figma-load-scale
Load test Figma API integrations and plan for scale. Use when benchmarking API throughput, testing rate limit behavior, or planning capacity for high-volume Figma integrations. Trigger with phrases like "figma load test", "figma scale", "figma benchmark", "figma capacity", "figma throughput".
What this skill does
# Figma Load & Scale
## Overview
Test and plan for the throughput limits of your Figma API integration. Figma's rate limits use a leaky bucket algorithm -- this skill helps you find the bucket size for your plan tier and design your integration to stay within it.
## Prerequisites
- k6 load testing tool (`brew install k6` or `apt install k6`)
- Figma test PAT (do not load test with production token)
- A test Figma file (not your production design system)
## Instructions
### Step 1: k6 Load Test Script
```javascript
// figma-load-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Rate, Trend } from 'k6/metrics';
const figmaErrors = new Rate('figma_errors');
const figmaLatency = new Trend('figma_latency', true);
export const options = {
scenarios: {
// Test 1: Find your rate limit ceiling
rate_limit_probe: {
executor: 'constant-arrival-rate',
rate: 10, // 10 requests per second
timeUnit: '1s',
duration: '2m',
preAllocatedVUs: 5,
maxVUs: 20,
},
},
thresholds: {
figma_errors: ['rate<0.10'], // Less than 10% errors
figma_latency: ['p(95)<3000'], // P95 under 3 seconds
http_req_duration: ['p(99)<5000'], // P99 under 5 seconds
},
};
const PAT = __ENV.FIGMA_PAT;
const FILE_KEY = __ENV.FIGMA_FILE_KEY;
export default function () {
// Use a lightweight endpoint for rate limit testing
const res = http.get(
`https://api.figma.com/v1/files/${FILE_KEY}?depth=1`,
{
headers: { 'X-Figma-Token': PAT },
tags: { endpoint: 'files' },
}
);
figmaLatency.add(res.timings.duration);
const isError = res.status !== 200;
figmaErrors.add(isError);
check(res, {
'status is 200': (r) => r.status === 200,
'not rate limited': (r) => r.status !== 429,
'latency < 2s': (r) => r.timings.duration < 2000,
});
if (res.status === 429) {
const retryAfter = parseInt(res.headers['Retry-After'] || '60');
console.log(`Rate limited. Retry-After: ${retryAfter}s`);
sleep(retryAfter);
} else {
sleep(0.1); // 100ms between requests
}
}
```
### Step 2: Run Load Tests
```bash
# Probe rate limits
k6 run \
--env FIGMA_PAT="${FIGMA_PAT}" \
--env FIGMA_FILE_KEY="${FIGMA_FILE_KEY}" \
figma-load-test.js
# Export results to JSON for analysis
k6 run \
--env FIGMA_PAT="${FIGMA_PAT}" \
--env FIGMA_FILE_KEY="${FIGMA_FILE_KEY}" \
--out json=results.json \
figma-load-test.js
```
### Step 3: Capacity Planning
```typescript
interface FigmaCapacityPlan {
planTier: string;
measuredLimitPerMinute: number;
currentUsagePerMinute: number;
headroomPercent: number;
recommendation: string;
}
function planCapacity(
measuredLimit: number,
currentUsage: number,
planTier: string
): FigmaCapacityPlan {
const headroom = ((measuredLimit - currentUsage) / measuredLimit) * 100;
let recommendation: string;
if (headroom > 50) {
recommendation = 'Adequate capacity. Monitor monthly.';
} else if (headroom > 20) {
recommendation = 'Approaching limits. Implement caching and batching.';
} else {
recommendation = 'Near capacity. Upgrade plan or reduce request volume.';
}
return {
planTier,
measuredLimitPerMinute: measuredLimit,
currentUsagePerMinute: currentUsage,
headroomPercent: Math.round(headroom),
recommendation,
};
}
```
### Step 4: Scaling Strategies
```typescript
// Strategy 1: Request coalescing
// Multiple callers requesting the same file get a single API call
class RequestCoalescer {
private pending = new Map<string, Promise<any>>();
async get(key: string, fetcher: () => Promise<any>): Promise<any> {
if (this.pending.has(key)) {
return this.pending.get(key)!;
}
const promise = fetcher().finally(() => this.pending.delete(key));
this.pending.set(key, promise);
return promise;
}
}
const coalescer = new RequestCoalescer();
// 10 simultaneous requests for the same file = 1 API call
const results = await Promise.all(
Array(10).fill(null).map(() =>
coalescer.get(fileKey, () => figmaClient.getFile(fileKey))
)
);
// Strategy 2: Stagger requests across time
import PQueue from 'p-queue';
const figmaQueue = new PQueue({
concurrency: 3,
interval: 1000,
intervalCap: 5, // Max 5 requests per second
});
// Strategy 3: Pre-fetch during off-peak hours
// Run design token sync at 3 AM, cache results for the day
```
### Step 5: Benchmark Report Template
```markdown
## Figma API Benchmark Report
**Date:** YYYY-MM-DD
**Plan:** [Starter/Pro/Org/Enterprise]
**Seat:** [Full/Collab/Viewer]
### Rate Limit Findings
| Endpoint | Measured Limit/min | First 429 At | Retry-After |
|----------|-------------------|--------------|-------------|
| GET /v1/files/:key?depth=1 | ~30 | Request #31 | 60s |
| GET /v1/files/:key/nodes | ~30 | Request #32 | 60s |
| GET /v1/images/:key | ~20 | Request #21 | 60s |
### Latency
| Endpoint | P50 | P95 | P99 |
|----------|-----|-----|-----|
| /v1/files (depth=1) | 200ms | 500ms | 1200ms |
| /v1/files (full) | 800ms | 2000ms | 4000ms |
| /v1/images | 300ms | 800ms | 1500ms |
### Recommendations
- Cache file metadata (changes infrequently)
- Use webhooks instead of polling
- Batch node IDs in single requests
- Use `depth=1` unless full tree is needed
```
## Output
- k6 load test measuring actual rate limits
- Capacity plan with headroom analysis
- Scaling strategies implemented
- Benchmark report documented
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| All requests 429'd | Rate too aggressive | Start lower, ramp gradually |
| Inconsistent limits | Shared rate limit bucket | Other services using same token |
| k6 connection errors | Too many parallel VUs | Reduce `preAllocatedVUs` |
| Results vary between runs | Leaky bucket state | Wait 5min between test runs |
## Resources
- [k6 Documentation](https://grafana.com/docs/k6/)
- [Figma Rate Limits](https://developers.figma.com/docs/rest-api/rate-limits/)
- [p-queue](https://github.com/sindresorhus/p-queue)
## Next Steps
For reliability patterns, see `figma-reliability-patterns`.
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