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from __future__ import annotations
import datetime
from functools import cached_property
from typing import TYPE_CHECKING, Sequence
from kubernetes.client import ApiException
from kubernetes.watch import Watch
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.cncf.kubernetes.hooks.kubernetes import KubernetesHook, _load_body_to_dict
if TYPE_CHECKING:
from kubernetes.client.models import CoreV1EventList
from airflow.utils.context import Context
[docs]class SparkKubernetesOperator(BaseOperator):
"""
Creates 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_file: Defines Kubernetes 'custom_resource_definition' of 'sparkApplication' as either a
path to a '.yaml' file, '.json' file, YAML string or python dictionary.
:param namespace: kubernetes namespace to put sparkApplication
:param kubernetes_conn_id: The :ref:`kubernetes connection id <howto/connection:kubernetes>`
for the to Kubernetes cluster.
:param api_group: kubernetes api group of sparkApplication
:param api_version: kubernetes api version of sparkApplication
:param watch: whether to watch the job status and logs or not
"""
[docs] template_fields: Sequence[str] = ("application_file", "namespace")
[docs] template_ext: Sequence[str] = (".yaml", ".yml", ".json")
def __init__(
self,
*,
application_file: str | dict,
namespace: str | None = None,
kubernetes_conn_id: str = "kubernetes_default",
api_group: str = "sparkoperator.k8s.io",
api_version: str = "v1beta2",
in_cluster: bool | None = None,
cluster_context: str | None = None,
config_file: str | None = None,
watch: bool = False,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.namespace = namespace
self.kubernetes_conn_id = kubernetes_conn_id
self.api_group = api_group
self.api_version = api_version
self.plural = "sparkapplications"
self.application_file = application_file
self.in_cluster = in_cluster
self.cluster_context = cluster_context
self.config_file = config_file
self.watch = watch
@cached_property
[docs] def hook(self) -> KubernetesHook:
return KubernetesHook(
conn_id=self.kubernetes_conn_id,
in_cluster=self.in_cluster,
config_file=self.config_file,
cluster_context=self.cluster_context,
)
def _get_namespace_event_stream(self, namespace, query_kwargs=None):
try:
return Watch().stream(
self.hook.core_v1_client.list_namespaced_event,
namespace=namespace,
watch=True,
**(query_kwargs or {}),
)
except ApiException as e:
if e.status == 410: # Resource version is too old
events: CoreV1EventList = self.hook.core_v1_client.list_namespaced_event(
namespace=namespace, watch=False
)
resource_version = events.metadata.resource_version
query_kwargs["resource_version"] = resource_version
return self._get_namespace_event_stream(namespace, query_kwargs)
else:
raise
[docs] def execute(self, context: Context):
if isinstance(self.application_file, str):
body = _load_body_to_dict(self.application_file)
else:
body = self.application_file
name = body["metadata"]["name"]
namespace = self.namespace or self.hook.get_namespace()
response = None
is_job_created = False
if self.watch:
try:
namespace_event_stream = self._get_namespace_event_stream(
namespace=namespace,
query_kwargs={
"field_selector": f"involvedObject.kind=SparkApplication,involvedObject.name={name}"
},
)
response = self.hook.create_custom_object(
group=self.api_group,
version=self.api_version,
plural=self.plural,
body=body,
namespace=namespace,
)
is_job_created = True
for event in namespace_event_stream:
obj = event["object"]
if event["object"].last_timestamp >= datetime.datetime.strptime(
response["metadata"]["creationTimestamp"], "%Y-%m-%dT%H:%M:%S%z"
):
self.log.info(obj.message)
if obj.reason == "SparkDriverRunning":
pod_log_stream = Watch().stream(
self.hook.core_v1_client.read_namespaced_pod_log,
name=f"{name}-driver",
namespace=namespace,
timestamps=True,
)
for line in pod_log_stream:
self.log.info(line)
elif obj.reason in [
"SparkApplicationSubmissionFailed",
"SparkApplicationFailed",
"SparkApplicationDeleted",
]:
is_job_created = False
raise AirflowException(obj.message)
elif obj.reason == "SparkApplicationCompleted":
break
else:
continue
except Exception:
if is_job_created:
self.on_kill()
raise
else:
response = self.hook.create_custom_object(
group=self.api_group,
version=self.api_version,
plural=self.plural,
body=body,
namespace=namespace,
)
return response
[docs] def on_kill(self) -> None:
if isinstance(self.application_file, str):
body = _load_body_to_dict(self.application_file)
else:
body = self.application_file
name = body["metadata"]["name"]
namespace = self.namespace or self.hook.get_namespace()
self.hook.delete_custom_object(
group=self.api_group,
version=self.api_version,
plural=self.plural,
namespace=namespace,
name=name,
)
