mcp-server/spark_executor/tools/submit.py

# coding=utf-8
"""
@Time   :2026/6/24
@Author :tao.chen
"""
import os
import secrets
import uuid
from datetime import datetime

from common.logging import logger
from spark_executor.core.connection_store import store as conn_store
from spark_executor.core.job_store import JobStore
from spark_executor.core.log_parser import parse_spark_submit_output
from spark_executor.core.pending_store import store as pending_store
from spark_executor.core.spark_submit import (
    SparkSubmitError,
    build_spark_submit_command,
    run_spark_submit,
)
from spark_executor.models import Job, PendingSubmission, SubmitResult


def _script_path_error(path: str) -> ValueError:
    """Instructive error for an invalid script_path. The MCP client receives
    this via the ValueError -> 400 exception handler in server.py."""
    return ValueError(
        f"script_path does not exist or is not a file: {path!r}. "
        f"Two ways to fix this:\n"
        f"  1. (Recommended) Call generate_job_file(code=...) first to write "
        f"the PySpark source to disk, then pass the returned script_path.\n"
        f"  2. If the file already exists on the host, mount it into the "
        f"container (e.g. -v /host/path:/app/scripts:ro in docker run) and "
        f"pass the in-container path here."
    )


def _check_script_path(script_path: str) -> None:
    """Verify script_path points at an existing regular file. Raises ValueError
    (-> 400 via the FastAPI exception handler) with an instructive message."""
    if not script_path or not os.path.isfile(script_path):
        raise _script_path_error(script_path)


def _new_pending_id() -> str:
    return "p_" + secrets.token_hex(6)


def prepare_submit_job(
    *,
    connection: str,
    script_path: str,
    queue: str = "default",
    executor_memory: str = "4G",
    executor_cores: int = 2,
    num_executors: int = 2,
) -> dict[str, object]:
    """Snapshot connection params and persist a PendingSubmission. Does NOT submit."""
    logger.debug(
        f"prepare_submit_job enter connection={connection} script_path={script_path} "
        f"queue={queue} executor_memory={executor_memory} executor_cores={executor_cores} "
        f"num_executors={num_executors}"
    )
    # Order of checks matters for the error the agent sees:
    #   1. Unknown connection  -> 404 (KeyError -> 404 in server.py)
    #   2. Missing script file -> 400 (ValueError -> 400)
    # Connection is checked first because it is a more fundamental problem
    # (the agent is asking about a cluster that doesn't exist), and the
    # agent shouldn't have to fix the script path only to learn the
    # connection name is wrong.
    conn = conn_store.get(connection)
    if conn is None:
        raise KeyError(f"Unknown connection: {connection}")
    # Fail fast: a non-existent path is the most common agent mistake (it
    # generated the code in its own context but forgot to call
    # generate_job_file first, or its path refers to the host filesystem
    # which is invisible inside the container). Better to surface this with
    # a 400 + clear remediation than to let spark-submit fail later with
    # an opaque FileNotFoundError -> 500.
    _check_script_path(script_path)

    pending_id = _new_pending_id()
    pending = PendingSubmission(
        pending_id=pending_id,
        connection=connection,
        master=conn.master,
        deploy_mode=conn.deploy_mode,
        yarn_rm_url=conn.yarn_rm_url,
        script_path=script_path,
        queue=queue,
        executor_memory=executor_memory,
        executor_cores=executor_cores,
        num_executors=num_executors,
        spark_conf=dict(conn.spark_conf),
        created_at=datetime.utcnow(),
        status="PENDING",
    )
    pending_store.save(pending)
    logger.info(
        f"prepare_submit_job ok pending_id={pending_id} connection={connection} "
        f"master={conn.master} script_path={script_path}"
    )
    return {
        "pending_id": pending_id,
        "status": "PENDING",
        "parameters": pending.model_dump(),
    }


# Module-level job store singleton; replaced in tests.
job_store: JobStore = JobStore()


def confirm_submit_job(*, pending_id: str) -> SubmitResult:
    """Actually invoke spark-submit for a previously-prepared PendingSubmission."""
    logger.debug(f"confirm_submit_job enter pending_id={pending_id}")
    pending = pending_store.get(pending_id)
    if pending is None:
        raise KeyError(f"Unknown pending_id: {pending_id}")
    if pending.status != "PENDING":
        raise ValueError(
            f"pending_id {pending_id} is in status {pending.status!r}, not PENDING"
        )
    # Defense in depth: re-verify the script still exists. A user could
    # delete the file between prepare and confirm (or an external cleanup
    # job could remove it). 400 via the ValueError -> 400 handler.
    _check_script_path(pending.script_path)

    cmd = build_spark_submit_command(
        master=pending.master,
        deploy_mode=pending.deploy_mode,
        script_path=pending.script_path,
        queue=pending.queue,
        executor_memory=pending.executor_memory,
        executor_cores=pending.executor_cores,
        num_executors=pending.num_executors,
        spark_conf=pending.spark_conf,
    )
    logger.info(
        f"confirm_submit_job start pending_id={pending_id} "
        f"application_target={pending.master} script_path={pending.script_path}"
    )
    try:
        result = run_spark_submit(cmd)
    except SparkSubmitError as exc:
        pending.status = "FAILED"
        pending.error = str(exc)
        pending_store.save(pending)
        logger.error(f"confirm_submit_job failed pending_id={pending_id} err={exc}")
        raise

    application_id, tracking_url = parse_spark_submit_output(result.stderr)
    job_id = uuid.uuid4().hex[:12]

    job_store.put(
        Job(
            job_id=job_id,
            application_id=application_id,
            script_path=pending.script_path,
            queue=pending.queue,
            submit_time=datetime.utcnow(),
            connection=pending.connection,
            yarn_rm_url=pending.yarn_rm_url,
        )
    )

    pending.status = "SUBMITTED"
    pending.job_id = job_id
    pending.application_id = application_id
    pending_store.save(pending)
    logger.info(
        f"confirm_submit_job ok pending_id={pending_id} job_id={job_id} "
        f"application_id={application_id}"
    )
    return SubmitResult(
        job_id=job_id,
        application_id=application_id,
        tracking_url=tracking_url,
    )


def list_pending_jobs() -> list[dict[str, object]]:
    logger.debug("list_pending_jobs enter")
    return [p.model_dump() for p in pending_store.list_all()]


def get_pending_job(pending_id: str) -> dict[str, object]:
    logger.debug(f"get_pending_job enter pending_id={pending_id}")
    p = pending_store.get(pending_id)
    if p is None:
        raise KeyError(f"Unknown pending_id: {pending_id}")
    return p.model_dump()


def cancel_pending_job(pending_id: str) -> dict[str, str]:
    logger.debug(f"cancel_pending_job enter pending_id={pending_id}")
    p = pending_store.get(pending_id)
    if p is None:
        raise KeyError(f"Unknown pending_id: {pending_id}")
    if p.status in ("SUBMITTED", "FAILED"):
        raise ValueError(
            f"pending_id {pending_id} is in status {p.status!r} and cannot be cancelled"
        )
    p.status = "CANCELLED"
    pending_store.save(p)
    logger.info(f"cancel_pending_job ok pending_id={pending_id}")
    return {"pending_id": pending_id, "status": "CANCELLED"}