removing queues example

Author: codyde

docs/platforms/javascript/common/tracing/span-metrics/examples.mdx

@@ -330,99 +330,3 @@ The frontend span tracks the user's checkout experience, while the backend span
 - Monitor inventory availability impact on conversions
 - Measure end-to-end order completion times
 - Identify friction points in the user experience
-
-## Job Scheduling and Processing Pipeline
-
-**Challenge:** Understanding performance and reliability of distributed job processing systems, from job creation through completion.
-
-**Solution:** Comprehensive tracking of job lifecycle across scheduling, queueing, and processing stages.
-
-**Frontend Instrumentation:**
-```javascript
-// Client-side job submission and monitoring
-Sentry.startSpan(
-  {
-    name: 'Job Submission Flow',
-    op: 'job.client',
-    attributes: {
-      // Job configuration
-      'job.type': 'video_transcoding',
-      'job.priority': 'high',
-      'job.estimated_duration_ms': 300000,
-      
-      // Input metrics
-      'input.size_bytes': 52428800,  // 50MB
-      'input.format': 'mp4',
-      'input.segments': 5,
-      
-      // Client-side scheduling
-      'schedule.requested_start': '2024-03-15T10:00:00Z',
-      'schedule.deadline': '2024-03-15T11:00:00Z',
-      
-      // Progress monitoring
-      'monitor.polling_interval_ms': 5000,
-      'monitor.status_updates_received': 12,
-      'monitor.last_progress_percent': 45
-    }
-  },
-  async () => {
-    // Job submission and progress tracking implementation
-  }
-);
-```
-
-**Backend Instrumentation:**
-```javascript
-// Server-side job processing
-Sentry.startSpan(
-  {
-    name: 'Job Processing Pipeline',
-    op: 'job.server',
-    attributes: {
-      // Queue metrics
-      'queue.name': 'video-processing',
-      'queue.provider': 'redis',
-      'queue.length_at_enqueue': 23,
-      'queue.wait_time_ms': 45000,
-      
-      // Worker metrics
-      'worker.id': 'worker-pod-123',
-      'worker.current_load': 0.75,
-      'worker.memory_usage_mb': 1024,
-      
-      // Processing stages
-      'processing.stages_completed': ['download', 'transcode', 'thumbnail'],
-      'processing.stage_durations_ms': {
-        'download': 12000,
-        'transcode': 180000,
-        'thumbnail': 5000
-      },
-      
-      // Resource utilization
-      'resource.cpu_percent': 85,
-      'resource.gpu_utilization': 0.92,
-      'resource.memory_peak_mb': 2048,
-      
-      // Job outcome
-      'outcome.status': 'completed',
-      'outcome.retry_count': 0,
-      'outcome.output_size_bytes': 31457280  // 30MB
-    }
-  },
-   async () => {
-    // Job processing implementation
-   }
- )
-);
-```
-
-**How the Trace Works Together:**
-The frontend span tracks job submission and monitoring, while the backend span captures queue management and processing details. The distributed trace provides visibility into the entire job lifecycle, enabling you to:
-
-- Monitor end-to-end job processing times and success rates
-- Track queue health and worker resource utilization
-- Identify bottlenecks in specific processing stages
-- Analyze job scheduling efficiency and queue wait times
-- Optimize resource allocation based on job characteristics
-
-For more information about implementing these examples effectively, see our <PlatformLink to="/tracing/span-metrics/">Span Metrics guide</PlatformLink> which includes detailed best practices and implementation guidelines.