The benefits of using a MVVM architecture

Let’s delve into the world of MVVM (Model-View-ViewModel) architecture and explore its advantages. 🚀

Understanding MVVM Architecture

MVVM is a software design pattern that cleanly separates the graphical user interface (View) from the business logic (Model) of an application. It was invented by Microsoft architects Ken Cooper and Ted Peters. The ultimate goal of MVVM is to make the view completely independent from the application logic. Here are the key components of MVVM:

  1. Model: Represents the app’s domain model, including data models, business logic, and validation rules. It communicates with the ViewModel and remains unaware of the View.
  2. View: Represents the user interface of the application. It holds limited, purely presentational logic and is completely agnostic to the business logic. The View communicates with the ViewModel through data binding.
  3. ViewModel: Acts as the link between the View and the Model. It exposes public properties and commands that the View uses via data binding. When state changes occur, the ViewModel notifies the View through notification events.

Advantages of MVVM

  1. Easier Development:
  • Separating the View from the logic allows different teams to work on different aspects of the application simultaneously.
  • Developers can focus on their specific areas (View, ViewModel, or Model) without stepping on each other’s toes.
  1. Easier Testing:
  • UI testing is notoriously challenging. MVVM simplifies this by isolating the business logic in the ViewModel.
  • Unit testing the ViewModel becomes straightforward, as it doesn’t rely on UI components.
  1. Improved Maintainability:
  • The separation between View and ViewModel makes code maintenance more manageable.
  • Changes to the UI (View) won’t impact the underlying logic (ViewModel).
  1. Code Reusability:
  • ViewModel logic can be reused across different Views.
  • For example, if you have similar functionality in multiple screens, you can share the ViewModel code.
  1. Parallel Development:
  • MVVM allows parallel development by enabling different teams to work on different layers.
  • UI designers can focus on the View, while developers handle the ViewModel and Model.

MVVM vs. Other Architectures

  • MVC (Model-View-Controller): MVVM evolved from MVC. While MVC separates applications into three components (Model, View, and Controller), MVVM replaces the Controller with the ViewModel. MVVM aims to minimize code-behind logic in the View.
  • Two-Way Communication: Unlike MVC’s one-way communication (Controller to View), MVVM enables two-way communication between View and ViewModel through data binding.

In summary, MVVM provides a clear separation of concerns, improves maintainability, and enhances testability. It’s a powerful pattern for building robust and scalable applications. So, next time you’re architecting your app, consider embracing MVVM! 🌟

References:

  1. Built In: What Is MVVM Architecture? ¹
  2. Medium: Understanding MVVM Architecture in Android ²
  3. Dev.to: Mastering MVVM ³

Source: Conversation with Bing, 2/26/2024
(1) What Is MVVM Architecture? (Definition, Advantages) | Built In. https://builtin.com/software-engineering-perspectives/mvvm-architecture.
(2) Understanding MVVM Architecture in Android – Medium. https://medium.com/swlh/understanding-mvvm-architecture-in-android-aa66f7e1a70b.
(3) Mastering MVVM: A Comprehensive Guide to the Model-View-ViewModel …. https://dev.to/mochafreddo/mastering-mvvm-a-comprehensive-guide-to-the-model-view-viewmodel-architecture-221g.
(4) Understanding MVVM architecture for Beginners | by Rosh | Medium. https://medium.com/@rosh_dev/understanding-mvvm-architecture-for-beginners-586caaa72179.
(5) Why MVVM and what are it’s core benefits? – Stack Overflow. https://stackoverflow.com/questions/1644453/why-mvvm-and-what-are-its-core-benefits.

Using Detekt in Android Studio Projects

Let’s dive into how you can integrate Detekt into your Android Studio projects to improve code quality. Detekt is a powerful static code analysis tool for Kotlin that helps identify code smells and enforce best practices. Here’s a step-by-step guide:

Integrating Detekt in Android Studio Projects

1. Understanding Detekt

Detekt is designed to enhance your codebase by enforcing a set of rules. It’s particularly useful when collaborating with a team of developers. Some key features of Detekt include:

  • Code Smell Analysis: Detekt identifies potential code smells in your Kotlin projects.
  • Highly Configurable: You can customize Detekt according to your specific needs.
  • Suppression Options: Suppress findings if you don’t want warnings for everything.
  • IDE Integration: Detekt integrates seamlessly with Android Studio.
  • Thresholds and Baselines: Specify code-smell thresholds to break builds or print warnings.

2. Adding Detekt to Your Project

To integrate Detekt into your Android project, follow these steps:

  1. Open Android Studio and sync your project with the Gradle files.
  2. Add Detekt Gradle Plugin: In your project’s build.gradle file, add the Detekt Gradle plugin as a dependency. For example:
    gradle plugins { id("io.gitlab.arturbosch.detekt") version "1.18.1" }
  3. Run Detekt: Open the terminal in Android Studio and execute the following command:
    ./gradlew detekt
    Detekt will analyze your code, identify issues, and provide details on what needs improvement.

3. Rule Sets in Detekt

Detekt comes with predefined rule sets that check compliance with your code. These rules focus on improving code quality without affecting your app’s functionality. Here are some common rule sets:

  • Comments: Addresses issues in comments and documentation.
  • Complexity: Reports complex code, long methods, and parameter lists.
  • Coroutines: Analyzes potential coroutine problems.
  • Empty Blocks: Identifies empty blocks of code.
  • Exceptions: Reports issues related to exception handling.
  • Formatting: Checks codebase formatting (indentation, spacing, etc.).
  • Naming: Enforces naming conventions for classes, packages, functions, and variables.

Remember that Detekt is highly configurable, so you can tailor it to your project’s specific needs.

4. Custom Rules and Processors

Detekt allows you to add your own custom rules and processors. If you encounter specific patterns or code smells unique to your project, consider creating custom rules to catch them.

Conclusion

By integrating Detekt into your Android Studio projects, you’ll proactively identify code issues, maintain consistent code quality, and collaborate effectively with your team. Happy coding! 🚀

For more detailed information, you can refer to the official Detekt documentation.


I hope you find this guide helpful! If you have any further questions or need additional assistance, feel free to ask. 😊

Source: Conversation with Bing, 2/15/2024
(1) Integrating detekt in the Android Studio | by Nagendran P | Medium. https://medium.com/@nagendran.p/integrating-detekt-in-the-android-studio-442128e971f8.
(2) Integrating detekt in the Workflow | Kodeco. https://www.kodeco.com/24470020-integrating-detekt-in-the-workflow.
(3) How to Analyze Your Code with Detekt | by Maria Luíza – Medium. https://medium.com/mobile-app-development-publication/how-analyze-your-code-with-detekt-37be6c9c9105.
(4) detekt – IntelliJ IDEs Plugin | Marketplace – JetBrains Marketplace. https://plugins.jetbrains.com/plugin/10761-detekt.
(5) How to use detekt on a daily basis (in a multi module Android project …. https://proandroiddev.com/how-to-use-detekt-in-a-multi-module-android-project-6781937fbef2.
(6) undefined. https://detekt.github.io/detekt/configurations.html%29.
(7) Improve Code Quality Using Static Code Analysis With detekt. https://williamkingsley.medium.com/improve-code-quality-with-static-code-analysis-using-detekt-454b7e66d2ec.
(8) Adding Detekt to your Android project – Since Last Commit. https://blog.asadmansoor.com/adding-detekt-to-your-android-project/.

Common String Extension Methods in Kotlin

Kotlin is a modern programming language that offers many features to make coding easier and more enjoyable. One of these features is the ability to extend a class or an interface with new functionality without having to inherit from it or use design patterns such as Decorator. This is done via special declarations called extension functions.

Extension functions are useful when you want to add some functionality to a class that you can’t modify, such as a class from a third-party library. For example, you can write new functions for the String class that can be used as if they were methods of the String class.

In this blog post, we will explore some common extension functions that are available in Kotlin for the String class. We will also see how to write our own extension functions and how they are resolved at compile-time.

Built-in Extension Functions for String Class

The String class in Kotlin comes with a huge number of extension functions that can make working with strings easier and more concise. Here are some examples of these functions:

  • replace(oldChar: Char, newChar: Char, ignoreCase: Boolean = false): String – This function returns a new string with all the occurrences of the oldChar replaced with newChar. The ignoreCase parameter is false by default. If set to true, it will ignore case while handling the replacement. For example:

val inputString = "Jelly" println(inputString.replace('l', 'z')) // Jezzy println(inputString.replace('j', 'P', true)) // Pelly println(inputString.replace('j', 'P')) // Jelly

  • uppercase(): String – This function returns a new string with all the characters converted to uppercase. For example:

val inputString = "hello" println(inputString.uppercase()) // HELLO

  • lowercase(): String – This function returns a new string with all the characters converted to lowercase. For example:

val inputString = "HELLO" println(inputString.lowercase()) // hello

  • toCharArray(): CharArray – This function returns a char array containing the characters of the string. For example:

val inputString = "hello" val charArray = inputString.toCharArray() println(charArray.joinToString()) // h, e, l, l, o

  • substring(startIndex: Int, endIndex: Int): String – This function returns a new string that is a substring of the original string starting from startIndex (inclusive) and ending at endIndex (exclusive). For example:

val inputString = "hello" println(inputString.substring(1, 4)) // ell

  • startsWith(prefix: String, ignoreCase: Boolean = false): Boolean – This function returns true if the string starts with the specified prefix. The ignoreCase parameter is false by default. If set to true, it will ignore case while checking for the prefix. For example:

val inputString = "hello" println(inputString.startsWith("he")) // true println(inputString.startsWith("He", true)) // true println(inputString.startsWith("He")) // false

  • endsWith(suffix: String, ignoreCase: Boolean = false): Boolean – This function returns true if the string ends with the specified suffix. The ignoreCase parameter is false by default. If set to true, it will ignore case while checking for the suffix. For example:

val inputString = "hello" println(inputString.endsWith("lo")) // true println(inputString.endsWith("Lo", true)) // true println(inputString.endsWith("Lo")) // false

  • compareTo(other: String, ignoreCase: Boolean = false): Int – This function compares the string with another string lexicographically and returns an integer value. The value is negative if the string is less than the other string, zero if they are equal, and positive if the string is greater than the other string. The ignoreCase parameter is false by default. If set to true, it will ignore case while comparing the strings. For example:

val inputString = "hello" println(inputString.compareTo("world")) // -15 println(inputString.compareTo("Hello", true)) // 0 println(inputString.compareTo("Hello")) // 32

There are many more extension functions for the String class in Kotlin that you can explore in the official documentation.

Writing Your Own Extension Functions for String Class

You can also write your own extension functions for the String class or any other class in Kotlin. To declare an extension function, you need to prefix its name with a receiver type, which refers to the type being extended. For example, the following adds a reverse() function to the String class:

fun String.reverse(): String { return this.reversed() }

The this keyword inside an extension function corresponds to the receiver object (the one that is passed before the dot). Now, you can call such a function on any String object in Kotlin:

val inputString = "hello" println(inputString.reverse()) // olleh

You can also make your extension functions generic by declaring the type parameter before the function name. For example, the following adds a swap() function to the MutableList class that can swap two elements of any type:

fun <T> MutableList<T>.swap(index1: Int, index2: Int) { val tmp = this[index1] this[index1] = this[index2] this[index2] = tmp } val list = mutableListOf(1, 2, 3) list.swap(0, 2) println(list) // [3, 2, 1]

How Extension Functions Are Resolved

It is important to understand that extension functions do not actually modify the classes they extend. They are just syntactic sugar that allows you to call new functions with the dot-notation on variables of that type. Extension functions are resolved statically at compile-time, which means they are not virtual by receiver type. The extension function being called is determined by the type of the expression on which the function is invoked, not by the type of the result from evaluating that expression at runtime.

For example, consider the following code:

open class Shape class Rectangle: Shape() fun Shape.getName() = "Shape" fun Rectangle.getName() = "Rectangle" fun printClassName(s: Shape) { println(s.getName()) } printClassName(Rectangle())

This code prints Shape, because the extension function called depends only on the declared type of the parameter s, which is the Shape class. The actual type of s at runtime (Rectangle) does not matter.

If a class has a member function with the same name and signature as an extension function, the member always wins. For example:

class Example { fun printFunctionType() { println("Class method") } } fun Example.printFunctionType() { println("Extension function") } Example().printFunctionType() // Class method

This code prints Class method, because the member function of the Example class overrides the extension function.

Conclusion

In this blog post, we have learned about some common extension functions for the String class in Kotlin and how to write our own extension functions for any class. We have also seen how extension functions are resolved at compile-time and how they do not modify the classes they extend.

Extension functions are a powerful feature of Kotlin that can help you write more concise and expressive code. They can also help you extend existing classes with new functionality without having to inherit from them or use design patterns such as Decorator.

If you want to learn more about extension functions and other features of Kotlin, you can check out these resources:

Variance in Kotlin: A Beginner’s Guide

Variance is a concept that describes how types relate to each other when they have type parameters. For example, if Dog is a subtype of Animal, is List<Dog> a subtype of List<Animal>? The answer depends on the variance of the type parameter of List.

In this blog, we will explore the different kinds of variance in Kotlin and how they affect the type system and the code we write. We will also compare them with Java’s wildcard types and see how Kotlin simplifies the syntax and semantics of generics.

Declaration-site variance

One way to achieve variance in Kotlin is by using declaration-site variance. This means that we can specify the variance of a type parameter at the class level, where it is declared. This affects all the members and fields of the class that use that type parameter.

For example, let’s define a simple class that represents a producer of some type T:

class Producer<T>(val value: T) { fun produce(): T = value }

This class has a type parameter T that appears as the return type of the produce() method. Now, let’s say we have two subtypes of AnimalDog and Cat. We can create instances of Producer<Dog> and Producer<Cat>:

val dogProducer = Producer(Dog()) val catProducer = Producer(Cat())

But can we assign a Producer<Dog> to a variable of type Producer<Animal>? Intuitively, this should be possible, because a producer of dogs is also a producer of animals. We can always get an animal from it by calling produce(). However, if we try to do this in Kotlin, we get a compiler error:

val animalProducer: Producer<Animal> = dogProducer // Error: Type mismatch

This is because by default, generic types in Kotlin are invariant, meaning that they are not subtypes of each other, even if their type arguments are. This is similar to how Java behaves without wildcards.

To fix this error, we need to make the type parameter T covariant, meaning that it preserves the subtype relationship. We can do this by adding the out modifier to the type parameter declaration:

class Producer<out T>(val value: T) { fun produce(): T = value }

The out modifier tells the compiler that T is only used as an output, not as an input. This means that we can only return values of type T from the class, but we cannot accept them as parameters. This ensures that we don’t violate the type safety by putting a wrong value into the class.

With this modifier, we can now assign a Producer<Dog> to a Producer<Animal>, because Producer<Dog> is a subtype of Producer<Animal>:

val animalProducer: Producer<Animal> = dogProducer // OK

This is called covariance, because the subtype relationship varies in the same direction as the type argument. If Dog is a subtype of Animal, then Producer<Dog> is a subtype of Producer<Animal>.

Covariance is useful when we want to read values from a generic class, but not write to it. For example, Kotlin’s standard library defines the interface List<out T> as covariant, because we can only get elements from a list, but not add or remove them. This allows us to assign a List<Dog> to a List<Animal>, which is convenient for polymorphism.

Use-site variance

Another way to achieve variance in Kotlin is by using use-site variance. This means that we can specify the variance of a type parameter at the point where we use it, such as in a function parameter or a variable declaration. This allows us to override the default variance of the class or interface where the type parameter is declared.

For example, let’s define another simple class that represents a consumer of some type T:

class Consumer<T>(var value: T) { fun consume(value: T) { this.value = value } }

This class has a type parameter T that appears as the parameter type of the consume() method. Now, let’s say we have two subtypes of AnimalDog and Cat. We can create instances of Consumer<Dog> and Consumer<Cat>:

val dogConsumer = Consumer(Dog()) val catConsumer = Consumer(Cat())

But can we assign a Consumer<Animal> to a variable of type Consumer<Dog>? Intuitively, this should be possible, because a consumer of animals can also consume dogs. We can always pass a dog to it by calling consume(). However, if we try to do this in Kotlin, we get a compiler error:

val dogConsumer: Consumer<Dog> = animalConsumer // Error: Type mismatch

This is because by default, generic types in Kotlin are invariant, meaning that they are not subtypes of each other, even if their type arguments are. This is similar to how Java behaves without wildcards.

To fix this error, we need to make the type parameter T contravariant, meaning that it reverses the subtype relationship. We can do this by adding the in modifier to the type parameter usage:

val dogConsumer: Consumer<in Dog> = animalConsumer // OK

The in modifier tells the compiler that T is only used as an input, not as an output. This means that we can only accept values of type T as parameters, but we cannot return them from the class. This ensures that we don’t violate the type safety by getting a wrong value from the class.

With this modifier, we can now assign a Consumer<Animal> to a Consumer<Dog>, because Consumer<Animal> is a subtype of Consumer<Dog>:

val dogConsumer: Consumer<in Dog> = animalConsumer // OK

This is called contravariance, because the subtype relationship varies in the opposite direction as the type argument. If Dog is a subtype of Animal, then Consumer<Animal> is a subtype of Consumer<Dog>.

Contravariance is useful when we want to write values to a generic class, but not read from it. For example, Kotlin’s standard library defines the interface MutableList<T> as invariant, because we can both get and set elements in a mutable list. However, if we only want to add elements to a list, we can use the function addAll(elements: Collection<T>), which accepts a collection of any subtype of T. This function uses use-site variance to make the parameter type covariant:

fun <T> MutableList<T>.addAll(elements: Collection<out T>)

This allows us to add a List<Dog> to a MutableList<Animal>, which is convenient for polymorphism.

Comparison with Java

If you are familiar with Java’s generics, you might notice some similarities and differences between Kotlin and Java’s variance mechanisms. Java uses wildcard types (? extends T and ? super T) to achieve covariance and contravariance, respectively. Kotlin uses declaration-site variance (out T and in T) and use-site variance (T and in T) instead.

The main advantage of Kotlin’s approach is that it simplifies the syntax and semantics of generics. Wildcard types can be confusing and verbose, especially when they are nested or combined with other types. Declaration-site variance allows us to specify the variance once at the class level, instead of repeating it at every usage site. Use-site variance allows us to override the default variance when needed, without introducing new types.

Another advantage of Kotlin’s approach is that it avoids some of the limitations and pitfalls of wildcard types. For example, wildcard types cannot be used as return types or in generic type arguments. Declaration-site variance does not have this restriction, as long as the type parameter is used consistently as an output or an input. Use-site variance also allows us to use both covariant and contravariant types in the same context, such as in function parameters or variables.

Conclusion

In this blog, we learned about variance in Kotlin and how it affects the type system and the code we write. We saw how declaration-site variance and use-site variance can help us achieve covariance and contravariance for generic types. We also compared them with Java’s wildcard types and saw how Kotlin simplifies the syntax and semantics of generics.

Variance is an important concept for writing generic and polymorphic code in Kotlin. It allows us to express more precise and flexible types that can adapt to different situations. By understanding how variance works in Kotlin, we can write more idiomatic and effective code with generics.

I hope you enjoyed this blog and learned something new. If you have any questions or feedback, please let me know in the comments below. Thank you for reading! 😊

How to use composable functions in Android Jetpack Compose

Android Jetpack Compose is a modern toolkit for building native UI. It simplifies and accelerates UI development on Android with less code, powerful tools, and intuitive Kotlin APIs1. In this blog post, we will explore how to use composable functions, which are the building blocks of Jetpack Compose.

What are composable functions?

Composable functions are functions that can be used to describe your UI programmatically by providing data dependencies, rather than focusing on the process of the UI’s construction1. To create a composable function, you just need to add the @Composable annotation to the function name. For example:

@Composable fun Greeting(name: String) { Text(text = "Hello, $name!") }

This function defines a simple UI element that displays a text label with a greeting message. You can call this function from another composable function, such as the setContent block that defines the activity’s layout:

override fun onCreate(savedInstanceState: Bundle?) { super.onCreate(savedInstanceState) setContent { Greeting(name = "World") } }

This will render the text “Hello, World!” on the screen. You can also pass different parameters to the composable function to customize its behavior. For example:

setContent { Greeting(name = "Android") }

This will render the text “Hello, Android!” on the screen.

How to preview composable functions in Android Studio?

One of the advantages of using composable functions is that you can preview them in Android Studio without having to build and install the app to an Android device or emulator1. To do this, you need to use the @Preview annotation on a composable function that does not take in parameters. For example:

@Preview @Composable fun PreviewGreeting() { Greeting(name = "Compose") }

This function calls the Greeting function with a parameter of “Compose”. You can then see a preview of this function in Android Studio by clicking on the split (design/code) view. You can also refresh the preview at any time by clicking on the refresh button at the top of the preview window.

How to use different types of composable functions?

There are many types of composable functions that you can use to create different UI elements and layouts in Jetpack Compose. Some of the most common ones are:

  • Text: This function displays a text label on the screen. You can customize its appearance by passing parameters such as colorfontSizefontWeight, etc.
  • Image: This function displays an image on the screen. You can load an image from a resource or a URL by using the painterResource or rememberImagePainter functions respectively. You can also adjust its size and shape by using parameters such as modifiercontentScalecontentDescription, etc.
  • Button: This function displays a button on the screen. You can handle its click event by passing a lambda expression to the onClick parameter. You can also style it by using parameters such as colorsshapeelevation, etc.
  • Row: This function arranges its children horizontally in a row. You can control how they are aligned and spaced by using parameters such as horizontalArrangementverticalAlignmentmodifier, etc.
  • Column: This function arranges its children vertically in a column. You can control how they are aligned and spaced by using parameters such as verticalArrangementhorizontalAlignmentmodifier, etc.
  • Box: This function stacks its children on top of each other in a box. You can control how they are positioned and sized by using parameters such as alignmentcontentAlignmentmodifier, etc.

Here is an example of how to use some of these composable functions to create a simple UI:

@Composable fun ProfileCard(name: String, image: Int) { Row( modifier = Modifier .padding(16.dp) .fillMaxWidth() .border(1.dp, Color.Gray) ) { Image( painter = painterResource(id = image), contentDescription = null, modifier = Modifier .size(64.dp) .clip(CircleShape) ) Spacer(modifier = Modifier.width(8.dp)) Column( verticalArrangement = Arrangement.Center ) { Text(text = name, fontWeight = FontWeight.Bold) Text(text = "Android Developer", fontStyle = FontStyle.Italic) } } }

This function creates a profile card with an image and some text. You can preview it in Android Studio by adding another function with the @Preview annotation:

@Preview @Composable fun PreviewProfileCard() { ProfileCard(name = "John Doe", image = R.drawable.profile_pic) }

This will show you how the profile card looks like in the preview window.

Conclusion

In this blog post, we learned how to use composable functions in Android Jetpack Compose. We saw how to create, preview, and use different types of composable functions to build native UI. Composable functions are a powerful and expressive way to describe your UI with less code and more flexibility. If you want to learn more about Jetpack Compose, you can check out the official documentation1 or some of the tutorials23 available online.


I hope this blog post was helpful for you. If you have any questions or feedback, please let me know in the comments. Thank you for reading! 😊

How to use Apollo GraphQL in Android Kotlin

GraphQL is a query language for APIs that allows you to specify the data you want from a server in a declarative way. Apollo GraphQL is a popular GraphQL client that generates Kotlin and Java models from GraphQL queries and executes them against a GraphQL server. In this blog post, I will show you how to set up Apollo GraphQL in your Android Kotlin project and how to use it to perform queries, mutations and subscriptions.

Setting up Apollo GraphQL

To use Apollo GraphQL in your Android Kotlin project, you need to add the following dependencies to your build.gradle.kts file:

plugins { id("com.apollographql.apollo3") version "3.8.1" } dependencies { implementation("com.apollographql.apollo3:apollo-runtime:3.8.1") }

You also need to set the package name for the generated models in your apollo block:

apollo { service("service") { packageName.set("com.example") } }

Apollo GraphQL supports three types of files:

  • .graphqls schema files: describe the types in your backend using the GraphQL syntax.
  • .json schema files: describe the types in your backend using the JSON syntax.
  • .graphql executable files: describe your queries and operations in the GraphQL syntax.

By default, Apollo GraphQL requires a schema in your module’s src/main/graphql directory. You can download a schema using introspection with the ./gradlew downloadApolloSchema task.

Writing and executing queries

To write a query, you need to create a .graphql file in your src/main/graphql directory with the following syntax:

query GetPosts($limit: Int) { posts(limit: $limit) { id title author { name } } }

This query will fetch a list of posts with their id, title and author name, and accept a limit argument to limit the number of posts.

Apollo GraphQL will generate a Kotlin class for this query with the same name as the file (GetPostsQuery) and a data class for each type (Post, Author). You can use these classes to execute the query using an ApolloClient instance:

val apolloClient = ApolloClient.Builder() .serverUrl("https://example.com/graphql") .build() val query = GetPostsQuery(limit = 10) apolloClient.query(query).execute().let { response -> if (response.isSuccessfull) { // handle success val posts = response.data?.posts // do something with posts } else { // handle error val error = response.errors?.firstOrNull() // do something with error } }

The execute() method returns an ApolloResponse object that contains either data or errors. You can access the data as query-specific Kotlin types and handle any errors that may occur.

Writing and executing mutations

To write a mutation, you need to create a .graphql file in your src/main/graphql directory with the following syntax:

mutation CreatePost($title: String!, $authorId: ID!) { createPost(input: {title: $title, authorId: $authorId}) { id title author { name } } }

This mutation will create a new post with the given title and author id, and return the created post with its id, title and author name.

Apollo GraphQL will generate a Kotlin class for this mutation with the same name as the file (CreatePostMutation) and a data class for each type (Post, Author). You can use these classes to execute the mutation using an ApolloClient instance:

val apolloClient = ApolloClient.Builder() .serverUrl("https://example.com/graphql") .build() val mutation = CreatePostMutation(title = "Hello world", authorId = "1") apolloClient.mutate(mutation).execute().let { response -> if (response.isSuccessfull) { // handle success val post = response.data?.createPost // do something with post } else { // handle error val error = response.errors?.firstOrNull() // do something with error } }

The execute() method returns an ApolloResponse object that contains either data or errors. You can access the data as mutation-specific Kotlin types and handle any errors that may occur.

Writing and executing subscriptions

To write a subscription, you need to create a .graphql file in your src/main/graphql directory with the following syntax:

subscription OnPostCreated { postCreated { id title author { name } } }

This subscription will listen for new posts created on the server and return the new post with its id, title and author name.

Apollo GraphQL will generate a Kotlin class for this subscription with the same name as the file (OnPostCreatedSubscription) and a data class for each type (Post, Author). You can use these classes to execute the subscription using an ApolloClient instance:

val apolloClient = ApolloClient.Builder() .serverUrl("wss://example.com/graphql") .build() val subscription = OnPostCreatedSubscription() apolloClient.subscribe(subscription).execute().collect { response -> if (response.isSuccessfull) { // handle success val post = response.data?.postCreated // do something with post } else { // handle error val error = response.errors?.firstOrNull() // do something with error } }

The execute() method returns a Flow of ApolloResponse objects that emit data or errors. You can collect the data as subscription-specific Kotlin types and handle any errors that may occur.

Conclusion

In this blog post, I showed you how to use Apollo GraphQL in your Android Kotlin project and how to perform queries, mutations and subscriptions. Apollo GraphQL is a powerful and type-safe GraphQL client that makes it easy to work with GraphQL APIs. You can learn more about Apollo GraphQL from their official documentation12 or their GitHub repository3. I hope you enjoyed this blog post and found it useful. Happy coding! 🚀


How did I do? 😊

Image Loading in Jetpack Compose

Jetpack Compose is a modern toolkit for building native UI on Android. It simplifies and accelerates UI development with declarative and reactive programming. One of the common tasks in UI development is to display images from various sources, such as network, local storage, assets or resources. In this blog post, we will explore how to load images in Jetpack Compose using different libraries and techniques.

Loading images from disk

To load an image from disk, such as a PNG, JPEG, WEBP or vector resource, we can use the Image composable with the painterResource API. The painterResource function takes an image reference (such as a resource ID) and returns a Painter object that can be used to draw the image on the screen. We don’t need to know the type of the asset, just use painterResource in Image or paint modifiers1.

For example, to load an image from a resource:

Image( painter = painterResource(id = R.drawable.dog), contentDescription = stringResource(id = R.string.dog_content_description) )

To ensure that our app is accessible, we should supply a contentDescription for visual elements on screen. TalkBack reads out the content description, so we must ensure that the text is meaningful if read out loud and translated. In the above example, we use a stringResource function to load up the translated content description from the strings.xml file. If our visual element on screen is purely for visual decoration, we can set our contentDescription to null for the screen reader to ignore it1.

If we need lower-level ImageBitmap specific functionality, we can use ImageBitmap.imageResource function to load up a bitmap. For more information on ImageBitmaps, read the ImageBitmap versus ImageVector section1.

Loading images from the internet

To load an image from the internet, there are several third-party libraries available that can help us handle the process. Image loading libraries do a lot of the heavy lifting for us; they handle both caching (so we don’t download the image multiple times) and networking logic to download the image and display it on screen1.

Some of the popular image loading libraries for Jetpack Compose are:

To use any of these libraries in our Android app, we need to add the corresponding dependency to our build.gradle file. Then, we can use their provided composables or extensions to load an image from a URL.

For example, to load an image with Coil:

AsyncImage( model = "https://example.com/image.jpg", contentDescription = "Translated description of what the image contains" )

To load an image with Glide:

GlideImage( model = "https://example.com/image.jpg", contentDescription = "Translated description of what the image contains" )

To load an image with Fresco:

FrescoImage( model = "https://example.com/image.jpg", contentDescription = "Translated description of what the image contains" )

To load an image with Landscapist:

NetworkImage( imageUrl = "https://example.com/image.jpg", contentDescription = "Translated description of what the image contains" )

We can also configure the requests with optional parameters or lambdas to customize the loading behavior and appearance of the images.

Code examples

Here are some code examples of using different libraries to load images from the internet in Jetpack Compose:

Loading an image with Coil

AsyncImage( model = "https://coil-kt.github.io/coil/logo.svg", contentDescription = "Coil logo", modifier = Modifier.size(200.dp), loading = { Box(Modifier.matchParentSize()) { CircularProgressIndicator(Modifier.align(Alignment.Center)) } }, error = { Image( painter = painterResource(id = R.drawable.error), contentDescription = "Error image" ) } )

Loading an image with Glide

GlideImage( model = "https://coil-kt.github.io/coil/logo.svg", contentDescription = "Coil logo", modifier = Modifier.size(200.dp), requestBuilder = { crossfade(true) placeholder(R.drawable.placeholder) error(R.drawable.error) } )

Loading an image with Fresco

FrescoImage( model = "https://coil-kt.github.io/coil/logo.svg", contentDescription = "Coil logo", modifier = Modifier.size(200.dp), controllerBuilder = { autoPlayAnimations(true) placeholderImage(R.drawable.placeholder) failureImage(R.drawable.error) } )

Loading an image with Landscapist

NetworkImage( imageUrl = "https://coil-kt.github.io/coil/logo.svg", contentDescription = "Coil logo", modifier = Modifier.size(200.dp), loading = { ShimmerParams( baseColor = Color.LightGray, highlightColor = Color.White, dropOff = 0.65f ) }, failure = { Image( painter = painterResource(id = R.drawable.error), contentDescription = "Error image" ) }, success = { RevealCircularTransition( durationMillis = 600, delayMillis = 100 ) } )

Structural Design Patterns in Android Kotlin

Structural design patterns are all about how you compose objects and classes to obtain new functionality. They help you simplify the design by finding a simple way of realizing relationships between entities1. In this blog post, we will cover three common structural design patterns: adapter, decorator and facade. We will also see how to implement them in Kotlin using some of its features such as extension functions, data classes and delegation.

Adapter Pattern

The adapter pattern allows you to convert the interface of a class into another interface that clients expect. It lets you make incompatible classes work together by wrapping one of them with an adapter class that implements the desired interface2. For example, suppose you have a class that represents a book:data class Book(val title: String, val author: String, val pages: Int) Copy

And you have another class that represents a library:class Library { private val books = mutableListOf<Book>() fun addBook(book: Book) { books.add(book) } fun getBooks(): List<Book> { return books } } Copy

Now, suppose you want to use a third-party library that provides a function to print a list of books in a nice format. However, this function expects a list of objects that implement the Printable interface:interface Printable { fun print(): String } Copy

How can you make your Book class compatible with this function? One way is to use the adapter pattern. You can create an adapter class that implements Printable and wraps a Book object:class BookAdapter(private val book: Book) : Printable { override fun print(): String { return "Title: ${book.title}, Author: ${book.author}, Pages: ${book.pages}" } } Copy

Then, you can use this adapter class to convert your list of books into a list of printable objects:val library = Library() library.addBook(Book("The Lord of the Rings", "J.R.R. Tolkien", 1178)) library.addBook(Book("Harry Potter and the Philosopher's Stone", "J.K. Rowling", 223)) library.addBook(Book("The Hitchhiker's Guide to the Galaxy", "Douglas Adams", 180)) val printables = library.getBooks().map { book -> BookAdapter(book) } printBooks(printables) // This is the third-party function that expects a list of Printable objects Copy

Alternatively, you can use an extension function to achieve the same result without creating an adapter class:fun Book.toPrintable(): Printable { return object : Printable { override fun print(): String { return "Title: ${this@toPrintable.title}, Author: ${this@toPrintable.author}, Pages: ${this@toPrintable.pages}" } } } val printables = library.getBooks().map { book -> book.toPrintable() } printBooks(printables) Copy

The advantage of using an extension function is that it reduces the amount of code and classes needed. However, it may not be suitable for complex scenarios where you need more control over the adaptation logic.

Decorator Pattern

The decorator pattern allows you to add new behavior or functionality to an existing object without modifying its structure or subclassing it. It lets you wrap an object with another object that implements the same interface and delegates all the requests to the original object while adding some extra logic before or after2. For example, suppose you have an interface that represents a coffee:interface Coffee { fun cost(): Double fun description(): String } Copy

And you have a concrete class that implements this interface:class Espresso : Coffee { override fun cost(): Double { return 1.5 } override fun description(): String { return "Espresso" } } Copy

Now, suppose you want to add some extra ingredients to your coffee, such as milk, whipped cream or caramel. One way is to use the decorator pattern. You can create an abstract class that implements Coffee and wraps another Coffee object:abstract class CoffeeDecorator(private val coffee: Coffee) : Coffee { override fun cost(): Double { return coffee.cost() } override fun description(): String { return coffee.description() } } Copy

Then, you can create concrete subclasses that extend this abstract class and add their own logic:class Milk(private val coffee: Coffee) : CoffeeDecorator(coffee) { override fun cost(): Double { return super.cost() + 0.5 } override fun description(): String { return "${super.description()}, Milk" } } class WhippedCream(private val coffee: Coffee) : CoffeeDecorator(coffee) { override fun cost(): Double { return super.cost() + 0.7 } override fun description(): String { return "${super.description()}, Whipped Cream" } } class Caramel(private val coffee: Coffee) : CoffeeDecorator(coffee) { override fun cost(): Double { return super.cost() + 0.8 } override fun description(): String { return "${super.description()}, Caramel" } } Copy

Then, you can use these decorator classes to create different combinations of coffee:val espresso = Espresso() println("${espresso.description()} costs ${espresso.cost()}") // Espresso costs 1.5 val espressoWithMilk = Milk(espresso) println("${espressoWithMilk.description()} costs ${espressoWithMilk.cost()}") // Espresso, Milk costs 2.0 val espressoWithMilkAndWhippedCream = WhippedCream(espressoWithMilk) println("${espressoWithMilkAndWhippedCream.description()} costs ${espressoWithMilkAndWhippedCream.cost()}") // Espresso, Milk, Whipped Cream costs 2.7 val espressoWithMilkAndWhippedCreamAndCaramel = Caramel(espressoWithMilkAndWhippedCream) println("${espressoWithMilkAndWhippedCreamAndCaramel.description()} costs ${espressoWithMilkAndWhippedCreamAndCaramel.cost()}") // Espresso, Milk, Whipped Cream, Caramel costs 3.5 Copy

Alternatively, you can use delegation instead of inheritance to implement the decorator pattern in Kotlin. You can create an interface that represents a decorator:interface Decorator<T> : T { val delegate: T } Copy

Then, you can create concrete classes that implement this interface and delegate all the requests to the wrapped object while adding their own logic:class Milk(override val delegate: Coffee) : Decorator<Coffee>, Coffee by delegate { override fun cost(): Double { return delegate.cost() + 0.5 } override fun description(): String { return "${delegate.description()}, Milk" } } class WhippedCream(override val delegate: Coffee) : Decorator<Coffee>, Coffee by delegate { override fun cost(): Double { return delegate.cost() + 0.7 } override fun description(): String { return "${delegate.description()}, Whipped Cream" } } class Caramel(override val delegate: Coffee) : Decorator<Coffee>, Coffee by delegate { override fun cost(): Double { return delegate.cost() + 0.8 } override fun description(): String { return "${delegate.description()}, Caramel" } } Copy

Then, you can use these decorator classes to create different combinations of coffee as before:val espresso = Espresso() println("${espresso.description()} costs ${espresso.cost()}") // Espresso costs 1.5 val espressoWithMilk = Milk(espresso) println("${espressoWithMilk.description()} costs ${espressoWithMilk.cost()}") // Espresso, Milk costs 2.0 val espressoWithMilkAndWhippedCream = WhippedCream(espressoWithMilk) println("${espressoWithMilkAndWhippedCream.description()} costs ${espressoWithMilkAndWhippedCream.cost()}") // Espresso, Milk, Whipped Cream costs 2.7 val espressoWithMilkAndWhippedCreamAndCaramel = Caramel(espressoWithMilkAndWhippedCream) println("${espressoWithMilkAndWhippedCreamAndCaramel.description()} costs ${espressoWithMilkAndWhippedCreamAndCaramel.cost()}") // Espresso, Milk, Whipped Cream, Caramel costs 3.5 Copy

The advantage of using delegation is that it avoids creating unnecessary subclasses and makes the code more concise and readable.

Facade Pattern

The facade pattern simplifies the interaction with a complex system or library by providing a unified and easy-to-use interface. It hides the details and implementation of the system from the clients and exposes only the essential functionality. For example, suppose you have a complex library that offers various functions for image processing:class ImageProcessingLibrary { fun loadBitmap(path: String): Bitmap { ... } fun resizeBitmap(bitmap: Bitmap, width: Int, height: Int): Bitmap { ... } fun cropBitmap(bitmap: Bitmap, x: Int, y: Int, width: Int, height: Int): Bitmap { ... } fun applyFilter(bitmap: Bitmap, filter: Filter): Bitmap { ... } fun saveBitmap(bitmap: Bitmap, path: String) { ... } // ... more functions } Copy

Using this library directly may be cumbersome and error-prone for the clients. They need to know how to use each function correctly and in what order. For example, if they want to load an image from a file, resize it, apply a filter and save it back to another file, they need to write something like this:val library = ImageProcessingLibrary() val bitmap = library.loadBitmap("input.jpg") val resizedBitmap = library.resizeBitmap(bitmap, 300, 300) val filteredBitmap = library.applyFilter(resizedBitmap, Filter.GRAYSCALE) library.saveBitmap(filteredBitmap, "output.jpg") Copy

To make this task easier and more convenient, you can use the facade pattern. You can create a class that acts as a facade for the library and provides a simple interface for common operations:class ImageProcessor(private val library: ImageProcessingLibrary) { fun processImage(inputPath: String, outputPath: String, width: Int, height: Int, filter: Filter) { val bitmap = library.loadBitmap(inputPath) val resizedBitmap = library.resizeBitmap(bitmap, width, height) val filteredBitmap = library.applyFilter(resizedBitmap, filter) library.saveBitmap(filteredBitmap, outputPath) } // ... more functions for other operations } Copy

Then, the clients can use this facade class to perform the same operation with less code and complexity:val processor = ImageProcessor(ImageProcessingLibrary()) processor.processImage("input.jpg", "output.jpg", 300, 300, Filter.GRAYSCALE) Copy

The advantage of using the facade pattern is that it reduces the coupling between the clients and the system or library. It also improves the readability and maintainability of the code by encapsulating the logic and details of the system or library in one place.

Conclusion

In this blog post, we have learned about three common structural design patterns: adapter, decorator and facade. We have seen how they can help us simplify the design and implementation of our Android applications by composing objects and classes in different ways. We have also seen how to implement them in Kotlin using some of its features such as extension functions, data classes and delegation. We hope you have enjoyed this post and found it useful. If you have any questions or feedback, please feel free to leave a comment below. Happy coding!

Creational Design Patterns in Kotlin Android

Creational design patterns are a set of solutions to common software development problems that deal with how objects are being created. Using such patterns will ensure that your code is flexible and reusable, and that you avoid hard-coded dependencies and tight coupling between classes.

In this blog post, I will show you some of the most important and widely used creational design patterns in Kotlin Android. You will learn how to apply these patterns to your projects and how they can help you write better and more maintainable code. I will cover four creational design patterns:

  • Factory and abstract factory (provider model) method
  • Singleton
  • Builder
  • Dependency injection

Factory and abstract factory (provider model) method

The factory method pattern defines an interface for creating an object, but lets subclasses decide which class to instantiate. The abstract factory pattern provides an interface for creating families of related or dependent objects without specifying their concrete classes.

These patterns are useful when you want to decouple the creation of objects from their usage, or when you want to provide different implementations of the same interface depending on some conditions. For example, you can use these patterns to create different types of views or fragments based on the device configuration or user preferences.

In Kotlin, you can use the provider model to implement these patterns. The provider model is a way of creating objects using lambda expressions or function references that act as factories. For example, you can use a provider function to create different types of fragments based on a parameter:

// An interface for fragments that display some content interface ContentFragment { fun showContent(content: String) } // A concrete implementation of ContentFragment that shows content in a text view class TextFragment : Fragment(), ContentFragment { private lateinit var textView: TextView override fun onCreateView( inflater: LayoutInflater, container: ViewGroup?, savedInstanceState: Bundle? ): View? { return inflater.inflate(R.layout.text_fragment, container, false) } override fun onViewCreated(view: View, savedInstanceState: Bundle?) { super.onViewCreated(view, savedInstanceState) textView = view.findViewById(R.id.text_view) } override fun showContent(content: String) { textView.text = content } } // Another concrete implementation of ContentFragment that shows content in a web view class WebFragment : Fragment(), ContentFragment { private lateinit var webView: WebView override fun onCreateView( inflater: LayoutInflater, container: ViewGroup?, savedInstanceState: Bundle? ): View? { return inflater.inflate(R.layout.web_fragment, container, false) } override fun onViewCreated(view: View, savedInstanceState: Bundle?) { super.onViewCreated(view, savedInstanceState) webView = view.findViewById(R.id.web_view) } override fun showContent(content: String) { webView.loadUrl(content) } } // A provider function that returns a ContentFragment based on a parameter fun provideContentFragment(type: String): ContentFragment = when (type) { "text" -> TextFragment() "web" -> WebFragment() else -> throw IllegalArgumentException("Unknown type: $type") }

Singleton

The singleton pattern ensures that a class has only one instance and provides a global point of access to it. This pattern is useful when you want to have a single source of truth for some data or functionality in your app. For example, you can use this pattern to create a repository that handles data access from different sources.

In Kotlin, you can use the object declaration to create a singleton class. The object declaration combines a class declaration and a single instance of that class into one expression. For example, you can use an object declaration to create a news repository that fetches data from a remote data source:

// A singleton class that acts as a repository for news data object NewsRepository { // A reference to the remote data source private val newsRemoteDataSource = // getDataSource() // A flow that emits the latest news from the remote data source val latestNews: Flow<List<ArticleHeadline>> = newsRemoteDataSource.latestNews // A function that returns the details of an article by its id suspend fun getArticleDetails(id: String): ArticleDetails { return newsRemoteDataSource.getArticleDetails(id) } }

Builder

The builder pattern separates the construction of a complex object from its representation so that the same construction process can create different representations. This pattern is useful when you want to create objects with many optional parameters or when you want to have more control over how the object is constructed. For example, you can use this pattern to create an alert dialog with various options.

In Kotlin, you can use named arguments and default values to implement this pattern. Named arguments allow you to specify the name of a parameter when calling a function, which makes the code more readable and avoids errors when there are many parameters. Default values allow you to omit some parameters when calling a function if they have a predefined value. For example, you can use named arguments and default values to create an alert dialog builder class:

// A class that represents an alert dialog with various options class AlertDialog( val title: String, val message: String, val positiveButton: String = "OK", val negativeButton: String? = null, val icon: Int? = null, val onPositiveClick: () -> Unit = {}, val onNegativeClick: () -> Unit = {} ) { // A function that shows the alert dialog on the screen fun show() { // Create and display an alert dialog using the Android SDK ... } } // A builder class that creates an AlertDialog instance using named arguments and default values class AlertDialogBuilder { // A function that returns an AlertDialog instance with the given parameters fun build( title: String, message: String, positiveButton: String = "OK", negativeButton: String? = null, icon: Int? = null, onPositiveClick: () -> Unit = {}, onNegativeClick: () -> Unit = {} ): AlertDialog { return AlertDialog( title = title, message = message, positiveButton = positiveButton, negativeButton = negativeButton, icon = icon, onPositiveClick = onPositiveClick, onNegativeClick = onNegativeClick ) } }

Dependency injection

The dependency injection pattern is a technique whereby one object supplies the dependencies of another object. A dependency is an object that can be used (a service). An injection is the passing of a dependency to a dependent object (a client) that would use it.

This pattern is useful when you want to reduce coupling and increase testability between classes by delegating the responsibility of creating and providing dependencies to another object or framework. For example, you can use this pattern to inject dependencies into your activities or view models.

In Kotlin Android, you can use frameworks like Dagger or Koin to implement this pattern. These frameworks provide annotations or DSLs to define dependencies and inject them into your classes. For example, you can use Koin to inject dependencies into your view model:

// A class that represents a user profile view model with some dependencies class UserProfileViewModel( private val userRepository: UserRepository, private val analyticsService: AnalyticsService ) : ViewModel() { // Some view model logic using userRepository and analyticsService ... } // A module that defines dependencies using Koin DSL val appModule = module { // Define UserRepository as a singleton using factory function single<UserRepository> { UserRepositoryImpl(get()) } // Define AnalyticsService as a singleton using constructor injection single<AnalyticsService> { AnalyticsServiceImpl() } // Define UserProfileViewModel using constructor injection viewModel { UserProfileViewModel(get(), get()) } } // Start Koin with appModule in Application class class MyApp : Application() { override fun onCreate() { super.onCreate() startKoin { androidContext(this@MyApp) modules(appModule) } } } // Get UserProfileViewModel instance using Koin extension function in Activity class class UserProfileActivity : AppCompatActivity() { // Inject UserProfileViewModel private val viewModel by viewModel<UserProfileViewModel>() override fun onCreate(savedInstanceState: Bundle?) { super.onCreate(savedInstanceState) setContentView(R.layout.activity_user_profile) // Use viewModel ... } }

Conclusion

In this blog post, you learned how to use creational design patterns in Kotlin Android. You learned how to apply these patterns to your projects and how they can help you write better and more maintainable code. You learned how to use factory and abstract factory (provider model) method, singleton, builder, and dependency injection patterns.

If you want to learn more about design patterns and other Kotlin features for Android development, check out these resources:

I hope you enjoyed this blog post and found it useful. Happy coding! 😊

How to use Flow in Android Programming

Flow is a stream processing API in Kotlin developed by JetBrains1It’s an implementation of the Reactive Stream specification, an initiative whose goal is to provide a standard for asynchronous stream processing1Jetbrains built Kotlin Flow on top of Kotlin Coroutines, which means that you can use suspend functions to produce and consume values asynchronously2.

In this blog post, I will show you how to use Flow in your Android project to handle live data updates and endless streams of data. You will learn how to create flows, modify them, collect them, and use StateFlow and SharedFlow to share state and events across your app.

Creating a flow

To create flows, use the flow builder APIs. The flow builder function creates a new flow where you can manually emit new values into the stream of data using the emit function2. For example, you can use a flow to receive live updates from a network API:

class NewsRemoteDataSource( private val newsApi: NewsApi, private val refreshIntervalMs: Long = 5000 ) { val latestNews: Flow<List<ArticleHeadline>> = flow { while(true) { val latestNews = newsApi.fetchLatestNews() emit(latestNews) // Emits the result of the request to the flow delay(refreshIntervalMs) // Suspends the coroutine for some time } } } // Interface that provides a way to make network requests with suspend functions interface NewsApi { suspend fun fetchLatestNews(): List<ArticleHeadline> }

The flow builder is executed within a coroutine. Thus, it benefits from the same asynchronous APIs, but some restrictions apply:

Modifying the stream

You can use various operators to transform or filter the values emitted by a flow. For example, you can use map to apply a function to each value, filter to remove unwanted values, or combine to merge two flows into one2. For example, you can use map to convert the list of article headlines into a list of article titles:

val latestNewsTitles: Flow<List<String>> = latestNews.map { headlines -> headlines.map { headline -> headline.title } }

You can also use operators that are specific to flows, such as debounce or distinctUntilChanged. These operators help you deal with flows that emit values too frequently or unnecessarily2. For example, you can use debounce to ignore values that are emitted in quick succession:

val debouncedNewsTitles: Flow<List<String>> = latestNewsTitles.debounce(1000) // Ignores values that are emitted less than 1000 ms apart

Collecting from a flow

To start receiving values from a flow, you need to collect it. Collecting is a terminal operation that triggers the execution of the flow and invokes a given action for every value emitted by the flow2. You need to collect flows from a coroutine or a suspend function. For example, you can collect the debounced news titles from an activity:

class LatestNewsActivity : AppCompatActivity() { private val newsRemoteDataSource = // getDataSource() override fun onCreate(savedInstanceState: Bundle?) { ... // Start a coroutine in the lifecycle scope lifecycleScope.launch { // repeatOnLifecycle launches the block in a new coroutine every time the // lifecycle is in the STARTED state (or above) and cancels it when it's STOPPED. repeatOnLifecycle(Lifecycle.State.STARTED) { // Trigger the flow and start listening for values. newsRemoteDataSource.debouncedNewsTitles.collect { titles -> // Update UI with new titles } } } } }

Note that collecting from a flow can be a suspending operation if the flow is infinite or slow. This means that you should not collect from multiple flows sequentially in the same coroutine, as this will block the execution of the next collect until the previous one finishes. Instead, you should launch multiple coroutines or use other operators like zip or flatMapMerge to collect from multiple flows concurrently2.

StateFlow and SharedFlow

StateFlow and SharedFlow are Flow APIs that enable flows to optimally emit state updates and emit values to multiple consumers3.

StateFlow is a state-holder observable flow that emits the current and new state updates to its collectors. The current state value can also be read through its value property. To update state and send it to the flow, assign a new value to the value property of the MutableStateFlow class3.

In Android, StateFlow is a great fit for classes that need to maintain an observable mutable state. For example, you can use StateFlow to expose UI state from a ViewModel:

class LatestNewsViewModel( private val newsRepository: NewsRepository ) : ViewModel() { // Backing property to avoid state updates from other classes private val _uiState = MutableStateFlow(LatestNewsUiState.Success(emptyList())) // The UI collects from this StateFlow to get its state updates val uiState: StateFlow<LatestNewsUiState> = _uiState init { viewModelScope.launch { newsRepository.favoriteLatestNews // Update View with the latest favorite news // Writes to the value property of MutableStateFlow, // adding a new element to the flow and updating all // of its collectors .collect { favoriteNews -> _uiState.value = LatestNewsUiState.Success(favoriteNews) } } } } // Represents different states for the LatestNews screen sealed class LatestNewsUiState { data class Success(val news: List<ArticleHeadline>): LatestNewsUiState() data class Error(val exception: Throwable): LatestNewsUiState() }

Unlike a cold flow built using the flow builder, a StateFlow is hot: collecting from the flow doesn’t trigger any producer code. A StateFlow is always active and in memory, and it becomes eligible for garbage collection only when there are no other references to it from a garbage collection root. When a new consumer starts collecting from the flow, it receives the last state in the stream and any subsequent states. You can find this behavior in other observable classes like LiveData3.

SharedFlow is an observable hot flow that emits values only when active collectors are present. Unlike StateFlow, SharedFlow does not have any initial value nor does it store any value at all. To emit values into SharedFlow use its emit function3.

In Android, SharedFlow is useful for sharing events among multiple consumers without having any initial value or state associated with them. For example, you can use SharedFlow to broadcast user input events across your app:

class UserInputManager { // Creates an instance of MutableSharedFlow with zero replay buffer size, // meaning that only new events will be emitted by this SharedFlow. private val _userInputEvents = MutableSharedFlow<UserInputEvent>() // Exposes only SharedFlow interface so other classes cannot modify it. val userInputEvents: SharedFlow<UserInputEvent> = _userInputEvents fun onUserInput(event: UserInputEvent) { viewModelScope.launch { _userInputEvents.emit(event) // Emits event into SharedFlow } } } // Represents different types of user input events sealed class UserInputEvent { data class Tap(val x: Float, val y: Float): UserInputEvent() data class Swipe(val direction: Direction): UserInputEvent() }

To collect from StateFlow or SharedFlow, you can use any terminal operator like collect or first as with any other flow.

Conclusion

In this blog post, you learned how to use Flow in your Android project to handle live data updates and endless streams of data. You learned how to create flows, modify them, collect them, and use StateFlow and SharedFlow to share state and events across your app.

If you want to learn more about Flow and other Kotlin features for Android development, check out these resources:

I hope you enjoyed this blog post and found it useful. Happy coding! 😊

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