Optimize performance by reducing the amount of computation performed, especially on data that won't appear in the final result. Apply these strategies:
Optimize performance by reducing the amount of computation performed, especially on data that won’t appear in the final result. Apply these strategies:
1) Filter early: Filter collections before performing expensive transformations like sorting
// Suboptimal: Sorts all elements, then removes private ones
functions.sortBy(KmFunction::name)
functions.removeIf { it.visibility.isPrivate }
// Optimized: Removes private elements first, then sorts only what's needed
functions.removeIf { it.visibility.isPrivate }
functions.sortBy(KmFunction::name)
2) Short-circuit common cases: Add early returns for special cases like empty collections or single elements
public fun <T> Array<out Array<out T>>.flatten(): List<T> {
if (isEmpty()) return emptyList() // Early return for common case
// Regular processing...
}
3) Cache expensive results: Use memoization for operations that may be called repeatedly with the same input
private val containsCache = mutableMapOf<IrDeclaration, Boolean>()
override fun containsDeclaration(declaration: IrDeclaration): Boolean =
containsCache.getOrPut(declaration) {
// Expensive computation here
}
4) Optimize for expected usage patterns: Special-case handle common scenarios based on benchmarks
// Optimization for common no-argument functions
if (parameters.isEmpty()) {
return reflectionCall {
caller.call(if (isSuspend) arrayOf(continuationArgument) else emptyArray()) as R
}
}
Always verify your optimizations with benchmarks to ensure they provide measurable benefits in real-world scenarios.
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