Apache Airflow dags w/ backend configuration bundle.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 

101 lines
3.6 KiB

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import Any, Dict, List, Optional
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
class EmrHook(AwsBaseHook):
"""
Interact with AWS EMR. emr_conn_id is only necessary for using the
create_job_flow method.
Additional arguments (such as ``aws_conn_id``) may be specified and
are passed down to the underlying AwsBaseHook.
.. seealso::
:class:`~airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook`
"""
conn_name_attr = "emr_conn_id"
default_conn_name = "emr_default"
conn_type = "emr"
hook_name = "Elastic MapReduce"
def __init__(
self, emr_conn_id: Optional[str] = default_conn_name, *args, **kwargs
) -> None:
self.emr_conn_id = emr_conn_id
kwargs["client_type"] = "emr"
super().__init__(*args, **kwargs)
def get_cluster_id_by_name(
self, emr_cluster_name: str, cluster_states: List[str]
) -> Optional[str]:
"""
Fetch id of EMR cluster with given name and (optional) states.
Will return only if single id is found.
:param emr_cluster_name: Name of a cluster to find
:type emr_cluster_name: str
:param cluster_states: State(s) of cluster to find
:type cluster_states: list
:return: id of the EMR cluster
"""
response = self.get_conn().list_clusters(ClusterStates=cluster_states)
matching_clusters = list(
filter(
lambda cluster: cluster["Name"] == emr_cluster_name,
response["Clusters"],
)
)
if len(matching_clusters) == 1:
cluster_id = matching_clusters[0]["Id"]
self.log.info(
"Found cluster name = %s id = %s", emr_cluster_name, cluster_id
)
return cluster_id
elif len(matching_clusters) > 1:
raise AirflowException(
f"More than one cluster found for name {emr_cluster_name}"
)
else:
self.log.info("No cluster found for name %s", emr_cluster_name)
return None
def create_job_flow(self, job_flow_overrides: Dict[str, Any]) -> Dict[str, Any]:
"""
Creates a job flow using the config from the EMR connection.
Keys of the json extra hash may have the arguments of the boto3
run_job_flow method.
Overrides for this config may be passed as the job_flow_overrides.
"""
if not self.emr_conn_id:
raise AirflowException("emr_conn_id must be present to use create_job_flow")
emr_conn = self.get_connection(self.emr_conn_id)
config = emr_conn.extra_dejson.copy()
config.update(job_flow_overrides)
response = self.get_conn().run_job_flow(**config)
return response