray<T> быстрее new T[] в среднем на 15% для массивов 2048 – 85 000 байт. В остальных случаях производительность с new T[] одинаковая.

+ +

content

+ +
    +
  1. Array.Initialize. Позволяет создавать массивы, задавая тип во время выполнения. Например, когда тип неизвестен на этапе компиляции. Полезно для сценариев, связанных с динамической типизацией.
    2. +
+ +

Производительность аналогична new T[], либо хуже.

+ +

content

+ +
    +
  1. InlineArrayAttribute. Относительно новая фича, появившаяся в C# 12. Позволяет объявить структуру с атрибутом InlineArray, которая затем будет вести себя как массив.
    2. +
+ +

Быстрее new T[] в среднем на 39% для небольших массивов и на 91% для больших массивов.

+ +

content

+ +
    +
  1. collections expressions. Синтаксический сахар, который позволяет записать массив в виде литерала. Например, int[] array = [1, 2, 3] создаёт массив в куче, аналогично использованию new T[]. А конструкция Span<int> array = [1, 2, 3] создаёт массив в стеке, аналогично stackalloc.
    2. +
+ +

content

+ +

Код бенчмарка и результаты тут.

+
+
+ + + +
+
+ + diff --git a/2024/09/07/frozen-dictionary-collisions-count.html b/2024/09/07/frozen-dictionary-collisions-count.html new file mode 100644 index 0000000..ff09737 --- /dev/null +++ b/2024/09/07/frozen-dictionary-collisions-count.html @@ -0,0 +1,183 @@ + + + + + + + +Как в FrozenDictionary подсчитываются коллизии хэша | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Как в FrozenDictionary подсчитываются коллизии хэша +

+

+ + #csharp + + #benchmark + + #hashtable + + #algorithms + +
+ +

Второй короткий пост о деталях реализации FrozenDictionary, которые остались за кадром. Сегодня об алгоритме подсчёта количества коллизий хэша.

+ +

В FrozenDictionary хэш-коды всех ключей известны заранее. Это позволяет выбрать такое количество бакетов, при котором число коллизий не превышает допустимый порог — на данный момент это 5%. Делается это следующим образом:

+ +
    +
  1. Выбирается количество бакетов n из определённого диапазона. Этот диапазон подобран разработчиками .NET эвристическим способом.
    2. +
  2. Для каждого хэш-кода рассчитывается остаток от деления на n и считается количество одинаковых результатов.
    3. +
  3. Если количество коллизий слишком велико, то n увеличивается.
    4. +
+ +

Вот пример с bool[]:

+ +
int[] hashCodes = [/*some hash codes*/];  
+var seenBuckets = new bool[bucketsNumber];  
+var collisionsCount = 0;
+
+foreach (var hash in hashCodes) {  
+    var bucketIndex = hash % bucketsCount;  
+    if (seenBuckets[bucketIndex]) {  
+        collisionsCount++;  
+    }  
+    else {  
+        seenBuckets[bucketIndex] = true;  
+    }  
+}
+
+ +

Но в .NET сделали иначе и вместо bool[] используют int[] с битовыми сдвигами.

+ +
int[] hashCodes = [/*some hash codes*/];  
+var seenBuckets = new int[bucketsNumber];  
+var collisionsCount = 0;
+
+foreach (var hash in hashCodes) {  
+    var bucketIndex = hash % bucketsCount;  
+    var i = bucketIndex / 32;  
+    var bit = 1 << bucketIndex;  
+    if ((seenBuckets[i] & bit) != 0) {  
+        collisionsCount++;  
+    }  
+    else {  
+        seenBuckets[i] |= bit;  
+    }  
+}
+
+ +

Такой подход позволяет значительно уменьшить размер массива (20% – 80%) и время его создания уменьшается в среднем на 70%. В то же время, скорость чтения и записи снижается в среднем на 50% и 198% соответственно (см. графики).

+ +

content

+ +

content

+ +

content

+ +

content

+ +

Получается, использование целых чисел с битовым сдвигом – компромиссное решение. Команда .NET видимо посчитала, что аллоцировать меньше памяти важнее, чем скорость алгоритма.

+
+
+ + + +
+
+ + diff --git a/2024/09/16/bad-vs-better-benchmark.html b/2024/09/16/bad-vs-better-benchmark.html new file mode 100644 index 0000000..3405100 --- /dev/null +++ b/2024/09/16/bad-vs-better-benchmark.html @@ -0,0 +1,175 @@ + + + + + + + +Как ошибки в бенчмарке могут привести к неправильным выводам | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Как ошибки в бенчмарке могут привести к неправильным выводам +

+

+ + #csharp + + #benchmark + +
+ +

content

+ +

Видел я однажды пост в LinkedIn, заголовок которого утверждал, что .NET 9 медленнее, чем .NET 8. Сильное заявление. Проверять я его конечно буду. Ведь я сам большой любитель замеров производительности. Перейдём сразу к тому, что не так с бенчмарком.

+ + + +

❌ Методы не возвращают результат

+ +

Современные компиляторы умные. Они могут понять, когда выполняемый код не влияет на результат программы, и просто удаляют его. Такая оптимизация называется dead-code elimination (DCE). В данном случае, метод DoSomeThing фактически не делает ничего, поэтому компилятор удаляет его в методе For. Но в методе ForEach_Linq такое сделать нельзя, т.к. в нём создаётся делегат. В результате получается сравнение методов с разным поведением (рисунок 2).

+ +

✅ Всегда возвращайте результат из методов

+ +

Исправленный вариант может выглядеть так. Нам неважно, что будет в переменной sum. Главное, что её использование предотвращает DCE и не оказывает значительного влияния на результаты бенчмарка.

+ +

content

+ +

❌ Слепое сравнивание foreach

+ +

Как мы знаем, foreach предназначен для итерации по коллекциям, которые возвращают Enumerator. Но даже если коллекция возвращает Enumerator, это не гарантирует, что он будет использоваться. Рассмотрим пример на рисунке 3:

+ +
    +
  • foreach с массивом будет преобразован в while c индексаторов;
    • +
  • foreach со списком – в while с List<int>.Enumerator;
    • +
  • foreach с ICollection<int> – в while с IEnumerator<int>.
    • +
+ +

✅ Сравнивать производительность foreach нужно с разными типами

+ +

Поскольку foreach компилируется в различный код в зависимости от коллекции, производительность может значительно отличаться. Поэтому важно сравнивать каждый тип отдельно. Не исключаю, что эта деталь реализации может измениться в будущем.

+ +

content

+ +

❌ Передача метода в виде параметра

+ +

При передаче метода в виде параметра создаётся делегат. В нашем примере – Action<int> (рисунок 4). Делегат, как известно ссылочный тип, то есть его создание – аллокация памяти, которая влияет на результаты бечмарка.

+ +

✅ При передаче метода в виде параметра нужно заранее создать экземпляр делегата

+ +

Правильнее было бы проинициализировать делегат в методе GlobalSetup, который выполняется перед бенчмарками.

+ +

content

+ +

Выводы

+ +

Теперь посмотрим на результаты исправленного бенчмарка. Bad – изначальный бенчмарк, Better – после исправления.

+ +

content

+ +

В бенчмарке Bad, который я запустил на своём ноутбуке, метод ForEach_LinqParallel отработал на .NET 9 чуть быстрее, чем на .NET 8. То есть та разница в, о которой говорил автор, была в рамках погрешности.

+ +

В бенчмарке Better, метод ForEach_LinqParallel отработал на .NET 9 медленнее чем на .NET 8 на 90 мкс. Такую разницу я бы тоже отнёс к погрешности и не стал акцентировать внимание.

+ +

В целом, результаты во всех трёх версиях .NET кажутся плюс-минус одинаковыми. Поэтому, я бы не стал заявлять о том, что .NET 9 медленнее .NET 8.

+
+
+ + + +
+
+ + diff --git a/2024/09/30/how-data-affects-performance.html b/2024/09/30/how-data-affects-performance.html new file mode 100644 index 0000000..cbe7600 --- /dev/null +++ b/2024/09/30/how-data-affects-performance.html @@ -0,0 +1,155 @@ + + + + + + + +Как данные влияют на производительность | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ Как данные влияют на производительность +

+

+ + #csharp + + #benchmark + +
+ +

Хочу рассказать о том, насколько важно способ инициализации данных для достижения максимальной производительности кода.

+ +

Рассмотрим следующий синтетический пример.

+ +

content

+ +
    +
  • Массивы t1 и t2 содержат информацию о транзакциях.
    • +
  • Два метода, Setup1 и Setup2, инициализируют массивы t1 и t2 соответственно: Setup1 – двумя циклами for, а Setup2 – одним.
    • +
  • Метод Sum позволяет вычислить сумму транзакций в массивах t1 или t2.
    • +
+ +

Вопрос: при каком способе инициализации расчёт суммы в массивах Sum(t1) + Sum(t2) выполнится быстрее?

+ + + +

Правильный ответ: Sum(t1) + Sum(t2) рассчитывается быстрее, если массивы инициализировать двумя циклами for.

+ +

content

+ +

Почему так? Всё дело в кэше данных процессора. Я уже писал пост на эту тему, но тогда рассматривал два разных алгоритма для обхода одной и той же матрицы. В данном случае алгоритм не меняется, но данные инициализируются по-разному.

+ +

Если массивы t1 и t2 инициализировать одним циклом for, то в памяти это выглядит примерно так:

+ +
t1[0], t2[0], t1[1], t2[1], t1[2], t2[3], ...
+
+ +

При расчёте, например, Sum(t1), в самом начале происходит обращение к элементу t1[0]. Копирование данных из памяти в кэш происходит блоками по 64 байта — это размер линии кэша большинства современных процессоров. Из-за этого в кэш попадают элементы массива t2, хотя методу Sum нужны элементы только одного массива. В результате увеличивается количество промахов кэша.

+ +

Если массивы t1 и t2 инициализировать двумя циклами for, то локальность данных более высокая, что снижает количество промахов кэша:

+ +
t1[0], t1[1], t1[2], t1[3], ... t2[0], t2[1], t2[2], t2[3], ...
+
+
+
+ + + +
+
+ + diff --git a/2024/10/09/frozen-dictionary.html b/2024/10/09/frozen-dictionary.html new file mode 100644 index 0000000..c6a040d --- /dev/null +++ b/2024/10/09/frozen-dictionary.html @@ -0,0 +1,462 @@ + + + + + + + +FrozenDictionary under the hood: how fast is it comparing to Dictionary and why | alexeyfv + + + + + + + + + + + + + + + + +
+ +
+
+
+
+
+ + + + + + +
+

+ FrozenDictionary under the hood: how fast is it comparing to Dictionary and why +

+

+ + #csharp + + #benchmark + + #hashtable + + #algorithms + +
+ +

With .NET 8 release, C# developers received a new type of generic collections – FrozenDictionary. The main feature of this dictionary is that it’s immutable, but allows reading the data faster comparing to a plain Dictionary. I split the results on the cover by a reason: the algorithms used in FrozenDictionary are highly depended on key type, the size of the array or even the number of the string keys with the same length. In this article, we’ll look into details how fast is FrozenDictionary and why.

+ +

content

+ +

Версия на русском тут.

+ + + +

Table of Contents

+ + + +

Before we get started

+ +

It’s important to notice, that FrozenDictionary<TKey, TValue> is an abstract class with multiple derived classes. To be precise, there are 18 classes. Instead of explaining which implementation is used when, just look at the diagram in Figure 1.

+ +

content + +Figure 1 – Choosing a FrozenDictionary implementation +

+ +

Don’t be afraid, because all these 18 implementations can be combined into 5 groups:

+ +
    +
  1. In DefaultFrozenDictionary and ValueTypeDefaultComparerFrozenDictionary a FrozenHashTable is used.
    2. +
  2. In Int32FrozenDictionary the FrozenHashTable is also used, but there is no hash code calculation, because the key is hash code itself.
    3. +
  3. LengthBucketsFrozenDictionary uses an algorithm which is similar to a bucket sort.
    4. +
  4. All 11 OrdinalStringFrozenDictionary implementations are also use FrozenHashTable, but they have a specific hash code calculation algorithm.
    5. +
  5. SmallValueTypeComparableFrozenDictionary, SmallValueTypeDefaultComparerFrozenDictionary and SmallFrozenDictionary use linear search, because their size is not greater than 10 elements.
    6. +
+ +

The choosing of the appropriate implementation depends on the multiple parameters and done in CreateFromDictionary method of a FrozenDictionary static class. Now, let’s take a look at each group separately and their algorithms, and run benchmarks.

+ +

Disclaimer

+ +

The results of the benchmarks are very conditional. I admit, that the benchmark may show different results on a different computer, with a different CPU, with a different compiler or in a different scenario. Always check your code in your specific conditions and don’t trust to the articles from the internet.

+ +

The source code and raw results are located in this repo.

+ +

Group 1. Default dictionaries

+ +

As I said earlier, the FrozehHashTable structure is used in DefaultFrozenDictionary and ValueTypeDefaultComparerFrozenDictionary. This structure, as you may guess from its name, is a hash table implementation. For better understanding how FrozenHashTable differs from Dictionary, need to remember how search is implemented in Dictionary. If you already know this part, you may skip the following explanation.

+ +

Let’s consider the following dictionary:

+ +
var dictionary = new Dictionary<Fruit, string>()  
+{  
+    [new("apple")] = "APPLE",  
+    [new("grape")] = "GRAPE",  
+    [new("lemon")] = "LEMON",  
+    [new("fig")] = "FIG",  
+    [new("lime")] = "LIME",  
+    [new("kiwi")] = "KIWI",  
+};
+
+public record Fruit(string Value);
+
+ +

When, for example, we search a value for Fruit("fig") key, the following are happened in Dictionary (Figure 2):

+ +
    +
  1. Calculate the key hashcode.
    2. +
  2. Calculate the index of the bucket (bucketIndex).
    3. +
  3. If the key in the entry is equal to the searchable key, then we return the related value. Otherwise, we go to the next entry and repeat step 3.
    4. +
+ +

content + +Figure 2 – Search in Dictionary +

+ +

Search algorithm

+ +

FrozenDictionary immutability allows working with buckets differently. Since the number of key-value pairs don’t change, it’s possible to:

+ +
    +
  1. Select the number of buckets so that the number of collisions will be no more than 5%.
    2. +
  2. Place keys and values in the _keys and _values ​​arrays, instead of a linked list in the Dictionary. This makes search more efficient due to higher data locality.
    3. +
+ +

Using FrozenDictionary, searching for a value for the key Fruit("fig") would look like this (Figure 3):

+ +
    +
  1. Calculate the hashCode of the key.
    2. +
  2. Calculate the bucketIndex.
    3. +
  3. In a bucket array, receive values start and end. These values are boundaries in a HashCodes array.
    4. +
  4. Iterate the HashCodes array from start to end and search the key. If found, return the value. Otherwise, return null.
    5. +
+ +

content + +Figure 3 – Search in DefaultFrozenDictionary +

+ +

Benchmark

+ +

The benchmarks results for DefaultFrozenDictionary and ValueTypeDefaultComparerFrozenDictionary are on Figure 4 and 5.

+ +

content + +Reading speed from ValueTypeDefaultComparerFrozenDictionary comparing to Dictionary +

+ +

content + +Reading speed from DefaultFrozenDictionary comparing to Dictionary +

+ +

The high search speed in Dictionary compared to ValueTypeDefaultComparerFrozenDictionary for dictionaries with up to 1000 elements is probably due to aggressive method inlining in Dictionary. I couldn’t understand why the limit is exactly 1000 elements, as there’s nothing about this in the source code. It might be related to the JIT compiler’s implementation. If you have any ideas on this, feel free to share them in the comments.

+ +

In other cases, FrozenDictionary is 31-32% faster for value types and 17-18% faster for reference types.

+ +

Group 2. Dictionary for Int32 keys

+ +

Int32FrozenDictionary also uses FrozenHashTable. The main feature of this class is that if the key type is an integer, its hash is equal to its value, so collisions in such a dictionary are impossible. For example, you can’t add two elements with the key 123 – an exception will be thrown.

+ +
var dict = new Dictionary<int, int>();  
+dict.Add(123, 1);  
+dict.Add(123, 2); // System.ArgumentException: An item with the same key has already been added.
+
+ +

Search algorithm

+ +

This allows skipping the hash calculation during reads and using the key’s value directly. As a result, value lookup works like this (Figure 6):

+ +
    +
  1. The bucket index is calculated directly from the key’s value.
    2. +
  2. From the bucket array, we get the start and end values, which define the boundaries in the HashCodes array.
    3. +
  3. We iterate through the HashCodes array from start to end, looking for the target key and return the value when found.
    4. +
+ +

content + +Figure 6 – Search in Int32FrozenDictionary +

+ +

Benchmark

+ +

Because of optimizations, reading from Int32FrozenDictionary is 34-42% faster (Figure 7).

+ +

content + +Figure 7 – Reading speed from Int32FrozenDictionary comparing to Dictionary +

+ +

Group 3. Dictionary with bucket sort algorithm

+ +

When creating “frozen” dictionaries with string keys, FrozenDictionary tries to create the LengthBucketsFrozenDictionary class. This class is optimized for situations where keys have different lengths. It achieves this by distributing the keys into buckets: for each unique key length, a bucket with a capacity of MaxPerLength = 5 elements is created. Essentially, this is an implementation of block sorting. To make it clearer, let’s look at an example:

+ +
var dictionary = new Dictionary<Fruit, string>()  
+{  
+    ["apple"] = "APPLE",  
+    ["grape"] = "GRAPE",  
+    ["lemon"] = "LEMON",  
+    ["fig"] = "FIG",  
+    ["lime"] = "LIME",  
+    ["kiwi"] = "KIWI",  
+}  
+var frozenDictionary = dictionary.ToFrozenDictionary();
+
+ +

There are keys with length of 3, 4 and 5 in the dictionary. Therefore, they can be distributed between 3 buckets (Figure 8):

+ +
    +
  1. Bucket for keys of length 3: fig.
    2. +
  2. Bucket for keys of length 4: lime and kiwi.
    3. +
  3. Bucket for keys of length 5: apple, grape, and lemon.
    4. +
+ +

content + +Figure 8 – Distribution of the strings based on their length +

+ +

Since we know the minimum (3) and maximum (5) lengths of the keys, there’s no need to create three separate buckets. We can store everything in a single array called lengthBuckets. In this case, the index is calculated like this: (key.Length - minLength) * MaxPerLength.

+ +

Search algorithm

+ +

The search is done in 3 steps (Figure 9):

+ +
    +
  1. The bucket is determined in the _lengthBuckets array.
    2. +
  2. A linear search in the bucket finds the index of the desired key in _keys.
    3. +
  3. The value is returned.
    4. +
+ +

content + +Figure 9 – Search in LengthBucketsFrozenDictionary +

+ +

LengthBucketsFrozenDictionary has two limitations:

+ +
    +
  1. The number of keys with the same length must not exceed MaxPerLength (Pigeonhole Principle). You can’t place 6 strings of the same length in a bucket with a capacity of 5 elements.
    2. +
  2. The number of empty buckets must be less than 20%. Otherwise, the implementation becomes inefficient in terms of memory usage.
    3. +
+ +

If either of these conditions is not met, one of the OrdinalStringFrozenDictionary implementations will be chosen (more on that later).

+ +

Benchmark

+ +

The benchmark results show that reading from a LengthBucketsFrozenDictionary can be up to 99% faster than a regular Dictionary. However, if the dictionary has 5 or more keys with the same length, the performance of small dictionaries (up to 100 items) can be worse (see Figure 10).

+ +

content + +Figure 10 – Reading speed LengthBucketsFrozenDictionary comparing to Dictionary +

+ +

Group 4. Dictionary with string keys

+ +

As we already know, LengthBucketsFrozenDictionary has limitations. When it’s not possible to distribute keys into buckets, one of 11 implementations of the abstract class OrdinalStringFrozenDictionary is used. All of them use FrozenHashTable, but differ in the algorithm for calculating the string’s hash code.

+ +

The choice of the optimal OrdinalStringFrozenDictionary implementation depends on the key analysis by the KeyAnalyzer class. In turn, the result of the analysis depends on key length, the presence of non-ASCII characters, specified string comparison rules and the presence of unique substrings in the keys.

+ +

Obviously, the longer the string, the slower the hash code calculation. Therefore, KeyAnalyzer tries to find the shortest substrings that allow the key to be uniquely identified. To better understand this, let’s revisit the example with fruits: apple, grape, fig, lime, lemon, and kiwi.

+ +

First, KeyAnalyzer analyzes substrings of length 1 with left-aligned keys (see Figure 11).

+ +

content + +Figure 11 – Single-char substrings with left and right algnment +

+ +

In this example, with left-aligned keys, there are repeating substrings. For instance, the 0th character of “lime” and “lemon” is the same, as well as the 1st character of “fig” and “lime” and the 2nd character of “lime” and “lemon.” This means that it is impossible to uniquely identify a key by a single character with such alignment. Therefore, the search for a substring continues with right-aligned keys. In this case, the substrings will be unique when using the 2nd or 1st character from the end. Knowing the alignment, starting index, and length of the substring, the string can be uniquely identified by calculating the hash code of its substring.

+ +

If there are no unique substrings of length 1, the search will continue for substrings of 2 characters, 3 characters, and so on, up to the maximum substring length. This value is calculated as the minimum between minLength (the shortest key length) and MaxSubstringLengthLimit = 8. This limitation is specifically set to avoid analyzing long substrings, as using them doesn’t improve performance.

+ +

If there is no unique substrings at all, the hash code will be calculated for the entire string.

+ +

In addition to the presence of unique substrings, the implementation is also affected by the specified string comparison parameters and the presence of non-ASCII characters. Based on these parameters, a more optimal comparator will be chosen.

+ +

Search algorithm

+ +

Search in dictionaries based on OrdinalStringFrozenDictionary is performed as follows:

+ +
    +
  1. +

    First, it checks whether the key length is within the acceptable range. This allows for quickly discarding keys that clearly do not match due to their length.

    +
    2. +
  2. +

    Next, the same steps that we’ve seen earlier in other dictionaries with FrozenHashTable are performed. The hash code of the substring is calculated, and a search is performed in the hash table. In case of a collision, a linear search is performed.

    +
    3. +
+ +

Benchmark

+ +

According to the benchmark results, a FrozenDictionary with up to 75,000 elements is faster than a regular Dictionary. However, as the dictionary size increases, the search speed becomes worse (see Figure 12).

+ +

content + +Рисунок 12 – Reading speed from OrdinalStringFrozenDictionary_LeftJustifiedSubstring comparing to Dictionary +

+ +

The high speed of FrozenDictionary is due to the fast hash code calculation of keys. The algorithm used in FrozenDictionary is 75% to 90% faster than the one in a regular Dictionary (see Figure 13).

+ +

content + +Figure 13 – Hash calculation speed +

+ +

The performance drop in dictionaries with 75,000 elements or more is caused by the increasing number of hash collisions as the dictionary size grows (see Figure 14).

+ +

content + +Figure 14 – Hash collisions count +

+ +

As shown in the figures, the algorithm used in FrozenDictionary significantly speeds up hash code calculation, improving performance by up to 70%. However, this approach negatively impacts search performance in relatively large dictionaries.

+ +

Group 5. Small dictionaries

+ +

SmallValueTypeComparableFrozenDictionary and SmallValueTypeDefaultComparerFrozenDictionary are used when the original dictionary has no more than 10 elements, while SmallFrozenDictionary is used when it has no more than 4 elements. SmallValueTypeComparableFrozenDictionary is applied if the key type is a built-in primitive value type (e.g., int, long, double, enum, etc.). If the key type is a custom structure, then SmallValueTypeDefaultComparerFrozenDictionary will be used. Developers on .NET explain this by noting that built-in types always implement the IComparable interface, allowing for a slight optimization in search by sorting the key and value arrays in advance.

+ +

Search algorithm

+ +

Strictly speaking, the classes SmallValueTypeComparableFrozenDictionary, SmallValueTypeDefaultComparerFrozenDictionary, and SmallFrozenDictionary are not hash tables. The search for a value in these classes is performed using a simple linear search via a for loop (Figure 15).

+ +

content + +Figure 15 – Search in SmallValueTypeComparableFrozenDictionary +

+ +

In SmallValueTypeComparableFrozenDictionary, since the _keys and _values arrays are sorted, the search can continue as long as the searched key is greater than the current value of _keys[i].

+ +

The implementations of SmallValueTypeDefaultComparerFrozenDictionary and SmallFrozenDictionary are similar to the previous one, except that sorting is not used. Therefore, a linear search through the _keys array will always be performed.

+ +

Benchmark

+ +

Despite all the optimizations in these classes, the benchmark results do not look impressive (see Figure 16). Even the slight speedup that these classes can provide amounts to just a few tens of nanoseconds.

+ +

content + +Figure 16 – Reading speed from SmallValueTypeComparableFrozenDictionary, SmallValueTypeDefaultComparerFrozenDictionary and SmallFrozenDictionary comparing to Dictionary +

+ +

Conclusion

+ +

In this article, I tried to explain the main implementation features of FrozenDictionary. We made sure that in most cases FrozenDictionary is faster than Dictionary.

+ +

Actually, there are lots of another algorithms and optimizations. For example, usage of ArrayPool, fast algorithm for modulus calculation, integer array with bit shift instead of boolean array etc. It would be impossible to do more detailed analysis in a single article. But from time to time I make such posts in my Telegram channel. If you are interested, I will be glad to see you among the readers.

+
+
+ + + +
+
+ + diff --git a/404.html b/404.html new file mode 100644 index 0000000..8364aba --- /dev/null +++ b/404.html @@ -0,0 +1,84 @@ + + + + + + + +Oops! | alexeyfv + + + + + + + + + + + + + + +
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+

Oops!

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+

Page not found :(

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The requested page could not be found

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+ + diff --git a/CNAME b/CNAME new file mode 100644 index 0000000..0d37d32 --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +www.alexeyfv.xyz diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..c4fa5de --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Alexey Fedorov + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..64d5fdb --- /dev/null +++ b/README.md @@ -0,0 +1,9 @@ +# Project repo for my personal blog + +[![build-jekyll](https://github.com/alexeyfv/blog/actions/workflows/build-jekyll.yml/badge.svg)](https://github.com/alexeyfv/blog/actions/workflows/build-jekyll.yml) [![pages-build-deployment](https://github.com/alexeyfv/blog/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/alexeyfv/blog/actions/workflows/pages/pages-build-deployment) + +## Run + +``` sh +docker run -p 4000:4000 -v path/to/site:/site bretfisher/jekyll-serve +``` diff --git a/about/index.html b/about/index.html new file mode 100644 index 0000000..12e18cd --- /dev/null +++ b/about/index.html @@ -0,0 +1,100 @@ + + + + + + + +About | alexeyfv + + + + + + + + + + + + + + +
+ +
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+
+ +
+

About

+
+ +
+

About me

+ +

My name is Alexey Fedorov, a software engineer with a master’s degree in Power System Automation from Chuvash State University, earned in 2017.

+ +

After working as an electrical power engineer for three years, I decided to completely change my career and become a software engineer. Starting as a desktop developer, I now specialize in backend development, mainly using C# and ASP.NET, but also working with TypeScript and React.

+ +

Since 2022, I’ve been living in Serbia. I’m fluent in Russian and English, and currently learning Serbian.

+ +

About this blog

+ +

I enjoy learning about .NET internals, analyzing and improving code performance, and working with algorithms and data structures. In this blog, I share tips and explain C# development, with a focus on making code faster. From time to time, I also post about life in emigration.

+ +

New posts are typically published weekly, but it depends on the article complexity.

+ +

I’m always learning. If you think I’ve missed something or made a mistake, please leave a comment or send me a message.

+ +

Contact

+ +

Feel free to contact me through the social media links below.

+ +
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+
+ + diff --git a/assets/2022/01/2022-01-27-insidious-enum-in-dotnet/first-response.png b/assets/2022/01/2022-01-27-insidious-enum-in-dotnet/first-response.png new file mode 100644 index 0000000000000000000000000000000000000000..fda41b742197abf56164feae3d0cdbafc29bd144 GIT binary patch literal 6071 zcmchbS5#Boo5n!|AwWck)PNu@AR-b7EwmsVF#!~%gepaZK#<<01XOzOL^`M_@PSH` zB7`DHQL6M_1frDCGVz<4HEU-6({ASClzrCOF4wMJAr;#>->P~=&IH!YyX$ry~%2kpYk>5wid?Z z9A3Ta%+4L*rxwa!IHS^G*SC~7CI~*9vQqA)O5|QwuKtmv6la3c4U@sR1T$&*-sAA47>C*cdvMZ#J zNi$>~VtmN)%BB^ibN}b7IARs~X#4gH-kyPt$*P&rnHFfv^cz*n!`{8qgX6u=-IAl} z2Q#Aw9M_V4Ti#$)V>g#hkB(25{7EG5)4RCq=N3Qkyhq2!Yzt2Q9J(k@-rI)~nrqMp zn^&q+HGxZA@mI1}6;qs6Q|;)hxUIxBnhk`XD}^gf$N8T7Xxws`UL`=S?K6Ze`PH~!uPW1O=U-!umJOG4#Me^%T^ zmq_{H*SckSPQ-Z@s0V-TrWDodYN%>la@yV-N)b8R+feg3EZcd_eUd4z?Zj>1Nw=%Z~&b5yeIV=P4Ws5A|ehwr1$MJw^6U?}%fIl@Tja{-r$mr|+3 zv6al^%C-t3dpm6HrAG1~OeH%}u>`}J5+wRUbV*99csJYQ_9-?;hUj2xes?!&Ak%5~ z-n0j(#vgx%MghCqm&e8JYWf8v(q@>L3J>jN8~Tvz`Kcuu-OpAPy+g^h^!Bs?4E==! 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