home / skills / secondsky / claude-skills / cloudflare-workers-observability

This skill enables Cloudflare Workers observability by guiding structured logging, metrics, tracing, and alert setups to improve debugging and reliability.

npx playbooks add skill secondsky/claude-skills --skill cloudflare-workers-observability

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SKILL.md
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---
name: workers-observability
description: Cloudflare Workers observability with logging, Analytics Engine, Tail Workers, metrics, and alerting. Use for monitoring, debugging, tracing, or encountering log parsing, metric aggregation, alert configuration errors.
---

# Cloudflare Workers Observability

Production-grade observability for Cloudflare Workers: logging, metrics, tracing, and alerting.

## Quick Start

```typescript
// Structured logging with context
export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
    const requestId = crypto.randomUUID();
    const logger = createLogger(requestId, env);

    try {
      logger.info('Request received', { method: request.method, url: request.url });

      const result = await handleRequest(request, env);

      logger.info('Request completed', { status: result.status });
      return result;
    } catch (error) {
      logger.error('Request failed', { error: error.message, stack: error.stack });
      throw error;
    }
  }
};

// Simple logger factory
function createLogger(requestId: string, env: Env) {
  return {
    info: (msg: string, data?: object) => console.log(JSON.stringify({ level: 'info', requestId, msg, ...data, timestamp: Date.now() })),
    error: (msg: string, data?: object) => console.error(JSON.stringify({ level: 'error', requestId, msg, ...data, timestamp: Date.now() })),
    warn: (msg: string, data?: object) => console.warn(JSON.stringify({ level: 'warn', requestId, msg, ...data, timestamp: Date.now() })),
  };
}
```

## Critical Rules

1. **Always use structured JSON logging** - Plain text logs are hard to parse and aggregate
2. **Include request context** - Request ID, method, path in every log entry
3. **Never log sensitive data** - Redact tokens, passwords, PII from logs
4. **Use appropriate log levels** - ERROR for failures, WARN for recoverable issues, INFO for operations
5. **Sample high-volume logs** - Use 1-10% sampling for request logs in production

## Observability Components

| Component | Purpose | When to Use |
|-----------|---------|-------------|
| `console.log` | Basic logging | Development, debugging |
| **Tail Workers** | Real-time log streaming | Production log aggregation |
| **Analytics Engine** | Custom metrics/analytics | Business metrics, performance tracking |
| **Logpush** | Log export to external services | Long-term storage, compliance |
| **Workers Trace Events** | Distributed tracing | Request flow debugging |

## Top 8 Errors Prevented

| Error | Symptom | Prevention |
|-------|---------|------------|
| Logs not appearing | No output in dashboard | Enable "Standard" logging in wrangler.jsonc |
| Log truncation | Messages cut off at 128KB | Chunk large payloads, use sampling |
| Tail Worker not receiving | No events processed | Check binding name matches wrangler.jsonc |
| Analytics Engine write fails | Data not recorded | Verify AE binding, check blobs format |
| PII in logs | Security/compliance violation | Implement redaction middleware |
| Missing request context | Can't correlate logs | Add requestId to all log entries |
| Log volume explosion | High costs, noise | Implement sampling for high-frequency events |
| Alerting gaps | Incidents not detected | Configure monitors for error rate thresholds |

## Logging Configuration

**wrangler.jsonc**:
```jsonc
{
  "name": "my-worker",
  "observability": {
    "enabled": true,
    "head_sampling_rate": 1 // 0-1, 1 = 100% of requests
  },
  "tail_consumers": [
    {
      "service": "log-aggregator", // Tail Worker name
      "environment": "production"
    }
  ],
  "analytics_engine_datasets": [
    {
      "binding": "ANALYTICS",
      "dataset": "my_worker_metrics"
    }
  ]
}
```

## Structured Logging Pattern

```typescript
interface LogEntry {
  level: 'debug' | 'info' | 'warn' | 'error';
  message: string;
  requestId: string;
  timestamp: number;
  // Contextual data
  method?: string;
  path?: string;
  status?: number;
  duration?: number;
  // Error details
  error?: {
    name: string;
    message: string;
    stack?: string;
  };
  // Custom fields
  [key: string]: unknown;
}

class Logger {
  constructor(private requestId: string, private baseContext: object = {}) {}

  private log(level: LogEntry['level'], message: string, data?: object) {
    const entry: LogEntry = {
      level,
      message,
      requestId: this.requestId,
      timestamp: Date.now(),
      ...this.baseContext,
      ...data,
    };

    // Redact sensitive fields
    const sanitized = this.redact(entry);

    const output = JSON.stringify(sanitized);
    level === 'error' ? console.error(output) : console.log(output);
  }

  private redact(entry: LogEntry): LogEntry {
    const sensitiveKeys = ['password', 'token', 'secret', 'authorization', 'cookie'];
    const redacted = { ...entry };

    for (const key of Object.keys(redacted)) {
      if (sensitiveKeys.some(s => key.toLowerCase().includes(s))) {
        redacted[key] = '[REDACTED]';
      }
    }
    return redacted;
  }

  info(message: string, data?: object) { this.log('info', message, data); }
  warn(message: string, data?: object) { this.log('warn', message, data); }
  error(message: string, data?: object) { this.log('error', message, data); }
  debug(message: string, data?: object) { this.log('debug', message, data); }
}
```

## Analytics Engine Usage

```typescript
interface Env {
  ANALYTICS: AnalyticsEngineDataset;
}

export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
    const start = Date.now();
    const url = new URL(request.url);

    try {
      const response = await handleRequest(request, env);

      // Write success metric
      env.ANALYTICS.writeDataPoint({
        blobs: [request.method, url.pathname, String(response.status)],
        doubles: [Date.now() - start], // Response time in ms
        indexes: [url.pathname.split('/')[1] || 'root'], // Index for fast queries
      });

      return response;
    } catch (error) {
      // Write error metric
      env.ANALYTICS.writeDataPoint({
        blobs: [request.method, url.pathname, 'error', error.message],
        doubles: [Date.now() - start],
        indexes: ['error'],
      });
      throw error;
    }
  }
};
```

## Tail Worker Pattern

```typescript
// tail-worker.ts - Receives logs from other workers
interface TailEvent {
  scriptName: string;
  event: {
    request?: { method: string; url: string };
    response?: { status: number };
  };
  logs: Array<{
    level: string;
    message: unknown[];
    timestamp: number;
  }>;
  exceptions: Array<{
    name: string;
    message: string;
    timestamp: number;
  }>;
  outcome: 'ok' | 'exception' | 'exceededCpu' | 'exceededMemory' | 'canceled';
  eventTimestamp: number;
}

export default {
  async tail(events: TailEvent[], env: Env): Promise<void> {
    for (const event of events) {
      // Filter and forward logs
      const errorLogs = event.logs.filter(l => l.level === 'error');
      const exceptions = event.exceptions;

      if (errorLogs.length > 0 || exceptions.length > 0) {
        // Send to external logging service
        await fetch(env.LOGGING_ENDPOINT, {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({
            scriptName: event.scriptName,
            timestamp: event.eventTimestamp,
            errors: errorLogs,
            exceptions,
            outcome: event.outcome,
          }),
        });
      }
    }
  }
};
```

## When to Load References

Load specific references based on the task:

- **Setting up logging?** → Load `references/logging.md` for structured logging patterns, log levels, redaction
- **Building custom metrics?** → Load `references/analytics-engine.md` for Analytics Engine SQL queries, data modeling
- **Implementing log aggregation?** → Load `references/tail-workers.md` for Tail Worker patterns, external service integration
- **Creating dashboards/tracking?** → Load `references/custom-metrics.md` for business metrics, performance tracking
- **Setting up alerts?** → Load `references/alerting.md` for error rate monitoring, PagerDuty/Slack integration

## Templates

| Template | Purpose | Use When |
|----------|---------|----------|
| `templates/logging-setup.ts` | Production logging class | Setting up new worker with logging |
| `templates/analytics-worker.ts` | Analytics Engine integration | Adding custom metrics |
| `templates/tail-worker.ts` | Complete Tail Worker | Building log aggregation pipeline |

## Scripts

| Script | Purpose | Command |
|--------|---------|---------|
| `scripts/setup-logging.sh` | Configure logging settings | `./setup-logging.sh` |
| `scripts/analyze-logs.sh` | Query and analyze logs | `./analyze-logs.sh --errors --last 1h` |

## Resources

- Workers Observability: https://developers.cloudflare.com/workers/observability/
- Analytics Engine: https://developers.cloudflare.com/analytics/analytics-engine/
- Tail Workers: https://developers.cloudflare.com/workers/observability/tail-workers/
- Logpush: https://developers.cloudflare.com/logs/get-started/

Overview

This skill provides production-grade observability for Cloudflare Workers, including structured logging, Analytics Engine metrics, Tail Workers for real-time streaming, and alerting guidance. It helps teams monitor, debug, and trace requests while preventing common log, metric, and alerting mistakes. The content is focused on practical patterns, configuration snippets, and prevention of the top observability errors.

How this skill works

The skill supplies logging factories and a Logger class that enforce structured JSON logs with request context and automatic redaction of sensitive fields. It shows how to write Analytics Engine datapoints for latency and error tracking, plus a Tail Worker pattern that forwards critical events to external logging services. Configuration examples (wrangler.jsonc) and operational rules for sampling, log levels, and trace events are included to ensure reliable aggregation and alerting.

When to use it

  • Adding structured, parseable logs to Workers
  • Instrumenting response times and business metrics with Analytics Engine
  • Streaming production logs in real time using Tail Workers
  • Exporting logs for long-term storage or compliance via Logpush
  • Setting up monitors and alerts for error rate or latency regressions

Best practices

  • Always emit structured JSON logs and include requestId, method, path, and timestamp
  • Redact or omit PII and sensitive keys (password, token, authorization, cookie) at log time
  • Use appropriate log levels and 1–10% sampling for very high-volume request logs
  • Configure wrangler observability settings and Tail Worker bindings to match names exactly
  • Write Analytics Engine datapoints with blobs, doubles, and indexes to enable fast queries

Example use cases

  • Instrumenting an edge API with requestId and timed Analytics Engine writes for SLA tracking
  • Deploying a Tail Worker that filters error logs and forwards exceptions to an external aggregator
  • Preventing log truncation by chunking large payloads and sampling in production
  • Creating monitors that alert on sustained error-rate thresholds and missing telemetry
  • Building dashboards from Analytics Engine indexes for per-path performance trends

FAQ

How do I avoid logging sensitive data?

Redact sensitive keys in your Logger by filtering fields containing password, token, secret, authorization, cookie and replace values with [REDACTED] before emitting logs.

Why are logs missing in the dashboard?

Confirm observability is enabled in wrangler.jsonc (standard logging) and ensure Tail Worker binding names match the configuration; also check sampling and head_sampling_rate settings.