Claude
b3564737f7
yarn_client.py no longer invokes the 'yarn' binary via subprocess; it uses
httpx against /ws/v1/cluster/apps/* endpoints. This means the runtime image
no longer needs the Hadoop client installation — the only YARN-side
dependency left in the container is the config dir consumed by
spark-submit itself.
New model field:
- Job.yarn_rm_url: str | None
- PendingSubmission.yarn_rm_url: str | None (snapshotted at prepare)
prepare_submit_job snapshots Connection.yarn_rm_url into the pending
record (consistent with the existing master/deploy_mode/spark_conf
snapshot pattern); confirm_submit_job copies it onto the Job so
status/logs/kill can use it without re-looking-up the connection.
Resolution order for the RM URL at runtime:
1. Job.yarn_rm_url (preferred — survives connection edits/deletes)
2. Connection.yarn_rm_url fallback (if a future tool is added that
doesn't go through a Job)
3. YARN_RESOURCE_MANAGER_URL env var
Errors:
- YarnConfigError (HTTP 4xx semantics) when URL is missing/malformed
- YarnError for HTTP 4xx/5xx from the RM, network failures, missing
state field, or unparseable log responses
10 new tests in test_yarn_client.py cover the REST surface:
success, 404, 5xx, missing state field, env-var fallback, malformed
URL, log 404 with log-aggregation hint, kill PUT body shape, and
httpx connection-error wrapping.
58 lines
1.2 KiB
Python
58 lines
1.2 KiB
Python
# coding=utf-8
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"""
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@Time :2026/6/24
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@Author :tao.chen
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"""
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from datetime import datetime
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from pydantic import BaseModel, Field
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class Job(BaseModel):
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job_id: str
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application_id: str
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script_path: str
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queue: str
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submit_time: datetime
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connection: str
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yarn_rm_url: str | None = None
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class JobStatus(BaseModel):
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application_id: str
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state: str
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raw: str = Field(default="")
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class SubmitResult(BaseModel):
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job_id: str
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application_id: str
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tracking_url: str | None = None
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class Connection(BaseModel):
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name: str
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master: str
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deploy_mode: str = "cluster"
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yarn_rm_url: str | None = None
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spark_conf: dict[str, str] = Field(default_factory=dict)
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class PendingSubmission(BaseModel):
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pending_id: str
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connection: str
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master: str
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deploy_mode: str
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yarn_rm_url: str | None = None
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script_path: str
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queue: str
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executor_memory: str
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executor_cores: int
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num_executors: int
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spark_conf: dict[str, str] = Field(default_factory=dict)
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created_at: datetime
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status: str = "PENDING" # PENDING | SUBMITTED | CANCELLED | FAILED
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error: str | None = None
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job_id: str | None = None
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application_id: str | None = None
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