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Prad Nukala
6406208b35
- 16 cross-shell utilities converted to sh/bash chezmoi executables: ai c2p cp-appid extract_agents fab gdc gh-sonr-org-migrate ghg-clone ghn gho git-summary launch_omp_livediff_overlay lc-elir mdcp mdp pj (ghg-clone not named ghg: unrelated cargo binary owns that name) - cwd/history helpers stay per-shell: yy c cd clip-last + ghg/ghn/gho cd-wrappers in bash/functions/ and zsh/functions/, loaded by rc files - fish keeps only shell-state functions; converted ones now hit PATH - cp-appid ignore gating moved to .local/bin path - dot_agents/skills: 82 skills materialized from dotstate storage so dot_claude skill symlinks resolve on fresh bootstrap; dotstate profile/symlinks.json released .agents/skills .bashrc .zshrc
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2.3 KiB
Analytics Engine Patterns
Use Cases
| Use Case | Key Metrics | Index On |
|---|---|---|
| API Metering | requests, bytes, compute_units | api_key |
| Feature Usage | feature, action, duration | user_id |
| Error Tracking | error_type, endpoint, count | customer_id |
| Performance | latency_ms, cache_status | endpoint |
| A/B Testing | variant, conversions | user_id |
API Metering (Billing)
env.ANALYTICS.writeDataPoint({
blobs: [pathname, method, status, tier],
doubles: [1, computeUnits, bytes, latencyMs],
indexes: [apiKey]
});
// Query: Monthly usage by customer
// SELECT index1 AS api_key, SUM(double2) AS compute_units
// FROM usage WHERE timestamp >= DATE_TRUNC('month', NOW()) GROUP BY index1
Error Tracking
env.ANALYTICS.writeDataPoint({
blobs: [endpoint, method, errorName, errorMessage.slice(0, 1000)],
doubles: [1, timeToErrorMs],
indexes: [customerId]
});
Performance Monitoring
env.ANALYTICS.writeDataPoint({
blobs: [pathname, method, cacheStatus, status],
doubles: [latencyMs, 1],
indexes: [userId]
});
// Query: P95 latency by endpoint
// SELECT blob1, quantile(0.95)(double1) AS p95_ms FROM perf GROUP BY blob1
Anti-Patterns
| ❌ Wrong | ✅ Correct |
|---|---|
await writeDataPoint() |
writeDataPoint() (fire-and-forget) |
indexes: [method] (low cardinality) |
blobs: [method], indexes: [userId] |
blobs: [JSON.stringify(obj)] |
Store ID in blob, full object in D1/KV |
| Write every request at 10M/min | Pre-aggregate per second |
| Query from Worker | Query from external service/API |
Best Practices
- Design schema upfront - Document blob/double/index assignments
- Always include count metric -
doubles: [latency, 1]for AVG calculations - Use enums for blobs - Consistent values like
Status.SUCCESS - Handle sampling - Use ratios (avg_latency = SUM(latency)/SUM(count))
- Test queries early - Validate schema before heavy writes
Schema Template
/**
* Dataset: my_metrics
*
* Blobs:
* blob1: endpoint, blob2: method, blob3: status
*
* Doubles:
* double1: latency_ms, double2: count (always 1)
*
* Indexes:
* index1: customer_id (high cardinality)
*/