Lineage¶
Note
Lineage support is very experimental and subject to change.
Airflow can help track origins of data, what happens to it and where it moves over time. This can aid having audit trails and data governance, but also debugging of data flows.
Airflow tracks data by means of inlets and outlets of the tasks. Let’s work from an example and see how it works.
from airflow.operators.bash_operator import BashOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.lineage.datasets import File
from airflow.models import DAG
from datetime import timedelta
FILE_CATEGORIES = ["CAT1", "CAT2", "CAT3"]
args = {
'owner': 'Airflow',
'start_date': airflow.utils.dates.days_ago(2)
}
dag = DAG(
dag_id='example_lineage', default_args=args,
schedule_interval='0 0 * * *',
dagrun_timeout=timedelta(minutes=60))
f_final = File("/tmp/final")
run_this_last = DummyOperator(task_id='run_this_last', dag=dag,
inlets={"auto": True},
outlets={"datasets": [f_final,]})
f_in = File("/tmp/whole_directory/")
outlets = []
for file in FILE_CATEGORIES:
f_out = File("/tmp/{}/{{{{ execution_date }}}}".format(file))
outlets.append(f_out)
run_this = BashOperator(
task_id='run_me_first', bash_command='echo 1', dag=dag,
inlets={"datasets": [f_in,]},
outlets={"datasets": outlets}
)
run_this.set_downstream(run_this_last)
Tasks take the parameters inlets
and outlets
.
Inlets can be manually defined by the following options:
by a list of dataset
{"datasets": [dataset1, dataset2]}
can be configured to look for outlets from upstream tasks
{"task_ids": ["task_id1", "task_id2"]}
can be configured to pick up outlets from direct upstream tasks
{"auto": True}
a combination of them
Outlets are defined as list of dataset {"datasets": [dataset1, dataset2]}
. Any fields for the dataset are templated with
the context when the task is being executed.
Note
Operators can add inlets and outlets automatically if the operator supports it.
In the example DAG task run_me_first
is a BashOperator that takes 3 inlets: CAT1
, CAT2
, CAT3
, that are
generated from a list. Note that execution_date
is a templated field and will be rendered when the task is running.
Note
Behind the scenes Airflow prepares the lineage metadata as part of the pre_execute
method of a task. When the task
has finished execution post_execute
is called and lineage metadata is pushed into XCOM. Thus if you are creating
your own operators that override this method make sure to decorate your method with prepare_lineage
and apply_lineage
respectively.
Apache Atlas¶
Airflow can send its lineage metadata to Apache Atlas. You need to enable the atlas
backend and configure it
properly, e.g. in your airflow.cfg
:
[lineage]
backend = airflow.lineage.backend.atlas.AtlasBackend
[atlas]
username = my_username
password = my_password
host = host
port = 21000
Please make sure to have the atlasclient
package installed.