
mapbox-web-performance-patterns
Performance optimization patterns for Mapbox GL JS web applications. Covers initialization waterfalls, bundle size, rendering performance, memory management, and web optimization. Prioritized by impact on user experience.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mapbox/mapbox-agent-skills --skill mapbox-web-performance-patterns- 01
Install
Run the command in your project. The CLI asks which agents to install for.
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Describe the task
Ask your agent for something this skill covers. It loads mapbox-web-performance-patterns automatically when relevant.
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Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Mapbox Performance Patterns Skill
This skill provides performance optimization guidance for building fast, efficient Mapbox applications. Patterns are prioritized by impact on user experience, starting with the most critical improvements.
Performance philosophy: These aren't micro-optimizations. They show up as waiting time, jank, and repeat costs that hit every user session.
Priority Levels
Performance issues are prioritized by their impact on user experience:
- 🔴 Critical (Fix First): Directly causes slow initial load or visible jank
- 🟡 High Impact: Noticeable delays or increased resource usage
- 🟢 Optimization: Incremental improvements for polish
🔴 Critical: Eliminate Initialization Waterfalls
Problem: Sequential loading creates cascading delays where each resource waits for the previous one.
Note: Modern bundlers (Vite, Webpack, etc.) and ESM dynamic imports automatically handle code splitting and library loading. The primary waterfall to eliminate is data loading - fetching map data sequentially instead of in parallel with map initialization.
Anti-Pattern: Sequential Data Loading
// ❌ BAD: Data loads AFTER map initializes
async function initMap() {
const map = new mapboxgl.Map({
container: 'map',
accessToken: MAPBOX_TOKEN,
style: 'mapbox://styles/mapbox/streets-v12'
});
// Wait for map to load, THEN fetch data
map.on('load', async () => {
const data = await fetch('/api/data'); // Waterfall!
map.addSource('data', { type: 'geojson', data: await data.json() });
});
}
Timeline: Map init (0.5s) → Data fetch (1s) = 1.5s total
Solution: Parallel Data Loading
// ✅ GOOD: Data fetch starts immediately
async function initMap() {
// Start data fetch immediately (don't wait for map)
const dataPromise = fetch('/api/data').then((r) => r.json());
const map = new mapboxgl.Map({
container: 'map',
accessToken: MAPBOX_TOKEN,
style: 'mapbox://styles/mapbox/streets-v12'
});
// Data is ready when map loads
map.on('load', async () => {
const data = await dataPromise;
map.addSource('data', { type: 'geojson', data });
map.addLayer({
id: 'data-layer',
type: 'circle',
source: 'data'
});
});
}
Timeline: Max(map init, data fetch) = ~1s total
Set Precise Initial Viewport
// ✅ Set exact center/zoom so the map fetches the right tiles immediately
const map = new mapboxgl.Map({
container: 'map',
style: 'mapbox://styles/mapbox/streets-v12',
center: [-122.4194, 37.7749],
zoom: 13
});
// Use 'idle' to know when the initial viewport is fully rendered
// (all tiles, sprites, and other resources are loaded; no transitions in progress)
map.once('idle', () => {
console.log('Initial viewport fully rendered');
});
If you know the exact area users will see first, setting center and zoom upfront avoids the map starting at a default view and then panning/zooming to the target, which wastes tile fetches.
Defer Non-Critical Features
// ✅ Load critical features first, defer others
const map = new mapboxgl.Map({
/* config */
});
map.on('load', () => {
// 1. Add critical layers immediately
addCriticalLayers(map);
// 2. Defer secondary features
// Note: Standard style 3D buildings can be toggled via config:
// map.setConfigProperty('basemap', 'show3dObjects', false);
requestIdleCallback(
() => {
addTerrain(map);
addCustom3DLayers(map); // For classic styles with custom fill-extrusion layers
},
{ timeout: 2000 }
);
// 3. Defer analytics and non-visual features
setTimeout(() => {
initializeAnalytics(map);
}, 3000);
});
Impact: Significant reduction in time-to-interactive, especially when deferring terrain and 3D layers
🔴 Critical: Optimize Initial Bundle Size
Problem: Large bundles delay time-to-interactive on slow networks.
Note: Modern bundlers (Vite, Webpack, etc.) automatically handle code splitting for framework-based applications. The guidance below is most relevant for optimizing what gets bundled and when.
Style JSON Bundle Impact
// ❌ BAD: Inline massive style JSON (can be 500+ KB)
const style = {
version: 8,
sources: {
/* 100s of lines */
},
layers: [
/* 100s of layers */
]
};
// ✅ GOOD: Reference Mapbox-hosted styles
const map = new mapboxgl.Map({
style: 'mapbox://styles/mapbox/streets-v12' // Fetched on demand
});
// ✅ OR: Store large custom styles externally
const map = new mapboxgl.Map({
style: '/styles/custom-style.json' // Loaded separately
});
Impact: Reduces initial bundle by 30-50% when moving from inlined to hosted styles
🟡 High Impact: Optimize Marker Count
Problem: Too many markers causes slow rendering and interaction lag.
Performance Thresholds
- < 100 markers: HTML markers OK (Marker class)
- 100-10,000 markers: Use symbol layers (GPU-accelerated)
- 10,000+ markers: Clustering recommended
- 100,000+ markers: Vector tiles with server-side clustering
Anti-Pattern: Thousands of HTML Markers
// ❌ BAD: 5,000 HTML markers = 5+ second render, janky pan/zoom
restaurants.forEach((restaurant) => {
const marker = new mapboxgl.Marker()
.setLngLat([restaurant.lng, restaurant.lat])
.setPopup(new mapboxgl.Popup().setHTML(restaurant.name))
.addTo(map);
});
Result: 5,000 DOM elements, slow interactions, high memory
Solution: Use Symbol Layers (GeoJSON)
// ✅ GOOD: GPU-accelerated rendering, smooth at 10,000+ features
map.addSource('restaurants', {
type: 'geojson',
data: {
type: 'FeatureCollection',
features: restaurants.map((r) => ({
type: 'Feature',
geometry: { type: 'Point', coordinates: [r.lng, r.lat] },
properties: { name: r.name, type: r.type }
}))
}
});
map.addLayer({
id: 'restaurants',
type: 'symbol',
source: 'restaurants',
layout: {
'icon-image': 'restaurant',
'icon-size': 0.8,
'text-field': ['get', 'name'],
'text-size': 12,
'text-offset': [0, 1.5],
'text-anchor': 'top'
}
});
// Click handler (one listener for all features)
map.on('click', 'restaurants', (e) => {
const feature = e.features[0];
new mapboxgl.Popup().setLngLat(feature.geometry.coordinates).setHTML(feature.properties.name).addTo(map);
});
Performance: 10,000 features render in <100ms
Solution: Clustering for High Density
// ✅ GOOD: 50,000 markers → ~500 clusters at low zoom
map.addSource('restaurants', {
type: 'geojson',
data: restaurantsGeoJSON,
cluster: true,
clusterMaxZoom: 14, // Stop clustering at zoom 15
clusterRadius: 50 // Radius relative to tile dimensions (512 = full tile width)
});
// Cluster circle layer
map.addLayer({
id: 'clusters',
type: 'circle',
source: 'restaurants',
filter: ['has', 'point_count'],
paint: {
'circle-color': ['step', ['get', 'point_count'], '#51bbd6', 100, '#f1f075', 750, '#f28cb1'],
'circle-radius': ['step', ['get', 'point_count'], 20, 100, 30, 750, 40]
}
});
// Cluster count label
map.addLayer({
id: 'cluster-count',
type: 'symbol',
source: 'restaurants',
filter: ['has', 'point_count'],
layout: {
'text-field': '{point_count_abbreviated}',
'text-size': 12
}
});
// Individual point layer
map.addLayer({
id: 'unclustered-point',
type: 'circle',
source: 'restaurants',
filter: ['!', ['has', 'point_count']],
paint: {
'circle-color': '#11b4da',
'circle-radius': 6
}
});
Impact: 50,000 markers at 60 FPS with smooth interaction
Summary: Performance Checklist
When building a Mapbox application, verify these optimizations in order:
🔴 Critical (Do First)
- Load map library and data in parallel (eliminate waterfalls)
- Use dynamic imports for map code (reduce initial bundle)
- Defer non-critical features (terrain, custom 3D layers, analytics)
- Use symbol layers for > 100 markers (not HTML markers)
- Implement viewport-based data loading for large datasets
🟡 High Impact
- Debounce/throttle map event handlers (geocode inputs,
moveend) - Optimize queryRenderedFeatures with layers filter and bounding box
- Use GeoJSON for < 5 MB, vector tiles for > 20 MB
- Always call map.remove() on cleanup in SPAs / page teardown
- Attach
map.on('error', …)(or visible error UI) so style/tile/token failures are not silent - Reuse popup instances (don't create on every interaction)
- Use feature state instead of dynamic layers for hover/selection
- Cluster demos: generate enough points to stress clustering (thousands, not a few hundred)
Agent anti-pattern: happy-path only
First-pass agent code often ships a map with no map.on('error'), no map.remove(), and a tiny point set that never exercises cluster: true. Production demos need error visibility, teardown, and realistic scale.
🟢 Optimization
- Consolidate multiple layers with data-driven styling
- Add mobile-specific optimizations (circle layers, disabled rotation)
- Set minzoom/maxzoom on layers to avoid rendering at irrelevant zoom levels
- Avoid enabling preserveDrawingBuffer or antialias unless needed
Measurement
// Measure initial load time
console.time('map-load');
map.on('load', () => {
console.timeEnd('map-load');
// isStyleLoaded() returns true when style, sources, tiles, sprites, and models are all loaded
console.log('Style loaded:', map.isStyleLoaded());
});
// Monitor frame rate
let frameCount = 0;
map.on('render', () => frameCount++);
setInterval(() => {
console.log('FPS:', frameCount);
frameCount = 0;
}, 1000);
// Check memory usage (Chrome DevTools -> Performance -> Memory)
Target metrics:
- Time to Interactive: < 2 seconds on 3G
- Frame Rate: 60 FPS during pan/zoom
- Memory Growth: < 10 MB per hour of usage
- Bundle Size: < 500 KB initial (map lazy-loaded)
Reference Files
For detailed patterns on specific topics, load the corresponding reference file:
references/data-loading.md— GeoJSON vs Vector Tiles decision matrix, viewport-based loading, progressive loading, vector tiles for large datasetsreferences/interactions.md— Debounce/throttle events, optimize feature queries, batch DOM updatesreferences/memory.md— Map cleanup patterns, popup/marker reuse, feature state vs dynamic layersreferences/mobile.md— Device detection, mobile-optimized layers, touch interaction, constructor optionsreferences/layers-styles.md— Consolidate layers with data-driven styling, simplify expressions, zoom-based visibility
FAQ
What does the mapbox-web-performance-patterns skill do?
Performance optimization patterns for Mapbox GL JS web applications. Covers initialization waterfalls, bundle size, rendering performance, memory management, and web optimization. Prioritized by impact on user experience.
How do I install mapbox-web-performance-patterns?
Run npx skills add https://github.com/mapbox/mapbox-agent-skills --skill mapbox-web-performance-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/mapbox-web-performance-patterns/ (personal) or .claude/skills/mapbox-web-performance-patterns/ (project).
Which AI agents can use mapbox-web-performance-patterns?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is mapbox-web-performance-patterns safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains mapbox-web-performance-patterns?
It is published by mapbox in mapbox/mapbox-agent-skills, and it is an official skill from the maker of the technology it covers. It has 2,474 recorded installs.
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