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from __future__ import annotations

from collections.abc import Sequence
from functools import cached_property
from typing import TYPE_CHECKING

from kubernetes import client

from airflow.providers.cncf.kubernetes.hooks.kubernetes import KubernetesHook
from airflow.providers.common.compat.sdk import AirflowException, BaseSensorOperator

if TYPE_CHECKING:
    from airflow.sdk import Context


class SparkKubernetesSensor(BaseSensorOperator):
    """
    Checks sparkApplication object in kubernetes cluster.

    .. seealso::
        For more detail about Spark Application Object have a look at the reference:
        https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/blob/v1beta2-1.1.0-2.4.5/docs/api-docs.md#sparkapplication

    :param application_name: spark Application resource name
    :param namespace: the kubernetes namespace where the sparkApplication reside in
    :param container_name: the kubernetes container name where the sparkApplication reside in
    :param kubernetes_conn_id: The :ref:`kubernetes connection<howto/connection:kubernetes>`
        to Kubernetes cluster.
    :param attach_log: determines whether logs for driver pod should be appended to the sensor log
    :param api_group: kubernetes api group of sparkApplication
    :param api_version: kubernetes api version of sparkApplication
    """

    template_fields: Sequence[str] = ("application_name", "namespace")
    FAILURE_STATES = ("FAILED", "UNKNOWN")
    SUCCESS_STATES = ("COMPLETED",)

    def __init__(
        self,
        *,
        application_name: str,
        attach_log: bool = False,
        namespace: str | None = None,
        container_name: str = "spark-kubernetes-driver",
        kubernetes_conn_id: str = "kubernetes_default",
        api_group: str = "sparkoperator.k8s.io",
        api_version: str = "v1beta2",
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.application_name = application_name
        self.attach_log = attach_log
        self.namespace = namespace
        self.container_name = container_name
        self.kubernetes_conn_id = kubernetes_conn_id
        self.api_group = api_group
        self.api_version = api_version

    @cached_property
    def hook(self) -> KubernetesHook:
        return KubernetesHook(conn_id=self.kubernetes_conn_id)

    def _log_driver(self, application_state: str, response: dict) -> None:
        if not self.attach_log:
            return
        status_info = response["status"]
        if "driverInfo" not in status_info:
            return
        driver_info = status_info["driverInfo"]
        if "podName" not in driver_info:
            return
        driver_pod_name = driver_info["podName"]
        namespace = response["metadata"]["namespace"]
        log_method = self.log.error if application_state in self.FAILURE_STATES else self.log.info
        try:
            log = ""
            for line in self.hook.get_pod_logs(
                driver_pod_name, namespace=namespace, container=self.container_name
            ):
                log += line.decode()
            log_method(log)
        except client.rest.ApiException as e:
            self.log.warning(
                "Could not read logs for pod %s. It may have been disposed.\n"
                "Make sure timeToLiveSeconds is set on your SparkApplication spec.\n"
                "underlying exception: %s",
                driver_pod_name,
                e,
            )

    def poke(self, context: Context) -> bool:
        self.log.info("Poking: %s", self.application_name)

        response = self.hook.get_custom_object(
            group=self.api_group,
            version=self.api_version,
            plural="sparkapplications",
            name=self.application_name,
            namespace=self.namespace,
        )

        try:
            application_state = response["status"]["applicationState"]["state"]
        except KeyError:
            return False

        if self.attach_log and application_state in self.FAILURE_STATES + self.SUCCESS_STATES:
            self._log_driver(application_state, response)

        if application_state in self.FAILURE_STATES:
            message = f"Spark application failed with state: {application_state}"
            raise AirflowException(message)
        if application_state in self.SUCCESS_STATES:
            self.log.info("Spark application ended successfully")
            return True
        self.log.info("Spark application is still in state: %s", application_state)
        return False
