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Retrieve execution statistics for a job including resource usage (CPU, memory), execution duration, data transfer metrics, and performance indicators. Use for monitoring resource consumption and optimizing process configurations.
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SOC 職業分類に基づく
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| name | job-statistics |
| description | Retrieve execution statistics for a job including resource usage (CPU, memory), execution duration, data transfer metrics, and performance indicators. Use for monitoring resource consumption and optimizing process configurations. |
| license | Apache-2.0 |
| compatibility | Requires Weaver API access with statistics feature enabled. |
| metadata | {"author":"fmigneault"} |
Retrieve execution statistics and resource usage for a job.
# 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 $job
done
from weaver.cli import WeaverClient
client = WeaverClient(url="https://weaver.example.com")
# Get statistics
stats = client.statistics(job_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890")
print(f"Duration: {stats.body.get('duration')}")
print(f"CPU Usage: {stats.body.get('cpuUsage')}")
print(f"Memory Usage: {stats.body.get('memoryUsage')}")
curl -X GET \
"${WEAVER_URL}/jobs/a1b2c3d4-e5f6-7890-abcd-ef1234567890/statistics"
{
"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.
# 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")
# 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