Optimizing composition techniques can improve performance in Jetpack Compose.
Newer versions of Compose offer performance improvements and ongoing architectural enhancements.
Modifying the modifier system in Compose can significantly improve performance.
Deep dives
Optimizing Performance with Efficient Composition Techniques
App developers can take specific actions to enhance performance in Jetpack Compose. One technique is to move certain computations from composition time to layout or drawing time. For example, instead of using the 'box with constraints' composable function, a custom layout may be more efficient and reduce the number of passes during UI creation. Communication between parent and child composable functions can be achieved using the 'parent data modifier' to optimize weight assignment and stretching. By embracing these efficient composition techniques, app developers can improve the overall performance of their Compose-based apps.
Focus on Initial Composition Time and Ongoing Optimizations
While performance optimizations during recomposition and state changes have been a primary focus for Jetpack Compose, recent attention has shifted towards the initial composition time. App developers can benefit from upgrading to newer versions of Compose to leverage improvements in the initial composition cost. Moreover, ongoing architectural improvements and closer scrutiny of performance issues show similarities to the lengthy process of fine-tuning the view system in the past. Compose aims to expedite the identification and resolution of performance bottlenecks through continuous improvements and user feedback.
Modifying the Modifier System for Better Performance
Modifying the modifier system in Jetpack Compose can yield significant performance improvements. By revisiting the existing modifier composed API, which allows composition of state and model effects, the Compose team noticed certain modifiers, such as 'clickable', to be more expensive than expected. To address this, a new system called modifier.node was introduced, which improved modifiers' efficiency, resulting in performance gains. These changes, along with other compiler optimizations, led to reduced composition costs, especially in cases where modifier chains are long. The team continues to explore annotation-based optimizations and potential collaborations with build tools to further enhance performance.
Performance optimizations in Compose
The podcast episode discusses various performance optimizations in Compose. One key point is the importance of optimizing the code at the library level so that app developers don't have to worry about low-level optimizations like auto boxing. The goal is for developers to be able to focus on writing code without having to think about performance concerns. However, it is also important for developers to be aware of performance and use tools like memory profilers and bytecode decompilation to understand and analyze performance. Another key point is the impact of debugging on performance in Compose. Debug performance has been a challenge, but recent improvements, such as turning off live literals and using live edit, have made debug performance more reasonable. The podcast also mentions the importance of testing performance in the same environment that customers will experience and recommends using the macro benchmark framework for startup performance analysis.
Using Kotlin to balance abstraction and performance
The podcast discusses how Kotlin can be used to balance abstraction and performance. It highlights how Kotlin provides tools to create nice APIs that are easy to use and understand, but also allow for performance optimization when needed. Examples include using value classes and masking and shifting to optimize memory usage, overloading operators on arrays to avoid unnecessary object creation, and accepting parameters in functions to allow for pre-allocated data structures. The podcast emphasizes the importance of giving developers the ability to care about performance and make informed decisions based on their specific use cases. It also mentions the challenges that arise when working with certain APIs and data structures, and the ongoing efforts to minimize the cost of abstractions in order to reduce performance overhead.
This time, Romain, Tor, and Chet talk with Leland Richardson, George Mount, and Chuck Jazdzewski from the Jetpack Compose team about performance. The team has been looking at performance issues recently and discusses what they’ve found, what gotchas lie in wait for library developers, what tools and compilers can magically handle for you... and what they can’t. Tune in to learn about why we worry about autoboxing (and why you probably shouldn’t).
Foreground: Romain, Tor, George, and Chuck Background (on the monitor): Chet, Leland, and Cody (audio engineer/producer), plus another view of the Studio with Romain, Tor, George and Chuck again, for your recursive pleasure.
