Files
mcp-server/spark_executor/tools/requests.py
T
Claude da9ba657f3 feat(pending): app_name field and date-sharded pending storage
- Add optional app_name to PendingSubmission and prepare_submit_job,
  passed through PrepareSubmitJobRequest for user-provided tracking.
- Rewrite PendingStore to shard records by UTC date under
  data/pending_jobs/YYYY-MM-DD.json instead of a single monolithic
  pending_jobs.json.
- Legacy single-file pending_jobs.json remains readable; first save()
  migrates it and renames the old file to avoid double reads.
- Tests cover date sharding, multi-date list, legacy read, legacy
  migration, and app_name round-trip.
2026-06-26 14:36:24 +08:00

113 lines
3.2 KiB
Python

# coding=utf-8
"""
@Time :2026/6/24
@Author :tao.chen
Pydantic request models for the FastAPI route layer. The underlying tool
functions in tools/*.py still take keyword arguments; these models exist only
so fastapi-mcp can call the routes via tools/call (which sends args as a
JSON body) without 422-ing on dict-typed parameters like spark_conf.
"""
from pydantic import BaseModel, Field
class EmptyRequest(BaseModel):
"""Used for tools that take no arguments (list_connections, list_pending_jobs)."""
pass
class SaveConnectionRequest(BaseModel):
name: str
master: str
deploy_mode: str = "cluster"
yarn_rm_url: str | None = None
spark_conf: dict[str, str] | None = None
ssl_verify: bool | None = None
ssl_ca_bundle: str | None = None
auth_type: str = "none"
auth_user: str | None = None
auth_password: str | None = None
auth_principal: str | None = None
auth_keytab: str | None = None
class PrepareSubmitJobRequest(BaseModel):
connection: str
app_name: str | None = Field(
default=None,
description="Optional human-readable application name for tracking the pending submission.",
)
script_path: str = Field(
...,
description=(
"Absolute path to the PySpark script inside the container's "
"filesystem. Must point at an existing regular file. For "
"LLM-generated code, call generate_job_file(code=...) first "
"and pass the returned script_path here. For pre-existing "
"files on the host, mount them via a docker volume and pass "
"the in-container path. Returns 400 with a remediation hint "
"if the path is missing or not a file."
),
)
queue: str = "default"
executor_memory: str = "4G"
executor_cores: int = 2
num_executors: int = 2
class PendingIdRequest(BaseModel):
pending_id: str
class JobIdRequest(BaseModel):
job_id: str
class GetJobLogsRequest(BaseModel):
job_id: str
tail_chars: int = 5000
class ConnectionNameRequest(BaseModel):
name: str
class GenerateJobFileRequest(BaseModel):
code: str = Field(
...,
description=(
"Full PySpark source code to write to disk. Will be passed verbatim "
"to spark-submit after the agent calls prepare_submit_job on the "
"returned path."
),
)
class ReadJobFileRequest(BaseModel):
script_path: str = Field(
...,
description=(
"Absolute path to a PySpark script inside the container's "
"filesystem. Must point at an existing regular file."
),
)
class UpdateJobFileRequest(BaseModel):
script_path: str = Field(
...,
description=(
"Absolute path to an existing PySpark script inside the "
"container's filesystem. Must be under SPARK_EXECUTOR_JOBS_DIR "
"(the same dir generate_job_file writes to) — protects against "
"overwriting host-mounted configs or other critical files."
),
)
content: str = Field(
...,
description=(
"New file content (replaces the file in full; no merge/diff). "
"Maximum 1 MB to keep the MCP response bounded."
),
)