Files
mcp-server/spark_executor/tools/status.py
T
Claude 5c7dcf57cc feat: structured DEBUG/INFO logging via loguru
- common/logging.py: improve format to timestamp|LEVEL|module:func:line - message
- core/ layer: DEBUG log every subprocess invocation (cmd, rc, byte counts),
  JSON load/dump events, parsed application_id. ERROR log on failures.
- tools/ layer: DEBUG log every public tool entry with key parameters,
  INFO log on business outcomes (saved/submitted/killed/...).
- New tests/unit/test_logging.py: capture loguru output via in-memory sink
  and assert DEBUG + INFO messages are emitted for representative flows.
2026-06-24 15:02:54 +08:00

22 lines
724 B
Python

# coding=utf-8
"""
@Time :2026/6/24
@Author :tao.chen
"""
from common.logging import logger
from spark_executor.core.job_store import JobStore
from spark_executor.core.yarn_client import get_application_status
from spark_executor.models import JobStatus
store = JobStore()
def get_job_status(job_id: str) -> JobStatus:
logger.debug(f"get_job_status enter job_id={job_id}")
job = store.get(job_id)
if job is None:
raise KeyError(f"Unknown job_id: {job_id}")
state, raw = get_application_status(job.application_id)
logger.info(f"get_job_status ok job_id={job_id} application_id={job.application_id} state={state}")
return JobStatus(application_id=job.application_id, state=state, raw=raw)