Claude
f170c3045b
Restore defaults for queue/executor_memory/executor_cores/num_executors in prepare_submit_job, but require the caller to explicitly confirm them. If any defaulted field is omitted, the route returns HTTP 400 listing the defaults and asks the caller to resubmit with explicit values. app_name remains required (no meaningful default). extra_args remains optional. Tests cover rejection of unconfirmed defaults and acceptance of explicitly confirmed defaults.
129 lines
3.9 KiB
Python
129 lines
3.9 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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Pydantic request models for the FastAPI route layer. The underlying tool
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functions in tools/*.py still take keyword arguments; these models exist only
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so fastapi-mcp can call the routes via tools/call (which sends args as a
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JSON body) without 422-ing on dict-typed parameters like spark_conf.
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"""
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from pydantic import BaseModel, Field
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class EmptyRequest(BaseModel):
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"""Used for tools that take no arguments (list_connections, list_pending_jobs)."""
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pass
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class SaveConnectionRequest(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] | None = None
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ssl_verify: bool | None = None
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ssl_ca_bundle: str | None = None
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auth_type: str = "none"
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auth_user: str | None = None
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auth_password: str | None = None
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auth_principal: str | None = None
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auth_keytab: str | None = None
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class PrepareSubmitJobRequest(BaseModel):
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connection: str
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app_name: str = Field(
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...,
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description="Human-readable application name for tracking the pending submission.",
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)
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script_path: str = Field(
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...,
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description=(
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"Absolute path to the PySpark script inside the container's "
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"filesystem. Must point at an existing regular file. For "
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"LLM-generated code, call generate_job_file(code=...) first "
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"and pass the returned script_path here. For pre-existing "
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"files on the host, mount them via a docker volume and pass "
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"the in-container path. Returns 400 with a remediation hint "
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"if the path is missing or not a file."
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),
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)
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queue: str = Field(
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default="default",
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description="YARN queue to submit to. Must be explicitly confirmed by the caller.",
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)
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executor_memory: str = Field(
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default="4G",
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description="Executor memory, e.g. '4G'. Must be explicitly confirmed by the caller.",
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)
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executor_cores: int = Field(
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default=2,
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description="Number of cores per executor. Must be explicitly confirmed by the caller.",
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)
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num_executors: int = Field(
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default=2,
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description="Total number of executors. Must be explicitly confirmed by the caller.",
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)
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extra_args: dict[str, str] | None = Field(
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default=None,
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description="Additional spark-submit flags (e.g. jars, py-files) confirmed at prepare time.",
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)
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class PendingIdRequest(BaseModel):
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pending_id: str
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class JobIdRequest(BaseModel):
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job_id: str
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class GetJobLogsRequest(BaseModel):
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job_id: str
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tail_chars: int = 5000
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class ConnectionNameRequest(BaseModel):
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name: str
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class GenerateJobFileRequest(BaseModel):
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code: str = Field(
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...,
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description=(
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"Full PySpark source code to write to disk. Will be passed verbatim "
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"to spark-submit after the agent calls prepare_submit_job on the "
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"returned path."
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),
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)
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class ReadJobFileRequest(BaseModel):
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script_path: str = Field(
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...,
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description=(
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"Absolute path to a PySpark script inside the container's "
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"filesystem. Must point at an existing regular file."
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),
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)
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class UpdateJobFileRequest(BaseModel):
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script_path: str = Field(
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...,
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description=(
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"Absolute path to an existing PySpark script inside the "
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"container's filesystem. Must be under SPARK_EXECUTOR_JOBS_DIR "
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"(the same dir generate_job_file writes to) — protects against "
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"overwriting host-mounted configs or other critical files."
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),
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)
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content: str = Field(
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...,
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description=(
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"New file content (replaces the file in full; no merge/diff). "
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"Maximum 1 MB to keep the MCP response bounded."
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),
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)
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