Claude
4b07617af0
Remove defaults from queue, executor_memory, executor_cores, num_executors, and app_name in prepare_submit_job. All must now be explicitly confirmed by the caller. Also add extra_args to the PendingSubmission snapshot so users can confirm non-conf spark-submit flags (e.g. --jars, --py-files) at prepare time; confirm_submit_job passes them through to build_spark_submit_command. This is an intentional breaking change to the MCP tool contract: callers can no longer rely on implicit defaults.
240 lines
8.8 KiB
Python
240 lines
8.8 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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import os
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import secrets
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import uuid
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from datetime import datetime
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from common.logging import logger
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from common.sql_guard import validate_pyspark_code
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from spark_executor.core.connection_store import store as conn_store
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from spark_executor.core.job_store import JobStore
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from spark_executor.core.log_parser import parse_spark_submit_output
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from spark_executor.core.pending_store import store as pending_store
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from spark_executor.core.spark_submit import (
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SparkSubmitError,
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build_spark_submit_command,
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run_spark_submit,
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)
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from spark_executor.models import Job, PendingSubmission, SubmitResult
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def _script_path_error(path: str) -> ValueError:
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"""Instructive error for an invalid script_path. The MCP client receives
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this via the ValueError -> 400 exception handler in server.py."""
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return ValueError(
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f"script_path does not exist or is not a file: {path!r}. "
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f"Two ways to fix this:\n"
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f" 1. (Recommended) Call generate_job_file(code=...) first to write "
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f"the PySpark source to disk, then pass the returned script_path.\n"
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f" 2. If the file already exists on the host, mount it into the "
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f"container (e.g. -v /host/path:/app/scripts:ro in docker run) and "
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f"pass the in-container path here."
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)
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def _check_script_path(script_path: str) -> None:
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"""Verify script_path points at an existing regular file. Raises ValueError
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(-> 400 via the FastAPI exception handler) with an instructive message."""
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if not script_path or not os.path.isfile(script_path):
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raise _script_path_error(script_path)
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def _sql_guard_error(offenses: list[str]) -> ValueError:
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"""Instructive 400 error when the script's SQL fails the safety policy."""
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return ValueError(
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f"Script SQL violates the safety policy. "
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f"Only SELECT and INSERT statements are allowed. "
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f"Found forbidden statement(s): {offenses}. "
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f"Edit the script and try again. (If the code was generated via "
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f"generate_job_file, the generator should have caught this — check "
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f"for f-string-based SQL injection where the static analysis can't "
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f"see the runtime value.)"
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)
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def _new_pending_id() -> str:
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return "p_" + secrets.token_hex(6)
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def prepare_submit_job(
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*,
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connection: str,
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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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app_name: str,
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extra_args: dict[str, str] | None = None,
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) -> dict[str, object]:
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"""Snapshot connection params and persist a PendingSubmission. Does NOT submit."""
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logger.debug(
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f"prepare_submit_job enter connection={connection} script_path={script_path} "
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f"queue={queue} executor_memory={executor_memory} executor_cores={executor_cores} "
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f"num_executors={num_executors} app_name={app_name}"
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)
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# Order of checks matters for the error the agent sees:
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# 1. Unknown connection -> 404 (KeyError -> 404 in server.py)
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# 2. Missing script file -> 400 (ValueError -> 400)
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# 3. SQL policy violation -> 400 (ValueError -> 400)
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# Connection is checked first because it is a more fundamental problem
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# (the agent is asking about a cluster that doesn't exist), and the
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# agent shouldn't have to fix the script path only to learn the
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# connection name is wrong.
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conn = conn_store.get(connection)
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if conn is None:
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raise KeyError(f"Unknown connection: {connection}")
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# Fail fast: a non-existent path is the most common agent mistake (it
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# generated the code in its own context but forgot to call
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# generate_job_file first, or its path refers to the host filesystem
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# which is invisible inside the container). Better to surface this with
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# a 400 + clear remediation than to let spark-submit fail later with
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# an opaque FileNotFoundError -> 500.
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_check_script_path(script_path)
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# SQL safety: re-validate the script even though generate_job_file
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# already guards its own output. Catches files written by other means
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# (host volume mounts, manual edits).
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with open(script_path, encoding="utf-8") as f:
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script_body = f.read()
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offenses = validate_pyspark_code(script_body)
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if offenses:
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logger.warning(
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f"prepare_submit_job rejected: SQL policy violation(s) in "
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f"{script_path}: {offenses}"
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)
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raise _sql_guard_error(offenses)
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pending_id = _new_pending_id()
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pending = PendingSubmission(
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pending_id=pending_id,
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app_name=app_name,
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connection=connection,
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master=conn.master,
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deploy_mode=conn.deploy_mode,
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yarn_rm_url=conn.yarn_rm_url,
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script_path=script_path,
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queue=queue,
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executor_memory=executor_memory,
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executor_cores=executor_cores,
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num_executors=num_executors,
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spark_conf=dict(conn.spark_conf),
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extra_args=dict(extra_args or {}),
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created_at=datetime.utcnow(),
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status="PENDING",
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)
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pending_store.save(pending)
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logger.info(
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f"prepare_submit_job ok pending_id={pending_id} connection={connection} "
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f"master={conn.master} script_path={script_path}"
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)
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return {
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"pending_id": pending_id,
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"status": "PENDING",
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"parameters": pending.model_dump(),
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}
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# Module-level job store singleton; replaced in tests.
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job_store: JobStore = JobStore()
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def confirm_submit_job(*, pending_id: str) -> SubmitResult:
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"""Actually invoke spark-submit for a previously-prepared PendingSubmission."""
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logger.debug(f"confirm_submit_job enter pending_id={pending_id}")
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pending = pending_store.get(pending_id)
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if pending is None:
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raise KeyError(f"Unknown pending_id: {pending_id}")
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if pending.status != "PENDING":
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raise ValueError(
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f"pending_id {pending_id} is in status {pending.status!r}, not PENDING"
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)
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# Defense in depth: re-verify the script still exists. A user could
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# delete the file between prepare and confirm (or an external cleanup
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# job could remove it). 400 via the ValueError -> 400 handler.
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_check_script_path(pending.script_path)
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cmd = build_spark_submit_command(
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master=pending.master,
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deploy_mode=pending.deploy_mode,
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script_path=pending.script_path,
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queue=pending.queue,
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executor_memory=pending.executor_memory,
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executor_cores=pending.executor_cores,
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num_executors=pending.num_executors,
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spark_conf=pending.spark_conf,
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extra_args=pending.extra_args,
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)
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logger.info(
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f"confirm_submit_job start pending_id={pending_id} "
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f"application_target={pending.master} script_path={pending.script_path}"
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)
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try:
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result = run_spark_submit(cmd)
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except SparkSubmitError as exc:
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pending.status = "FAILED"
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pending.error = str(exc)
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pending_store.save(pending)
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logger.error(f"confirm_submit_job failed pending_id={pending_id} err={exc}")
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raise
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application_id, tracking_url = parse_spark_submit_output(result.stderr)
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job_id = uuid.uuid4().hex[:12]
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job_store.put(
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Job(
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job_id=job_id,
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application_id=application_id,
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script_path=pending.script_path,
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queue=pending.queue,
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submit_time=datetime.utcnow(),
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connection=pending.connection,
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yarn_rm_url=pending.yarn_rm_url,
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)
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)
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pending.status = "SUBMITTED"
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pending.job_id = job_id
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pending.application_id = application_id
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pending_store.save(pending)
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logger.info(
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f"confirm_submit_job ok pending_id={pending_id} job_id={job_id} "
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f"application_id={application_id}"
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)
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return SubmitResult(
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job_id=job_id,
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application_id=application_id,
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tracking_url=tracking_url,
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)
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def list_pending_jobs() -> list[dict[str, object]]:
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logger.debug("list_pending_jobs enter")
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return [p.model_dump() for p in pending_store.list_all()]
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def get_pending_job(pending_id: str) -> dict[str, object]:
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logger.debug(f"get_pending_job enter pending_id={pending_id}")
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p = pending_store.get(pending_id)
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if p is None:
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raise KeyError(f"Unknown pending_id: {pending_id}")
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return p.model_dump()
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def cancel_pending_job(pending_id: str) -> dict[str, str]:
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logger.debug(f"cancel_pending_job enter pending_id={pending_id}")
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p = pending_store.get(pending_id)
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if p is None:
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raise KeyError(f"Unknown pending_id: {pending_id}")
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if p.status in ("SUBMITTED", "FAILED"):
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raise ValueError(
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f"pending_id {pending_id} is in status {p.status!r} and cannot be cancelled"
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)
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p.status = "CANCELLED"
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pending_store.save(p)
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logger.info(f"cancel_pending_job ok pending_id={pending_id}")
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return {"pending_id": pending_id, "status": "CANCELLED"}
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