tools-only Analytics Engine Gotchas
Critical Issues
Sampling at High Volumes
Problem: Queries return fewer points than written at >1M writes/min.
Solution:
// Pre-aggregate before writing
let buffer = { count: 0, total: 0 }
buffer.count++
buffer.total += value
// Write once per second instead of per request
if (Date.now() % 1000 === 0) {
env.ANALYTICS.writeDataPoint({ doubles: [buffer.count, buffer.total] })
}
Detection: npx wrangler tail → look for "sampling enabled"
writeDataPoint Returns void
// ❌ Pointless await
await env.ANALYTICS.writeDataPoint({...});
// ✅ Fire-and-forget
env.ANALYTICS.writeDataPoint({...});
Writes can fail silently. Check tail logs.
Index vs Blob
| Cardinality | Use | Example |
|---|---|---|
| Millions | Index | user_id, api_key |
| Hundreds | Blob | endpoint, status_code, country |
// ✅ Correct
{ blobs: [method, path, status], indexes: [userId] }
Can't Query from Workers
Query API requires HTTP auth. Use external service or cache in KV/D1.
No Custom Timestamps
Auto-generated at write time. Store original in blob if needed.
Common Errors
| Error | Fix |
|---|---|
| Binding not found | Check wrangler.jsonc, redeploy |
| No data in query | Wait 30s; check dataset name; check time range |
| Query timeout | Add time filter; use index for filtering |
Limits
| Resource | Limit |
|---|---|
| Blobs per point | 20 |
| Doubles per point | 20 |
| Indexes per point | 1 |
| Blob/Index size | 16KB |
| Write rate (no sampling) | ~1M/min |
| Retention | 90 days |
| Query timeout | 30s |
Best Practices
✅ Pre-aggregate at high volumes
✅ Use index for high-cardinality (millions)
✅ Always include time filter in queries
✅ Design schema before coding
❌ Don't await writeDataPoint
❌ Don't use index for low-cardinality
❌ Don't query without time range
❌ Don't assume all writes succeed