#
# 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.
"""
This module contains Base AWS Hook.
.. seealso::
For more information on how to use this hook, take a look at the guide:
:ref:`howto/connection:aws`
"""
from __future__ import annotations
import datetime
import inspect
import json
import logging
import os
from copy import deepcopy
from functools import cached_property, wraps
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar, Union
import boto3
import botocore
import botocore.session
import jinja2
import requests
import tenacity
from botocore.config import Config
from botocore.waiter import Waiter, WaiterModel
from dateutil.tz import tzlocal
from slugify import slugify
from airflow.configuration import conf
from airflow.exceptions import (
AirflowException,
AirflowNotFoundException,
)
from airflow.hooks.base import BaseHook
from airflow.providers.amazon.aws.utils.connection_wrapper import AwsConnectionWrapper
from airflow.providers.amazon.aws.utils.identifiers import generate_uuid
from airflow.providers.amazon.aws.utils.suppress import return_on_error
from airflow.providers_manager import ProvidersManager
from airflow.utils.helpers import exactly_one
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.log.secrets_masker import mask_secret
[docs]BaseAwsConnection = TypeVar("BaseAwsConnection", bound=Union[boto3.client, boto3.resource])
if TYPE_CHECKING:
from aiobotocore.session import AioSession
from botocore.client import ClientMeta
from botocore.credentials import ReadOnlyCredentials
from airflow.models.connection import Connection
_loader = botocore.loaders.Loader()
"""
botocore data loader to be used with async sessions
By default, a botocore session creates and caches an instance of JSONDecoder which
consumes a lot of memory. This issue was reported here https://github.com/boto/botocore/issues/3078.
In the context of triggers which use boto sessions, this can result in excessive
memory usage and as a result reduced capacity on the triggerer. We can reduce
memory footprint by sharing the loader instance across the sessions.
:meta private:
"""
[docs]class BaseSessionFactory(LoggingMixin):
"""
Base AWS Session Factory class.
This handles synchronous and async boto session creation. It can handle most
of the AWS supported authentication methods.
User can also derive from this class to have full control of boto3 session
creation or to support custom federation.
.. note::
Not all features implemented for synchronous sessions are available
for async sessions.
.. seealso::
- :ref:`howto/connection:aws:session-factory`
"""
def __init__(
self,
conn: Connection | AwsConnectionWrapper | None,
region_name: str | None = None,
config: Config | None = None,
) -> None:
super().__init__()
self._conn = conn
self._region_name = region_name
self._config = config
@cached_property
[docs] def conn(self) -> AwsConnectionWrapper:
"""Cached AWS Connection Wrapper."""
return AwsConnectionWrapper(
conn=self._conn,
region_name=self._region_name,
botocore_config=self._config,
)
@cached_property
[docs] def basic_session(self) -> boto3.session.Session:
"""Cached property with basic boto3.session.Session."""
return self._create_basic_session(session_kwargs=self.conn.session_kwargs)
@property
@property
[docs] def region_name(self) -> str | None:
"""AWS Region Name read-only property."""
return self.conn.region_name
@property
[docs] def config(self) -> Config | None:
"""Configuration for botocore client read-only property."""
return self.conn.botocore_config
@property
[docs] def role_arn(self) -> str | None:
"""Assume Role ARN from AWS Connection."""
return self.conn.role_arn
def _apply_session_kwargs(self, session):
if self.conn.session_kwargs.get("profile_name", None) is not None:
session.set_config_variable("profile", self.conn.session_kwargs["profile_name"])
if (
self.conn.session_kwargs.get("aws_access_key_id", None)
or self.conn.session_kwargs.get("aws_secret_access_key", None)
or self.conn.session_kwargs.get("aws_session_token", None)
):
session.set_credentials(
access_key=self.conn.session_kwargs.get("aws_access_key_id"),
secret_key=self.conn.session_kwargs.get("aws_secret_access_key"),
token=self.conn.session_kwargs.get("aws_session_token"),
)
if self.conn.session_kwargs.get("region_name", None) is not None:
session.set_config_variable("region", self.conn.session_kwargs["region_name"])
[docs] def get_async_session(self):
from aiobotocore.session import get_session as async_get_session
session = async_get_session()
session.register_component("data_loader", _loader)
return session
[docs] def create_session(self, deferrable: bool = False) -> boto3.session.Session | AioSession:
"""Create boto3 or aiobotocore Session from connection config."""
if not self.conn:
self.log.info(
"No connection ID provided. Fallback on boto3 credential strategy (region_name=%r). "
"See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html",
self.region_name,
)
if deferrable:
session = self.get_async_session()
self._apply_session_kwargs(session)
return session
else:
return boto3.session.Session(region_name=self.region_name)
elif not self.role_arn:
if deferrable:
session = self.get_async_session()
self._apply_session_kwargs(session)
return session
else:
return self.basic_session
# Values stored in ``AwsConnectionWrapper.session_kwargs`` are intended to be used only
# to create the initial boto3 session.
# If the user wants to use the 'assume_role' mechanism then only the 'region_name' needs to be
# provided, otherwise other parameters might conflict with the base botocore session.
# Unfortunately it is not a part of public boto3 API, see source of boto3.session.Session:
# https://boto3.amazonaws.com/v1/documentation/api/latest/_modules/boto3/session.html#Session
# If we provide 'aws_access_key_id' or 'aws_secret_access_key' or 'aws_session_token'
# as part of session kwargs it will use them instead of assumed credentials.
assume_session_kwargs = {}
if self.conn.region_name:
assume_session_kwargs["region_name"] = self.conn.region_name
return self._create_session_with_assume_role(
session_kwargs=assume_session_kwargs, deferrable=deferrable
)
def _create_basic_session(self, session_kwargs: dict[str, Any]) -> boto3.session.Session:
return boto3.session.Session(**session_kwargs)
def _create_session_with_assume_role(
self, session_kwargs: dict[str, Any], deferrable: bool = False
) -> boto3.session.Session | AioSession:
if self.conn.assume_role_method == "assume_role_with_web_identity":
# Deferred credentials have no initial credentials
credential_fetcher = self._get_web_identity_credential_fetcher()
params = {
"method": "assume-role-with-web-identity",
"refresh_using": credential_fetcher.fetch_credentials,
"time_fetcher": lambda: datetime.datetime.now(tz=tzlocal()),
}
if deferrable:
from aiobotocore.credentials import AioDeferredRefreshableCredentials
credentials = AioDeferredRefreshableCredentials(**params)
else:
credentials = botocore.credentials.DeferredRefreshableCredentials(**params)
else:
# Refreshable credentials do have initial credentials
params = {
"metadata": self._refresh_credentials(),
"refresh_using": self._refresh_credentials,
"method": "sts-assume-role",
}
if deferrable:
from aiobotocore.credentials import AioRefreshableCredentials
credentials = AioRefreshableCredentials.create_from_metadata(**params)
else:
credentials = botocore.credentials.RefreshableCredentials.create_from_metadata(**params)
if deferrable:
from aiobotocore.session import get_session as async_get_session
session = async_get_session()
else:
session = botocore.session.get_session()
session._credentials = credentials
session.set_config_variable("region", self.basic_session.region_name)
if not deferrable:
return boto3.session.Session(botocore_session=session, **session_kwargs)
return session
def _refresh_credentials(self) -> dict[str, Any]:
self.log.debug("Refreshing credentials")
assume_role_method = self.conn.assume_role_method
if assume_role_method not in ("assume_role", "assume_role_with_saml"):
raise NotImplementedError(f"assume_role_method={assume_role_method} not expected")
sts_client = self.basic_session.client(
"sts",
config=self.config,
endpoint_url=self.conn.get_service_endpoint_url("sts", sts_connection_assume=True),
)
if assume_role_method == "assume_role":
sts_response = self._assume_role(sts_client=sts_client)
else:
sts_response = self._assume_role_with_saml(sts_client=sts_client)
sts_response_http_status = sts_response["ResponseMetadata"]["HTTPStatusCode"]
if sts_response_http_status != 200:
raise RuntimeError(f"sts_response_http_status={sts_response_http_status}")
credentials = sts_response["Credentials"]
expiry_time = credentials.get("Expiration").isoformat()
self.log.debug("New credentials expiry_time: %s", expiry_time)
credentials = {
"access_key": credentials.get("AccessKeyId"),
"secret_key": credentials.get("SecretAccessKey"),
"token": credentials.get("SessionToken"),
"expiry_time": expiry_time,
}
return credentials
def _assume_role(self, sts_client: boto3.client) -> dict:
kw = {
"RoleSessionName": self._strip_invalid_session_name_characters(f"Airflow_{self.conn.conn_id}"),
**self.conn.assume_role_kwargs,
"RoleArn": self.role_arn,
}
return sts_client.assume_role(**kw)
def _assume_role_with_saml(self, sts_client: boto3.client) -> dict[str, Any]:
saml_config = self.extra_config["assume_role_with_saml"]
principal_arn = saml_config["principal_arn"]
idp_auth_method = saml_config["idp_auth_method"]
if idp_auth_method == "http_spegno_auth":
saml_assertion = self._fetch_saml_assertion_using_http_spegno_auth(saml_config)
else:
raise NotImplementedError(
f"idp_auth_method={idp_auth_method} in Connection {self.conn.conn_id} Extra."
'Currently only "http_spegno_auth" is supported, and must be specified.'
)
self.log.debug("Doing sts_client.assume_role_with_saml to role_arn=%s", self.role_arn)
return sts_client.assume_role_with_saml(
RoleArn=self.role_arn,
PrincipalArn=principal_arn,
SAMLAssertion=saml_assertion,
**self.conn.assume_role_kwargs,
)
def _get_idp_response(
self, saml_config: dict[str, Any], auth: requests.auth.AuthBase
) -> requests.models.Response:
idp_url = saml_config["idp_url"]
self.log.debug("idp_url= %s", idp_url)
session = requests.Session()
# Configurable Retry when querying the IDP endpoint
if "idp_request_retry_kwargs" in saml_config:
idp_request_retry_kwargs = saml_config["idp_request_retry_kwargs"]
self.log.info("idp_request_retry_kwargs= %s", idp_request_retry_kwargs)
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
retry_strategy = Retry(**idp_request_retry_kwargs)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("https://", adapter)
session.mount("http://", adapter)
idp_request_kwargs = {}
if "idp_request_kwargs" in saml_config:
idp_request_kwargs = saml_config["idp_request_kwargs"]
idp_response = session.get(idp_url, auth=auth, **idp_request_kwargs)
idp_response.raise_for_status()
return idp_response
def _fetch_saml_assertion_using_http_spegno_auth(self, saml_config: dict[str, Any]) -> str:
# requests_gssapi will need paramiko > 2.6 since you'll need
# 'gssapi' not 'python-gssapi' from PyPi.
# https://github.com/paramiko/paramiko/pull/1311
import requests_gssapi
from lxml import etree
auth = requests_gssapi.HTTPSPNEGOAuth()
if "mutual_authentication" in saml_config:
mutual_auth = saml_config["mutual_authentication"]
if mutual_auth == "REQUIRED":
auth = requests_gssapi.HTTPSPNEGOAuth(requests_gssapi.REQUIRED)
elif mutual_auth == "OPTIONAL":
auth = requests_gssapi.HTTPSPNEGOAuth(requests_gssapi.OPTIONAL)
elif mutual_auth == "DISABLED":
auth = requests_gssapi.HTTPSPNEGOAuth(requests_gssapi.DISABLED)
else:
raise NotImplementedError(
f"mutual_authentication={mutual_auth} in Connection {self.conn.conn_id} Extra."
'Currently "REQUIRED", "OPTIONAL" and "DISABLED" are supported.'
"(Exclude this setting will default to HTTPSPNEGOAuth() )."
)
# Query the IDP
idp_response = self._get_idp_response(saml_config, auth=auth)
# Assist with debugging. Note: contains sensitive info!
xpath = saml_config["saml_response_xpath"]
log_idp_response = "log_idp_response" in saml_config and saml_config["log_idp_response"]
if log_idp_response:
self.log.warning(
"The IDP response contains sensitive information, but log_idp_response is ON (%s).",
log_idp_response,
)
self.log.debug("idp_response.content= %s", idp_response.content)
self.log.debug("xpath= %s", xpath)
# Extract SAML Assertion from the returned HTML / XML
xml = etree.fromstring(idp_response.content)
saml_assertion = xml.xpath(xpath)
if isinstance(saml_assertion, list):
if len(saml_assertion) == 1:
saml_assertion = saml_assertion[0]
if not saml_assertion:
raise ValueError("Invalid SAML Assertion")
return saml_assertion
def _get_web_identity_credential_fetcher(
self,
) -> botocore.credentials.AssumeRoleWithWebIdentityCredentialFetcher:
base_session = self.basic_session._session or botocore.session.get_session()
client_creator = base_session.create_client
federation = str(self.extra_config.get("assume_role_with_web_identity_federation"))
web_identity_token_loader = {
"file": self._get_file_token_loader,
"google": self._get_google_identity_token_loader,
}.get(federation)
if not web_identity_token_loader:
raise AirflowException(f"Unsupported federation: {federation}.")
return botocore.credentials.AssumeRoleWithWebIdentityCredentialFetcher(
client_creator=client_creator,
web_identity_token_loader=web_identity_token_loader(),
role_arn=self.role_arn,
extra_args=self.conn.assume_role_kwargs,
)
def _get_file_token_loader(self):
from botocore.credentials import FileWebIdentityTokenLoader
token_file = self.extra_config.get("assume_role_with_web_identity_token_file") or os.getenv(
"AWS_WEB_IDENTITY_TOKEN_FILE"
)
return FileWebIdentityTokenLoader(token_file)
def _get_google_identity_token_loader(self):
from google.auth.transport import requests as requests_transport
from airflow.providers.google.common.utils.id_token_credentials import (
get_default_id_token_credentials,
)
audience = self.extra_config.get("assume_role_with_web_identity_federation_audience")
google_id_token_credentials = get_default_id_token_credentials(target_audience=audience)
def web_identity_token_loader():
if not google_id_token_credentials.valid:
request_adapter = requests_transport.Request()
google_id_token_credentials.refresh(request=request_adapter)
return google_id_token_credentials.token
return web_identity_token_loader
def _strip_invalid_session_name_characters(self, role_session_name: str) -> str:
return slugify(role_session_name, regex_pattern=r"[^\w+=,.@-]+")
[docs]class AwsGenericHook(BaseHook, Generic[BaseAwsConnection]):
"""
Generic class for interact with AWS.
This class provide a thin wrapper around the boto3 Python library.
:param aws_conn_id: The Airflow connection used for AWS credentials.
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then default boto3 configuration would be used (and must be
maintained on each worker node).
:param verify: Whether or not to verify SSL certificates. See:
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
:param region_name: AWS region_name. If not specified then the default boto3 behaviour is used.
:param client_type: Reference to :external:py:meth:`boto3.client service_name \
<boto3.session.Session.client>`, e.g. 'emr', 'batch', 's3', etc.
Mutually exclusive with ``resource_type``.
:param resource_type: Reference to :external:py:meth:`boto3.resource service_name \
<boto3.session.Session.resource>`, e.g. 's3', 'ec2', 'dynamodb', etc.
Mutually exclusive with ``client_type``.
:param config: Configuration for botocore client. See:
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
"""
[docs] conn_name_attr = "aws_conn_id"
[docs] default_conn_name = "aws_default"
[docs] hook_name = "Amazon Web Services"
def __init__(
self,
aws_conn_id: str | None = default_conn_name,
verify: bool | str | None = None,
region_name: str | None = None,
client_type: str | None = None,
resource_type: str | None = None,
config: Config | dict[str, Any] | None = None,
) -> None:
super().__init__()
self.aws_conn_id = aws_conn_id
self.client_type = client_type
self.resource_type = resource_type
self._region_name = region_name
if isinstance(config, dict):
config = Config(**config)
self._config = config
self._verify = verify
@classmethod
@return_on_error("Unknown")
def _get_provider_version(cls) -> str:
"""Check the Providers Manager for the package version."""
manager = ProvidersManager()
hook = manager.hooks[cls.conn_type]
if not hook:
# This gets caught immediately, but without it MyPy complains
# Item "None" of "Optional[HookInfo]" has no attribute "package_name"
# on the following line and static checks fail.
raise ValueError(f"Hook info for {cls.conn_type} not found in the Provider Manager.")
return manager.providers[hook.package_name].version
@staticmethod
def _find_operator_class_name(target_function_name: str) -> str | None:
"""
Given a frame off the stack, return the name of the class that made the call.
This method may raise a ValueError or an IndexError. The caller is
responsible with catching and handling those.
"""
stack = inspect.stack()
# Find the index of the most recent frame which called the provided function name
# and pull that frame off the stack.
target_frames = [frame for frame in stack if frame.function == target_function_name]
if target_frames:
target_frame = target_frames[0][0]
else:
return None
# Get the local variables for that frame.
frame_variables = target_frame.f_locals["self"]
# Get the class object for that frame.
frame_class_object = frame_variables.__class__
# Return the name of the class object.
return frame_class_object.__name__
@staticmethod
def _find_executor_class_name() -> str | None:
"""Inspect the call stack looking for any executor classes and returning the first found."""
stack = inspect.stack()
# Fetch class objects on all frames, looking for one containing an executor (since it
# will inherit from BaseExecutor)
for frame in stack:
classes = []
for name, obj in frame[0].f_globals.items():
if inspect.isclass(obj):
classes.append(name)
if "BaseExecutor" in classes:
return classes[-1]
return None
@return_on_error("Unknown")
def _get_caller(self, target_function_name: str = "execute") -> str:
"""Try to determine the caller of this hook. Whether that be an AWS Operator, Sensor or Executor."""
caller = self._find_operator_class_name(target_function_name)
if caller == "BaseSensorOperator":
# If the result is a BaseSensorOperator, then look for whatever last called "poke".
caller = self._find_operator_class_name("poke")
if not caller:
# Check if we can find an executor
caller = self._find_executor_class_name()
return caller if caller else "Unknown"
@staticmethod
@return_on_error("00000000-0000-0000-0000-000000000000")
def _generate_dag_key() -> str:
"""
Generate a DAG key.
The Object Identifier (OID) namespace is used to salt the dag_id value.
That salted value is used to generate a SHA-1 hash which, by definition,
can not (reasonably) be reversed. No personal data can be inferred or
extracted from the resulting UUID.
"""
return generate_uuid(os.environ.get("AIRFLOW_CTX_DAG_ID"))
@staticmethod
@return_on_error("Unknown")
def _get_airflow_version() -> str:
"""Fetch and return the current Airflow version."""
# This can be a circular import under specific configurations.
# Importing locally to either avoid or catch it if it does happen.
from airflow import __version__ as airflow_version
return airflow_version
def _generate_user_agent_extra_field(self, existing_user_agent_extra: str) -> str:
user_agent_extra_values = [
f"Airflow/{self._get_airflow_version()}",
f"AmPP/{self._get_provider_version()}",
f"Caller/{self._get_caller()}",
f"DagRunKey/{self._generate_dag_key()}",
existing_user_agent_extra or "",
]
return " ".join(user_agent_extra_values).strip()
@cached_property
[docs] def conn_config(self) -> AwsConnectionWrapper:
"""Get the Airflow Connection object and wrap it in helper (cached)."""
connection = None
if self.aws_conn_id:
try:
connection = self.get_connection(self.aws_conn_id)
except AirflowNotFoundException:
self.log.warning(
"Unable to find AWS Connection ID '%s', switching to empty.", self.aws_conn_id
)
return AwsConnectionWrapper(
conn=connection, region_name=self._region_name, botocore_config=self._config, verify=self._verify
)
def _resolve_service_name(self, is_resource_type: bool = False) -> str:
"""Resolve service name based on type or raise an error."""
if exactly_one(self.client_type, self.resource_type):
# It is possible to write simple conditions, however it make mypy unhappy.
if self.client_type:
if is_resource_type:
raise LookupError("Requested `resource_type`, but `client_type` was set instead.")
return self.client_type
elif self.resource_type:
if not is_resource_type:
raise LookupError("Requested `client_type`, but `resource_type` was set instead.")
return self.resource_type
raise ValueError(
f"Either client_type={self.client_type!r} or "
f"resource_type={self.resource_type!r} must be provided, not both."
)
@property
[docs] def service_name(self) -> str:
"""Extracted botocore/boto3 service name from hook parameters."""
return self._resolve_service_name(is_resource_type=bool(self.resource_type))
@property
[docs] def service_config(self) -> dict:
"""Config for hook-specific service from AWS Connection."""
return self.conn_config.get_service_config(service_name=self.service_name)
@property
[docs] def region_name(self) -> str | None:
"""AWS Region Name read-only property."""
return self.conn_config.region_name
@property
[docs] def config(self) -> Config:
"""Configuration for botocore client read-only property."""
return self.conn_config.botocore_config or botocore.config.Config()
@property
[docs] def verify(self) -> bool | str | None:
"""Verify or not SSL certificates boto3 client/resource read-only property."""
return self.conn_config.verify
@cached_property
[docs] def account_id(self) -> str:
"""Return associated AWS Account ID."""
return (
self.get_session(region_name=self.region_name)
.client(
service_name="sts",
endpoint_url=self.conn_config.get_service_endpoint_url("sts"),
config=self.config,
verify=self.verify,
)
.get_caller_identity()["Account"]
)
[docs] def get_session(self, region_name: str | None = None, deferrable: bool = False) -> boto3.session.Session:
"""Get the underlying boto3.session.Session(region_name=region_name)."""
return SessionFactory(
conn=self.conn_config, region_name=region_name, config=self.config
).create_session(deferrable=deferrable)
def _get_config(self, config: Config | None = None) -> Config:
"""
No AWS Operators use the config argument to this method.
Keep backward compatibility with other users who might use it.
"""
if config is None:
config = deepcopy(self.config)
# ignore[union-attr] is required for this block to appease MyPy
# because the user_agent_extra field is generated at runtime.
user_agent_config = Config(
user_agent_extra=self._generate_user_agent_extra_field(
existing_user_agent_extra=config.user_agent_extra # type: ignore[union-attr]
)
)
return config.merge(user_agent_config) # type: ignore[union-attr]
[docs] def get_client_type(
self,
region_name: str | None = None,
config: Config | None = None,
deferrable: bool = False,
) -> boto3.client:
"""Get the underlying boto3 client using boto3 session."""
service_name = self._resolve_service_name(is_resource_type=False)
session = self.get_session(region_name=region_name, deferrable=deferrable)
endpoint_url = self.conn_config.get_service_endpoint_url(service_name=service_name)
if not isinstance(session, boto3.session.Session):
return session.create_client(
service_name=service_name,
endpoint_url=endpoint_url,
config=self._get_config(config),
verify=self.verify,
)
return session.client(
service_name=service_name,
endpoint_url=endpoint_url,
config=self._get_config(config),
verify=self.verify,
)
[docs] def get_resource_type(
self,
region_name: str | None = None,
config: Config | None = None,
) -> boto3.resource:
"""Get the underlying boto3 resource using boto3 session."""
service_name = self._resolve_service_name(is_resource_type=True)
session = self.get_session(region_name=region_name)
return session.resource(
service_name=service_name,
endpoint_url=self.conn_config.get_service_endpoint_url(service_name=service_name),
config=self._get_config(config),
verify=self.verify,
)
@cached_property
[docs] def conn(self) -> BaseAwsConnection:
"""
Get the underlying boto3 client/resource (cached).
:return: boto3.client or boto3.resource
"""
if self.client_type:
return self.get_client_type(region_name=self.region_name)
return self.get_resource_type(region_name=self.region_name)
@property
[docs] def async_conn(self):
"""Get an aiobotocore client to use for async operations."""
if not self.client_type:
raise ValueError("client_type must be specified.")
return self.get_client_type(region_name=self.region_name, deferrable=True)
@cached_property
def _client(self) -> botocore.client.BaseClient:
conn = self.conn
if isinstance(conn, botocore.client.BaseClient):
return conn
return conn.meta.client
@property
@property
[docs] def conn_region_name(self) -> str:
"""Get actual AWS Region Name from Hook connection (cached)."""
return self.conn_client_meta.region_name
@property
[docs] def conn_partition(self) -> str:
"""Get associated AWS Region Partition from Hook connection (cached)."""
return self.conn_client_meta.partition
[docs] def get_conn(self) -> BaseAwsConnection:
"""
Get the underlying boto3 client/resource (cached).
Implemented so that caching works as intended. It exists for compatibility
with subclasses that rely on a super().get_conn() method.
:return: boto3.client or boto3.resource
"""
# Compat shim
return self.conn
[docs] def get_credentials(self, region_name: str | None = None) -> ReadOnlyCredentials:
"""
Get the underlying `botocore.Credentials` object.
This contains the following authentication attributes: access_key, secret_key and token.
By use this method also secret_key and token will mask in tasks logs.
"""
# Credentials are refreshable, so accessing your access key and
# secret key separately can lead to a race condition.
# See https://stackoverflow.com/a/36291428/8283373
creds = self.get_session(region_name=region_name).get_credentials().get_frozen_credentials()
mask_secret(creds.secret_key)
if creds.token:
mask_secret(creds.token)
return creds
[docs] def expand_role(self, role: str, region_name: str | None = None) -> str:
"""
Get the Amazon Resource Name (ARN) for the role.
If IAM role is already an IAM role ARN, the value is returned unchanged.
:param role: IAM role name or ARN
:param region_name: Optional region name to get credentials for
:return: IAM role ARN
"""
if "/" in role:
return role
else:
session = self.get_session(region_name=region_name)
_client = session.client(
service_name="iam",
endpoint_url=self.conn_config.get_service_endpoint_url("iam"),
config=self.config,
verify=self.verify,
)
return _client.get_role(RoleName=role)["Role"]["Arn"]
@staticmethod
[docs] def retry(should_retry: Callable[[Exception], bool]):
"""Repeat requests in response to exceeding a temporary quote limit."""
def retry_decorator(fun: Callable):
@wraps(fun)
def decorator_f(self, *args, **kwargs):
retry_args = getattr(self, "retry_args", None)
if retry_args is None:
return fun(self, *args, **kwargs)
multiplier = retry_args.get("multiplier", 1)
min_limit = retry_args.get("min", 1)
max_limit = retry_args.get("max", 1)
stop_after_delay = retry_args.get("stop_after_delay", 10)
tenacity_before_logger = tenacity.before_log(self.log, logging.INFO) if self.log else None
tenacity_after_logger = tenacity.after_log(self.log, logging.INFO) if self.log else None
default_kwargs = {
"wait": tenacity.wait_exponential(multiplier=multiplier, max=max_limit, min=min_limit),
"retry": tenacity.retry_if_exception(should_retry),
"stop": tenacity.stop_after_delay(stop_after_delay),
"before": tenacity_before_logger,
"after": tenacity_after_logger,
}
return tenacity.retry(**default_kwargs)(fun)(self, *args, **kwargs)
return decorator_f
return retry_decorator
@classmethod
[docs] def get_ui_field_behaviour(cls) -> dict[str, Any]:
"""Return custom UI field behaviour for AWS Connection."""
return {
"hidden_fields": ["host", "schema", "port"],
"relabeling": {
"login": "AWS Access Key ID",
"password": "AWS Secret Access Key",
},
"placeholders": {
"login": "AKIAIOSFODNN7EXAMPLE",
"password": "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY",
"extra": json.dumps(
{
"region_name": "us-east-1",
"session_kwargs": {"profile_name": "default"},
"config_kwargs": {"retries": {"mode": "standard", "max_attempts": 10}},
"role_arn": "arn:aws:iam::123456789098:role/role-name",
"assume_role_method": "assume_role",
"assume_role_kwargs": {"RoleSessionName": "airflow"},
"aws_session_token": "AQoDYXdzEJr...EXAMPLETOKEN",
"endpoint_url": "http://localhost:4566",
},
indent=2,
),
},
}
[docs] def test_connection(self):
"""
Test the AWS connection by call AWS STS (Security Token Service) GetCallerIdentity API.
.. seealso::
https://docs.aws.amazon.com/STS/latest/APIReference/API_GetCallerIdentity.html
"""
try:
session = self.get_session()
conn_info = session.client(
service_name="sts",
endpoint_url=self.conn_config.get_service_endpoint_url("sts", sts_test_connection=True),
).get_caller_identity()
metadata = conn_info.pop("ResponseMetadata", {})
if metadata.get("HTTPStatusCode") != 200:
try:
return False, json.dumps(metadata)
except TypeError:
return False, str(metadata)
conn_info["credentials_method"] = session.get_credentials().method
conn_info["region_name"] = session.region_name
return True, ", ".join(f"{k}={v!r}" for k, v in conn_info.items())
except Exception as e:
return False, f"{type(e).__name__!r} error occurred while testing connection: {e}"
@cached_property
[docs] def waiter_path(self) -> os.PathLike[str] | None:
filename = self.client_type if self.client_type else self.resource_type
path = Path(__file__).parents[1].joinpath(f"waiters/{filename}.json").resolve()
return path if path.exists() else None
[docs] def get_waiter(
self,
waiter_name: str,
parameters: dict[str, str] | None = None,
deferrable: bool = False,
client=None,
) -> Waiter:
"""
Get a waiter by name.
First checks if there is a custom waiter with the provided waiter_name and
uses that if it exists, otherwise it will check the service client for a
waiter that matches the name and pass that through.
If `deferrable` is True, the waiter will be an AIOWaiter, generated from the
client that is passed as a parameter. If `deferrable` is True, `client` must be
provided.
:param waiter_name: The name of the waiter. The name should exactly match the
name of the key in the waiter model file (typically this is CamelCase).
:param parameters: will scan the waiter config for the keys of that dict,
and replace them with the corresponding value. If a custom waiter has
such keys to be expanded, they need to be provided here.
:param deferrable: If True, the waiter is going to be an async custom waiter.
An async client must be provided in that case.
:param client: The client to use for the waiter's operations
"""
from airflow.providers.amazon.aws.waiters.base_waiter import BaseBotoWaiter
if deferrable and not client:
raise ValueError("client must be provided for a deferrable waiter.")
# Currently, the custom waiter doesn't work with resource_type, only client_type is supported.
client = client or self._client
if self.waiter_path and (waiter_name in self._list_custom_waiters()):
# Technically if waiter_name is in custom_waiters then self.waiter_path must
# exist but MyPy doesn't like the fact that self.waiter_path could be None.
with open(self.waiter_path) as config_file:
config = json.loads(config_file.read())
config = self._apply_parameters_value(config, waiter_name, parameters)
return BaseBotoWaiter(client=client, model_config=config, deferrable=deferrable).waiter(
waiter_name
)
# If there is no custom waiter found for the provided name,
# then try checking the service's official waiters.
return client.get_waiter(waiter_name)
@staticmethod
def _apply_parameters_value(config: dict, waiter_name: str, parameters: dict[str, str] | None) -> dict:
"""Replace potential jinja templates in acceptors definition."""
# only process the waiter we're going to use to not raise errors for missing params for other waiters.
acceptors = config["waiters"][waiter_name]["acceptors"]
for a in acceptors:
arg = a["argument"]
template = jinja2.Template(arg, autoescape=False, undefined=jinja2.StrictUndefined)
try:
a["argument"] = template.render(parameters or {})
except jinja2.UndefinedError as e:
raise AirflowException(
f"Parameter was not supplied for templated waiter's acceptor '{arg}'", e
)
return config
[docs] def list_waiters(self) -> list[str]:
"""Return a list containing the names of all waiters for the service, official and custom."""
return [*self._list_official_waiters(), *self._list_custom_waiters()]
def _list_official_waiters(self) -> list[str]:
return self._client.waiter_names
def _list_custom_waiters(self) -> list[str]:
if not self.waiter_path:
return []
with open(self.waiter_path) as config_file:
model_config = json.load(config_file)
return WaiterModel(model_config).waiter_names
[docs]class AwsBaseHook(AwsGenericHook[Union[boto3.client, boto3.resource]]):
"""
Base class for interact with AWS.
This class provide a thin wrapper around the boto3 Python library.
:param aws_conn_id: The Airflow connection used for AWS credentials.
If this is None or empty then the default boto3 behaviour is used. If
running Airflow in a distributed manner and aws_conn_id is None or
empty, then default boto3 configuration would be used (and must be
maintained on each worker node).
:param verify: Whether or not to verify SSL certificates. See:
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
:param region_name: AWS region_name. If not specified then the default boto3 behaviour is used.
:param client_type: Reference to :external:py:meth:`boto3.client service_name \
<boto3.session.Session.client>`, e.g. 'emr', 'batch', 's3', etc.
Mutually exclusive with ``resource_type``.
:param resource_type: Reference to :external:py:meth:`boto3.resource service_name \
<boto3.session.Session.resource>`, e.g. 's3', 'ec2', 'dynamodb', etc.
Mutually exclusive with ``client_type``.
:param config: Configuration for botocore client. See:
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
"""
[docs]def resolve_session_factory() -> type[BaseSessionFactory]:
"""Resolve custom SessionFactory class."""
clazz = conf.getimport("aws", "session_factory", fallback=None)
if not clazz:
return BaseSessionFactory
if not issubclass(clazz, BaseSessionFactory):
raise TypeError(
f"Your custom AWS SessionFactory class `{clazz.__name__}` is not a subclass "
f"of `{BaseSessionFactory.__name__}`."
)
return clazz
[docs]SessionFactory = resolve_session_factory()