compose-performance-audit

name	compose-performance-audit
description	Audit and improve Jetpack Compose runtime performance from code review and architecture. Use when asked to diagnose slow rendering, janky scrolling, excessive recompositions, or performance issues in Compose UI.

Compose Performance Audit

Overview

Audit Jetpack Compose view performance end-to-end, from instrumentation and baselining to root-cause analysis and concrete remediation steps.

Workflow Decision Tree

• If the user provides code, start with "Code-First Review."
• If the user only describes symptoms, ask for minimal code/context, then do "Code-First Review."
• If code review is inconclusive, go to "Guide the User to Profile" and ask for Layout Inspector output or Perfetto traces.

1. Code-First Review

Collect:

• Target Composable code.
• Data flow: state, remember, derived state, ViewModel connections.
• Symptoms and reproduction steps.

Focus on:

• Recomposition storms from unstable parameters or broad state changes.
• Unstable keys in LazyColumn/LazyRow (key churn, missing keys).
• Heavy work in composition (formatting, sorting, filtering, object allocation).
• Unnecessary recompositions (missing remember, unstable classes, lambdas).
• Large images without proper sizing or async loading.
• Layout thrash (deep nesting, intrinsic measurements, SubcomposeLayout misuse).

Provide:

• Likely root causes with code references.
• Suggested fixes and refactors.
• If needed, a minimal repro or instrumentation suggestion.

2. Guide the User to Profile

Explain how to collect data:

• Use Layout Inspector in Android Studio to see recomposition counts.
• Enable Recomposition Highlights in Compose tooling.
• Use Perfetto or System Trace for frame timing analysis.
• Check Macrobenchmark results for startup/scroll metrics.

Ask for:

• Layout Inspector screenshot showing recomposition counts.
• Perfetto trace or System Trace export.
• Device/OS/build configuration (debug vs release).

Important: Ensure profiling is done on a release build with R8 enabled. Debug builds have significant overhead.

3. Analyze and Diagnose

Prioritize likely Compose culprits:

• Recomposition storms from unstable parameters or broad state changes.
• Unstable keys in lazy lists (key churn, index-based keys).
• Heavy work in composition (formatting, sorting, object allocation).
• Missing remember causing recreations on every recomposition.
• Large images without Modifier.size() constraints.
• Unnecessary state reads in wrong composition phases.

Summarize findings with evidence from traces/Layout Inspector.

4. Remediate

Apply targeted fixes:

• Stabilize parameters: Use @Stable or @Immutable annotations on data classes.
• Stabilize keys: Use stable, unique IDs for LazyColumn/LazyRow items.
• Defer state reads: Use derivedStateOf, lambda-based modifiers, or Modifier.drawBehind.
• Remember expensive computations: Wrap in remember { } or remember(key) { }.
• Skip recomposition: Extract stable composables, use key() to control identity.
• Async image loading: Use Coil/Glide with proper sizing constraints.
• Reduce layout complexity: Flatten hierarchies, avoid deep nesting.

Common Code Smells (and Fixes)

Unstable lambda captures

// BAD: New lambda instance every recomposition
Button(onClick = { viewModel.doSomething(item) }) { ... }

// GOOD: Use remember or method reference
val onClick = remember(item) { { viewModel.doSomething(item) } }
Button(onClick = onClick) { ... }

Expensive work in composition

// BAD: Sorting on every recomposition
@Composable
fun ItemList(items: List<Item>) {
    val sorted = items.sortedBy { it.name } // Runs every recomposition
    LazyColumn { items(sorted) { ... } }
}

// GOOD: Use remember with key
@Composable
fun ItemList(items: List<Item>) {
    val sorted = remember(items) { items.sortedBy { it.name } }
    LazyColumn { items(sorted) { ... } }
}

Missing keys in LazyColumn

// BAD: Index-based identity (causes recomposition on list changes)
LazyColumn {
    items(items) { item -> ItemRow(item) }
}

// GOOD: Stable key-based identity
LazyColumn {
    items(items, key = { it.id }) { item -> ItemRow(item) }
}

Unstable data classes

// BAD: Unstable (contains List, which is not stable)
data class UiState(
    val items: List<Item>,
    val isLoading: Boolean
)

// GOOD: Mark as Immutable if truly immutable
@Immutable
data class UiState(
    val items: ImmutableList<Item>, // kotlinx.collections.immutable
    val isLoading: Boolean
)

Reading state too early

// BAD: State read during composition (recomposes whole tree)
@Composable
fun AnimatedBox(scrollState: ScrollState) {
    val offset = scrollState.value // Recomposes on every scroll
    Box(modifier = Modifier.offset(y = offset.dp)) { ... }
}

// GOOD: Defer state read to layout/draw phase
@Composable
fun AnimatedBox(scrollState: ScrollState) {
    Box(modifier = Modifier.offset {
        IntOffset(0, scrollState.value) // Read in layout phase
    }) { ... }
}

Object allocation in composition

// BAD: Creates new Modifier chain every recomposition
Box(modifier = Modifier.padding(16.dp).background(Color.Red))

// GOOD for dynamic modifiers: Remember the modifier
val modifier = remember { Modifier.padding(16.dp).background(Color.Red) }
Box(modifier = modifier)

Stability Checklist

Type	Stable by Default?	Fix
Primitives (Int, String, Boolean)	Yes	N/A
data class with stable fields	Yes*	Ensure all fields are stable
List, Map, Set	No	Use ImmutableList from kotlinx
Classes with var properties	No	Use @Stable if externally stable
Lambdas	No	Use remember { }

5. Verify

Ask the user to:

• Re-run Layout Inspector and compare recomposition counts.
• Run Macrobenchmark and compare frame timing.
• Test on a real device with release build.

Summarize the delta (recomposition count, frame drops, jank) if provided.

Outputs

Provide:

• A short metrics table (before/after if available).
• Top issues (ordered by impact).
• Proposed fixes with estimated effort.

References

• Jetpack Compose Performance
• Compose Stability Explained
• Debugging Recomposition
• Macrobenchmark