mcp-server/spark_executor/models.py

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

from pydantic import BaseModel, Field, field_validator


class Job(BaseModel):
    job_id: str
    application_id: str
    script_path: str
    queue: str
    submit_time: datetime
    connection: str
    yarn_rm_url: str | None = None


class JobStatus(BaseModel):
    application_id: str
    state: str
    raw: str = Field(default="")


class JobResult(BaseModel):
    application_id: str
    state: str
    final_status: str | None = None
    diagnostics: str | None = None
    tracking_url: str | None = None
    started_time: int | None = None
    finished_time: int | None = None


class SubmitResult(BaseModel):
    job_id: str
    application_id: str
    tracking_url: str | None = None


class FetchUrlResult(BaseModel):
    url: str
    status_code: int
    content_type: str
    body: str
    truncated: bool = False


class ApplicationSummary(BaseModel):
    """A YARN application summary from /ws/v1/cluster/apps.

    Field names are mapped from the YARN JSON keys to clearer
    snake_case names by the tool function. Unused YARN fields
    (memorySeconds, vcoreSeconds, preemptedResource*, etc.) are
    not exposed — the LLM doesn't need them.
    """
    application_id: str
    name: str
    user: str
    queue: str
    state: str
    final_status: str | None = None
    application_type: str | None = None
    application_tags: str = ""
    started_time: int = 0
    finished_time: int = 0
    tracking_url: str | None = None
    progress: float | None = None


class Connection(BaseModel):
    name: str
    # Defaults to "yarn" because that's the literal string spark-submit wants
    # for --master when targeting YARN. Override for Standalone (spark://...),
    # Kubernetes (k8s://...), or local mode.
    master: str = "yarn"
    deploy_mode: str = "cluster"
    yarn_rm_url: str | None = None
    spark_conf: dict[str, str] = Field(default_factory=dict)
    # None means "fall back to Settings.ssl_verify_default". Explicit True/False
    # overrides the global default for this connection.
    ssl_verify: bool | None = None
    ssl_ca_bundle: str | None = None
    # Authentication for YARN REST calls.
    auth_type: str = "none"  # "none" | "simple" | "basic" | "kerberos"
    auth_user: str | None = None
    auth_password: str | None = None
    # Display/audit only for kerberos; actual SPNEGO uses the system cache.
    auth_principal: str | None = None
    auth_keytab: str | None = None

    url_allowlist: list[str] = Field(
        default_factory=list,
        description=(
            "List of fnmatch glob patterns for hosts the fetch_url tool may access. "
            "The list is mandatory-opt-in: an empty list (the default) denies all "
            "hosts, so you must populate it before fetch_url can access any URL. "
            "Useful for clusters whose hostnames do NOT share a common suffix — "
            "e.g. single-label hosts like 'ccam1'-'ccam99' (configure ['ccam*']) "
            "or HDFS namenode on a different subdomain ('*.hadoop.internal'). "
            "Patterns are matched against the URL host only (no port, no path). "
            "fnmatch rules apply: '*' does NOT match '.', so 'ccam*' matches "
            "'ccam50' but not 'ccam50.evil.com'."
        ),
    )

    @field_validator("master")
    @classmethod
    def _check_master(cls, v: str) -> str:
        """Catch common typos like 'yarn-cluster' or 'http://...'. """
        if v == "yarn":
            return v
        if v.startswith(("spark://", "k8s://", "mesos://", "local")):
            return v
        raise ValueError(
            f"master must be 'yarn', 'spark://...', 'k8s://...', 'mesos://...', "
            f"or 'local[/N]'; got {v!r}"
        )

    @field_validator("auth_type")
    @classmethod
    def _check_auth_type(cls, v: str) -> str:
        if v not in {"none", "simple", "basic", "kerberos"}:
            raise ValueError(
                f"auth_type must be one of none/simple/basic/kerberos; got {v!r}"
            )
        return v


class PendingSubmission(BaseModel):
    pending_id: str
    app_name: str | None = None

    connection: str
    master: str
    deploy_mode: str
    yarn_rm_url: str | None = None
    script_path: str
    queue: str
    executor_memory: str
    executor_cores: int
    num_executors: int
    spark_conf: dict[str, str] = Field(default_factory=dict)
    extra_args: dict[str, str] = Field(default_factory=dict)
    created_at: datetime
    status: str = "PENDING"  # PENDING | SUBMITTED | CANCELLED | FAILED
    error: str | None = None
    job_id: str | None = None
    application_id: str | None = None
    tracking_url: str | None = None