Monitoring

Worker Health

Workers report their health status via Redis heartbeat.

Check Worker Status

# List all workers
redis-cli KEYS "asynq:workers:*"

# Get worker details
redis-cli HGETALL asynq:workers:worker-abc123

The heartbeat hash includes:

Field	Description
started	Worker start timestamp
healthy	true if Python process is running
queue	Queue being processed
active_task	Currently processing task ID (if any)

Degraded State

When healthy: false:

• The supervised Python process has crashed
• Worker won't process new tasks
• Manual restart is required

Queue Metrics

Pending Tasks

# Count pending tasks
redis-cli LLEN asynq:inference:default:pending

# List pending task IDs
redis-cli LRANGE asynq:inference:default:pending 0 -1

Active Tasks

# Count active tasks
redis-cli LLEN asynq:inference:default:active

# List active task IDs
redis-cli LRANGE asynq:inference:default:active 0 -1

Task Inspection

View Task Data

redis-cli HGETALL asynq:t:task-id-here

View Task Result

redis-cli GET asynq:result:task-id-here

Prometheus Metrics

For production monitoring, consider adding Prometheus metrics to your worker:

// Example: Expose metrics endpoint
http.Handle("/metrics", promhttp.Handler())
go http.ListenAndServe(":9090", nil)

Common metrics to track:

• runqy_tasks_processed_total — Counter of processed tasks
• runqy_task_duration_seconds — Histogram of task duration
• runqy_worker_healthy — Gauge (1 = healthy, 0 = unhealthy)
• runqy_queue_pending_tasks — Gauge of pending tasks per queue

Alerting

Set up alerts for:

• Worker unhealthy: runqy_worker_healthy == 0
• High queue depth: runqy_queue_pending_tasks > threshold
• Task failures: rate(runqy_tasks_failed_total[5m]) > threshold
• Slow tasks: histogram_quantile(0.95, runqy_task_duration_seconds) > threshold