Boosting Compose Multiplatform Performance in Jetpack Compose

Jetpack Compose has revolutionized Android UI development with its declarative approach, making UI creation more intuitive and efficient. Now, with Compose Multiplatform, the power of Compose extends beyond Android to other platforms like iOS, desktop (JVM), and web. However, ensuring optimal performance across these diverse platforms is crucial. This article delves into techniques to maximize Compose Multiplatform Performance in Jetpack Compose applications.

What is Compose Multiplatform?

Compose Multiplatform is a declarative UI framework by JetBrains that enables sharing of UI code across multiple platforms including Android, iOS, desktop (JVM), and web. Built on top of Jetpack Compose, it allows developers to write UI logic once and deploy it on different platforms with minimal platform-specific adaptations.

Why Performance Matters in Compose Multiplatform

Performance is critical for a seamless user experience, especially in multiplatform apps. Inefficient code can lead to:

• Laggy UI
• High battery consumption
• Poor user ratings

Optimizing performance ensures your Compose Multiplatform application delivers a smooth, responsive, and enjoyable experience across all supported platforms.

Strategies for Optimizing Compose Multiplatform Performance

1. Understanding Compose Compiler

The Compose Compiler plays a crucial role in transforming composable functions into optimized UI instructions. To make the most of the compiler, follow these guidelines:

Composable Function Design

• Keep Composable Functions Small and Focused: Smaller functions are easier for the compiler to optimize. Break down complex UIs into smaller, reusable composables.
• Minimize State Readability: Only read the state variables that the composable function directly uses. Reading unnecessary state can trigger unwanted recompositions.

@Composable
fun UserProfileCard(user: User) {
    Column {
        ProfileImage(imageUrl = user.profileImageUrl)
        UserName(name = user.name)
        UserBio(bio = user.bio)
    }
}

@Composable
fun ProfileImage(imageUrl: String) {
    // Display user profile image
}

@Composable
fun UserName(name: String) {
    // Display user name
}

@Composable
fun UserBio(bio: String) {
    // Display user bio
}

2. Optimize Recomposition

Recomposition is the process of re-executing composable functions when their input data changes. Optimizing recomposition involves reducing the number of unnecessary recompositions.

Using remember and rememberSaveable

Use remember to cache expensive calculations and state that doesn’t need to be saved across configuration changes. Use rememberSaveable for state that should survive configuration changes (e.g., screen rotations).

import androidx.compose.runtime.*

@Composable
fun ExpensiveCalculation() {
    val result = remember { performExpensiveCalculation() }
    Text("Result: $result")
}

fun performExpensiveCalculation(): Int {
    // Simulate an expensive calculation
    Thread.sleep(200)
    return 42
}

Using derivedStateOf

derivedStateOf creates a derived state that is only updated when the source state changes, preventing unnecessary recompositions.

import androidx.compose.runtime.*

@Composable
fun SearchBar(searchText: String, onSearchTextChanged: (String) -> Unit) {
    val isSearchTextEmpty by remember(searchText) {
        derivedStateOf { searchText.isEmpty() }
    }

    Column {
        TextField(
            value = searchText,
            onValueChange = onSearchTextChanged,
            label = { Text("Search") }
        )
        if (isSearchTextEmpty) {
            Text("Enter search text")
        }
    }
}

Using SnapshotStateList

When dealing with lists, using SnapshotStateList for state management helps the Compose runtime track changes efficiently.

import androidx.compose.runtime.*
import androidx.compose.runtime.snapshots.SnapshotStateList

@Composable
fun ItemList() {
    val items = remember { mutableStateListOf("Item 1", "Item 2", "Item 3") }
    Column {
        items.forEach { item ->
            Text(item)
        }
    }
}

3. Smart Use of Modifier

Modifier instances can cause recompositions if they are recreated on every composition. Using static modifiers and avoiding inline lambda expressions can improve performance.

Avoid Inline Lambda Expressions in Modifiers

Use predefined modifiers or create extension functions to reuse modifiers, rather than defining them inline.

import androidx.compose.foundation.background
import androidx.compose.foundation.layout.padding
import androidx.compose.ui.graphics.Color
import androidx.compose.ui.unit.dp
import androidx.compose.ui.Modifier

// Bad: Inline lambda expression
@Composable
fun BadExample() {
    Text(
        text = "Hello",
        modifier = Modifier
            .padding(8.dp)
            .background(Color.LightGray)
    )
}

// Good: Using predefined modifiers
val myModifier = Modifier
    .padding(8.dp)
    .background(Color.LightGray)

@Composable
fun GoodExample() {
    Text(
        text = "Hello",
        modifier = myModifier
    )
}

4. Optimizing Image Loading

Loading and displaying images efficiently is critical, especially on platforms with limited resources like mobile devices. Consider the following techniques:

Using Caching

Implement caching mechanisms to store and retrieve images from memory or disk to reduce network requests.

import androidx.compose.runtime.*
import androidx.compose.ui.graphics.ImageBitmap
import androidx.compose.ui.graphics.asImageBitmap
import androidx.compose.ui.platform.LocalContext
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.withContext
import java.io.File
import java.net.URL
import javax.imageio.ImageIO

@Composable
fun LoadImage(imageUrl: String): ImageBitmap? {
    val context = LocalContext.current
    val cacheDir = context.cacheDir
    val imageName = imageUrl.substringAfterLast("/")
    val cachedImageFile = File(cacheDir, imageName)

    val imageBitmapState = remember { mutableStateOf(null) }

    LaunchedEffect(imageUrl) {
        withContext(Dispatchers.IO) {
            val imageBitmap = if (cachedImageFile.exists()) {
                ImageIO.read(cachedImageFile).asImageBitmap()
            } else {
                try {
                    val url = URL(imageUrl)
                    val image = ImageIO.read(url)
                    ImageIO.write(image, "png", cachedImageFile)
                    image.asImageBitmap()
                } catch (e: Exception) {
                    e.printStackTrace()
                    null
                }
            }
            imageBitmapState.value = imageBitmap
        }
    }

    return imageBitmapState.value
}

Downsampling Images

Load images at the required display size to avoid unnecessary memory usage.

import androidx.compose.ui.graphics.ImageBitmap
import androidx.compose.ui.res.loadImageBitmap
import androidx.compose.ui.platform.LocalContext
import java.io.InputStream

@Composable
fun LoadDownsampledImage(resourceId: Int, targetWidth: Int, targetHeight: Int): ImageBitmap {
    val context = LocalContext.current
    return remember(resourceId, targetWidth, targetHeight) {
        val options = BitmapFactory.Options().apply {
            inJustDecodeBounds = true
            context.resources.openRawResource(resourceId).use { input ->
                BitmapFactory.decodeStream(input, null, this)
            }

            inSampleSize = calculateInSampleSize(outWidth, outHeight, targetWidth, targetHeight)
            inJustDecodeBounds = false
        }

        context.resources.openRawResource(resourceId).use { input ->
            BitmapFactory.decodeStream(input, null, options)?.asImageBitmap() ?: error("Failed to decode image")
        }
    }
}

fun calculateInSampleSize(width: Int, height: Int, reqWidth: Int, reqHeight: Int): Int {
    var inSampleSize = 1
    if (height > reqHeight || width > reqWidth) {
        val halfHeight = height / 2
        val halfWidth = width / 2

        while (halfHeight / inSampleSize >= reqHeight && halfWidth / inSampleSize >= reqWidth) {
            inSampleSize *= 2
        }
    }
    return inSampleSize
}

5. Managing Concurrency

Concurrency in Compose Multiplatform apps can introduce performance issues if not handled properly. Use Kotlin Coroutines effectively to manage background tasks without blocking the main thread.

Using LaunchedEffect and rememberCoroutineScope

Use LaunchedEffect for performing suspend functions within composables and rememberCoroutineScope to manage the lifecycle of coroutines.

import androidx.compose.runtime.*
import kotlinx.coroutines.delay
import kotlinx.coroutines.launch

@Composable
fun MyComposable() {
    val coroutineScope = rememberCoroutineScope()
    var text by remember { mutableStateOf("Initial Text") }

    LaunchedEffect(key1 = true) {
        coroutineScope.launch {
            delay(1000)
            text = "Updated Text"
        }
    }

    Text(text = text)
}

6. Platform-Specific Optimizations

Different platforms may require specific optimizations to achieve peak performance.

Android

• Use Android Profiler to identify performance bottlenecks.
• Optimize layouts and reduce overdraw.
• Minimize allocations in tight loops.

iOS

• Leverage Instruments for profiling.
• Ensure efficient memory management with Kotlin/Native.

Desktop (JVM)

• Optimize JVM settings for better performance.
• Use efficient data structures and algorithms.

7. Monitoring and Profiling

Regular monitoring and profiling are essential to identify performance issues early in the development cycle. Tools like Android Profiler, Instruments (iOS), and JVM profiling tools can provide valuable insights into your application’s performance.

Conclusion

Optimizing Compose Multiplatform Performance involves a combination of best practices in Jetpack Compose, smart usage of Kotlin Coroutines, and platform-specific optimizations. By focusing on efficient recomposition, optimizing image loading, managing concurrency, and monitoring performance, you can ensure that your Compose Multiplatform applications deliver a smooth and responsive user experience across all platforms. Regular profiling and continuous optimization are key to maintaining high performance as your application evolves.