# 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 __future__ import annotations
import datetime
import json
import logging
import os
from contextlib import suppress
from functools import wraps
from typing import TYPE_CHECKING, Any, Iterable
from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
import attrs
from attrs import asdict
# TODO: move this maybe to Airflow's logic?
from openlineage.client.utils import RedactMixin
from airflow.compat.functools import cache
from airflow.configuration import conf
from airflow.providers.openlineage.plugins.facets import (
AirflowMappedTaskRunFacet,
AirflowRunFacet,
)
from airflow.utils.context import AirflowContextDeprecationWarning
from airflow.utils.log.secrets_masker import Redactable, Redacted, SecretsMasker, should_hide_value_for_key
if TYPE_CHECKING:
from airflow.models import DAG, BaseOperator, Connection, DagRun, TaskInstance
[docs]log = logging.getLogger(__name__)
_NOMINAL_TIME_FORMAT = "%Y-%m-%dT%H:%M:%S.%fZ"
[docs]def openlineage_job_name(dag_id: str, task_id: str) -> str:
return f"{dag_id}.{task_id}"
[docs]def get_operator_class(task: BaseOperator) -> type:
if task.__class__.__name__ in ("DecoratedMappedOperator", "MappedOperator"):
return task.operator_class
return task.__class__
[docs]def to_json_encodable(task: BaseOperator) -> dict[str, object]:
def _task_encoder(obj):
from airflow.models import DAG
if isinstance(obj, datetime.datetime):
return obj.isoformat()
elif isinstance(obj, DAG):
return {
"dag_id": obj.dag_id,
"tags": obj.tags,
"schedule_interval": obj.schedule_interval,
"timetable": obj.timetable.serialize(),
}
else:
return str(obj)
return json.loads(json.dumps(task.__dict__, default=_task_encoder))
[docs]def url_to_https(url) -> str | None:
# Ensure URL exists
if not url:
return None
base_url = None
if url.startswith("git@"):
part = url.split("git@")[1:2]
if part:
base_url = f'https://{part[0].replace(":", "/", 1)}'
elif url.startswith("https://"):
base_url = url
if not base_url:
raise ValueError(f"Unable to extract location from: {url}")
if base_url.endswith(".git"):
base_url = base_url[:-4]
return base_url
[docs]def redacted_connection_uri(conn: Connection, filtered_params=None, filtered_prefixes=None):
"""
Return the connection URI for the given Connection.
This method additionally filters URI by removing query parameters that are known to carry sensitive data
like username, password, access key.
"""
if filtered_prefixes is None:
filtered_prefixes = []
if filtered_params is None:
filtered_params = []
def filter_key_params(k: str):
return k not in filtered_params and any(substr in k for substr in filtered_prefixes)
conn_uri = conn.get_uri()
parsed = urlparse(conn_uri)
# Remove username and password
netloc = f"{parsed.hostname}" + (f":{parsed.port}" if parsed.port else "")
parsed = parsed._replace(netloc=netloc)
if parsed.query:
query_dict = dict(parse_qsl(parsed.query))
if conn.EXTRA_KEY in query_dict:
query_dict = json.loads(query_dict[conn.EXTRA_KEY])
filtered_qs = {k: v for k, v in query_dict.items() if not filter_key_params(k)}
parsed = parsed._replace(query=urlencode(filtered_qs))
return urlunparse(parsed)
[docs]def get_connection(conn_id) -> Connection | None:
from airflow.hooks.base import BaseHook
with suppress(Exception):
return BaseHook.get_connection(conn_id=conn_id)
return None
[docs]def get_job_name(task):
return f"{task.dag_id}.{task.task_id}"
[docs]def get_custom_facets(task_instance: TaskInstance | None = None) -> dict[str, Any]:
custom_facets = {}
# check for -1 comes from SmartSensor compatibility with dynamic task mapping
# this comes from Airflow code
if hasattr(task_instance, "map_index") and getattr(task_instance, "map_index") != -1:
custom_facets["airflow_mappedTask"] = AirflowMappedTaskRunFacet.from_task_instance(task_instance)
return custom_facets
[docs]class InfoJsonEncodable(dict):
"""
Airflow objects might not be json-encodable overall.
The class provides additional attributes to control
what and how is encoded:
* renames: a dictionary of attribute name changes
* | casts: a dictionary consisting of attribute names
| and corresponding methods that should change
| object value
* includes: list of attributes to be included in encoding
* excludes: list of attributes to be excluded from encoding
Don't use both includes and excludes.
"""
[docs] renames: dict[str, str] = {}
[docs] casts: dict[str, Any] = {}
[docs] includes: list[str] = []
[docs] excludes: list[str] = []
def __init__(self, obj):
self.obj = obj
self._fields = []
self._cast_fields()
self._rename_fields()
self._include_fields()
dict.__init__(
self,
**{field: InfoJsonEncodable._cast_basic_types(getattr(self, field)) for field in self._fields},
)
@staticmethod
def _cast_basic_types(value):
if isinstance(value, datetime.datetime):
return value.isoformat()
if isinstance(value, (set, list, tuple)):
return str(list(value))
return value
def _rename_fields(self):
for field, renamed in self.renames.items():
if hasattr(self.obj, field):
setattr(self, renamed, getattr(self.obj, field))
self._fields.append(renamed)
def _cast_fields(self):
for field, func in self.casts.items():
setattr(self, field, func(self.obj))
self._fields.append(field)
def _include_fields(self):
if self.includes and self.excludes:
raise Exception("Don't use both includes and excludes.")
if self.includes:
for field in self.includes:
if field not in self._fields and hasattr(self.obj, field):
setattr(self, field, getattr(self.obj, field))
self._fields.append(field)
else:
for field, val in self.obj.__dict__.items():
if field not in self._fields and field not in self.excludes and field not in self.renames:
setattr(self, field, val)
self._fields.append(field)
[docs]class DagInfo(InfoJsonEncodable):
"""Defines encoding DAG object to JSON."""
[docs] includes = ["dag_id", "schedule_interval", "tags", "start_date"]
[docs] casts = {"timetable": lambda dag: dag.timetable.serialize() if getattr(dag, "timetable", None) else None}
[docs] renames = {"_dag_id": "dag_id"}
[docs]class DagRunInfo(InfoJsonEncodable):
"""Defines encoding DagRun object to JSON."""
[docs] includes = [
"conf",
"dag_id",
"data_interval_start",
"data_interval_end",
"external_trigger",
"run_id",
"run_type",
"start_date",
]
[docs]class TaskInstanceInfo(InfoJsonEncodable):
"""Defines encoding TaskInstance object to JSON."""
[docs] includes = ["duration", "try_number", "pool"]
[docs] casts = {
"map_index": lambda ti: ti.map_index
if hasattr(ti, "map_index") and getattr(ti, "map_index") != -1
else None
}
[docs]class TaskInfo(InfoJsonEncodable):
"""Defines encoding BaseOperator/AbstractOperator object to JSON."""
[docs] renames = {
"_BaseOperator__init_kwargs": "args",
"_BaseOperator__from_mapped": "mapped",
"_downstream_task_ids": "downstream_task_ids",
"_upstream_task_ids": "upstream_task_ids",
}
[docs] excludes = [
"_BaseOperator__instantiated",
"_dag",
"_hook",
"_log",
"_outlets",
"_inlets",
"_lock_for_execution",
"handler",
"params",
"python_callable",
"retry_delay",
]
[docs] casts = {
"operator_class": lambda task: task.task_type,
"task_group": lambda task: TaskGroupInfo(task.task_group)
if hasattr(task, "task_group") and getattr(task.task_group, "_group_id", None)
else None,
}
[docs]class TaskGroupInfo(InfoJsonEncodable):
"""Defines encoding TaskGroup object to JSON."""
[docs] renames = {
"_group_id": "group_id",
}
[docs] includes = [
"downstream_group_ids",
"downstream_task_ids",
"prefix_group_id",
"tooltip",
"upstream_group_ids",
"upstream_task_ids",
]
[docs]def get_airflow_run_facet(
dag_run: DagRun,
dag: DAG,
task_instance: TaskInstance,
task: BaseOperator,
task_uuid: str,
):
return {
"airflow": json.loads(
json.dumps(
asdict(
AirflowRunFacet(
dag=DagInfo(dag),
dagRun=DagRunInfo(dag_run),
taskInstance=TaskInstanceInfo(task_instance),
task=TaskInfo(task),
taskUuid=task_uuid,
)
),
default=str,
)
)
}
[docs]class OpenLineageRedactor(SecretsMasker):
"""
This class redacts sensitive data similar to SecretsMasker in Airflow logs.
The difference is that our default max recursion depth is way higher - due to
the structure of OL events we need more depth.
Additionally, we allow data structures to specify data that needs not to be
redacted by specifying _skip_redact list by deriving RedactMixin.
"""
@classmethod
[docs] def from_masker(cls, other: SecretsMasker) -> OpenLineageRedactor:
instance = cls()
instance.patterns = other.patterns
instance.replacer = other.replacer
return instance
def _redact(self, item: Redactable, name: str | None, depth: int, max_depth: int) -> Redacted:
if depth > max_depth:
return item
try:
# It's impossible to check the type of variable in a dict without accessing it, and
# this already causes warning - so suppress it
with suppress(AirflowContextDeprecationWarning):
if type(item).__name__ == "Proxy":
# Those are deprecated values in _DEPRECATION_REPLACEMENTS
# in airflow.utils.context.Context
return "<<non-redactable: Proxy>>"
if name and should_hide_value_for_key(name):
return self._redact_all(item, depth, max_depth)
if attrs.has(type(item)):
# TODO: FIXME when mypy gets compatible with new attrs
for dict_key, subval in attrs.asdict(
item, # type: ignore[arg-type]
recurse=False,
).items():
if _is_name_redactable(dict_key, item):
setattr(
item,
dict_key,
self._redact(subval, name=dict_key, depth=(depth + 1), max_depth=max_depth),
)
return item
elif is_json_serializable(item) and hasattr(item, "__dict__"):
for dict_key, subval in item.__dict__.items():
if type(subval).__name__ == "Proxy":
return "<<non-redactable: Proxy>>"
if _is_name_redactable(dict_key, item):
setattr(
item,
dict_key,
self._redact(subval, name=dict_key, depth=(depth + 1), max_depth=max_depth),
)
return item
else:
return super()._redact(item, name, depth, max_depth)
except Exception as exc:
log.warning("Unable to redact %r. Error was: %s: %s", item, type(exc).__name__, exc)
return item
[docs]def is_json_serializable(item):
try:
json.dumps(item)
return True
except (TypeError, ValueError):
return False
def _is_name_redactable(name, redacted):
if not issubclass(redacted.__class__, RedactMixin):
return not name.startswith("_")
return name not in redacted.skip_redact
[docs]def print_warning(log):
def decorator(f):
@wraps(f)
def wrapper(*args, **kwargs):
try:
return f(*args, **kwargs)
except Exception as e:
log.warning(e)
return wrapper
return decorator
@cache
[docs]def is_source_enabled() -> bool:
source_var = conf.get(
"openlineage", "disable_source_code", fallback=os.getenv("OPENLINEAGE_AIRFLOW_DISABLE_SOURCE_CODE")
)
return isinstance(source_var, str) and source_var.lower() not in ("true", "1", "t")
[docs]def get_filtered_unknown_operator_keys(operator: BaseOperator) -> dict:
not_required_keys = {"dag", "task_group"}
return {attr: value for attr, value in operator.__dict__.items() if attr not in not_required_keys}
[docs]def normalize_sql(sql: str | Iterable[str]):
if isinstance(sql, str):
sql = [stmt for stmt in sql.split(";") if stmt != ""]
sql = [obj for stmt in sql for obj in stmt.split(";") if obj != ""]
return ";\n".join(sql)
