Job Statistics

Get Job Statistics

Retrieve execution statistics and resource usage for a job.

When to Use

• Monitoring resource consumption
• Optimizing process configurations
• Capacity planning and resource allocation
• Performance analysis and benchmarking
• Identifying resource bottlenecks
• Cost estimation for cloud resources

Parameters

Required

• job_id (string): Job identifier

CLI Usage

# Get job statistics
weaver statistics -u $WEAVER_URL -j a1b2c3d4-e5f6-7890-abcd-ef1234567890

# Compare statistics for multiple jobs
for job in $(weaver jobs -u $WEAVER_URL -p my-process -f json | jq -r '.jobs[].jobID'); do
    echo "Job $job:"
    weaver statistics -u $WEAVER_URL -j $job
done

Python Usage

from weaver.cli import WeaverClient

client = WeaverClient(url="https://weaver.example.com")

# Get statistics
stats = client.statistics(job_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890")

print(f"Duration: {stats.body.get('duration')}")
print(f"CPU Usage: {stats.body.get('cpuUsage')}")
print(f"Memory Usage: {stats.body.get('memoryUsage')}")

API Request

curl -X GET \
  "${WEAVER_URL}/jobs/a1b2c3d4-e5f6-7890-abcd-ef1234567890/statistics"

Returns

{
  "jobID": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "duration": "PT5M32S",
  "executionDuration": "PT5M15S",
  "queueDuration": "PT17S",
  "resource": {
    "cpuUsage": {
      "average": "45%",
      "peak": "87%"
    },
    "memoryUsage": {
      "average": "2.3 GB",
      "peak": "4.1 GB"
    },
    "diskIO": {
      "read": "150 MB",
      "write": "75 MB"
    }
  },
  "dataTransfer": {
    "inputSize": "500 MB",
    "outputSize": "200 MB"
  }
}

Note: Response may include additional fields. See API documentation for complete response schemas.

Statistics Fields

Timing

• duration: Total time from submission to completion
• executionDuration: Actual processing time
• queueDuration: Time spent waiting in queue

Resource Usage

• cpuUsage: CPU utilization (average and peak)
• memoryUsage: RAM consumption (average and peak)
• diskIO: Disk read/write operations
• networkIO: Network transfer (if applicable)

Data Metrics

• inputSize: Total size of input data
• outputSize: Total size of output data
• transferredData: Data transferred between services

Use Cases

Resource Optimization

# Analyze resource usage patterns
stats = client.statistics(job_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890")

if stats.body["resource"]["memoryUsage"]["peak"] > "8 GB":
    print("Consider increasing memory allocation")

Cost Estimation

# Calculate approximate cloud compute costs
duration_minutes = parse_duration(stats.body["duration"])
cpu_hours = duration_minutes / 60
estimated_cost = cpu_hours * cost_per_cpu_hour

Related Skills

• job-status - Check job status
• job-logs - View execution logs
• job-monitor - Monitor execution
• job-list - Compare multiple jobs

Documentation

• Job Statistics
• Resource Management
• CLI Reference