WIP/Idea: Pass task output as outlet to dataset trigger params#37888

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WIP/Idea: Pass task output as outlet to dataset trigger params #37888

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11 changes: 9 additions & 2 deletions airflow/example_dags/example_datasets.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
schedule=None,
tags=["produces", "dataset-scheduled"],
) as dag2:
BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

def some_python_callable():
return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down
8 changes: 8 additions & 0 deletions airflow/jobs/scheduler_job_runner.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
events=dataset_events,
)

run_conf = {}
for item in dataset_events:
event: DatasetEvent = item
extra: dict | None = event.extra
if extra:
run_conf.update(extra)
Comment on lines 1286 to 1291

@uranusjruranusjrMar 5, 2024

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This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)

@jscheffljschefflMar 5, 2024

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The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D

Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.

conf vs. params:

Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.
During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.
Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174


dag_run = dag.create_dagrun(
run_id=run_id,
run_type=DagRunType.DATASET_TRIGGERED,
conf=run_conf,
execution_date=exec_date,
data_interval=data_interval,
state=DagRunState.QUEUED,
Expand Down
13 changes: 9 additions & 4 deletions airflow/models/taskinstance.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

try:
if not mark_success:
self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
if not test_mode:
self.refresh_from_db(lock_for_update=True, session=session)
self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
session.add(Log(self.state, self))
session.merge(self).task = self.task
if self.state == TaskInstanceState.SUCCESS:
self._register_dataset_changes(session=session)
self._register_dataset_changes(result, session=session)

session.commit()
if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

return None

def _register_dataset_changes(self, *, session: Session) -> None:
def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
for obj in self.task.outlets or []:
self.log.debug("outlet obj %s", obj)
# Lineage can have other types of objects besides datasets
if isinstance(obj, Dataset):
dataset_manager.register_dataset_change(
task_instance=self,
dataset=obj,
extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
session=session,
)

def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
def _execute_task_with_callbacks(
self, context: Context, test_mode: bool = False, *, session: Session
) -> Any:
"""Prepare Task for Execution."""
from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
Stats.incr("ti_successes", tags=self.stats_tags)

return result

def _execute_task(self, context, task_orig):
"""
Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down
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observer.observe(document.body, { childList: true, subtree: true });
})();
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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WIP/Idea: Pass task output as outlet to dataset trigger params#37888

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WIP/Idea: Pass task output as outlet to dataset trigger params
#37888
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11 changes: 9 additions & 2 deletions
airflow/example_dags/example_datasets.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
 schedule=None,
 tags=["produces", "dataset-scheduled"],
) as dag2:
 BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

 def some_python_callable():
 return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

 PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down

8 changes: 8 additions & 0 deletions
airflow/jobs/scheduler_job_runner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
 events=dataset_events,
 )

 run_conf = {}
 for item in dataset_events:
 event: DatasetEvent = item
 extra: dict | None = event.extra
 if extra:
 run_conf.update(extra)

Comment on lines

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 to 
1291

@uranusjruranusjrMar 5, 2024

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This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)
@jscheffljschefflMar 5, 2024

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The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D
Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.
conf vs. params:
Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.

During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.

Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174

 dag_run = dag.create_dagrun(
 run_id=run_id,
 run_type=DagRunType.DATASET_TRIGGERED,
 conf=run_conf,
 execution_date=exec_date,
 data_interval=data_interval,
 state=DagRunState.QUEUED,
Expand Down

13 changes: 9 additions & 4 deletions
airflow/models/taskinstance.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

 try:
 if not mark_success:
 self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
 if not test_mode:
 self.refresh_from_db(lock_for_update=True, session=session)
 self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
 session.add(Log(self.state, self))
 session.merge(self).task = self.task
 if self.state == TaskInstanceState.SUCCESS:
 self._register_dataset_changes(session=session)
 self._register_dataset_changes(result, session=session)

 session.commit()
 if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

 return None

 def _register_dataset_changes(self, *, session: Session) -> None:
 def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
 for obj in self.task.outlets or []:
 self.log.debug("outlet obj %s", obj)
 # Lineage can have other types of objects besides datasets
 if isinstance(obj, Dataset):
 dataset_manager.register_dataset_change(
 task_instance=self,
 dataset=obj,
 extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
 session=session,
 )

 def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
 def _execute_task_with_callbacks(
 self, context: Context, test_mode: bool = False, *, session: Session
 ) -> Any:
 """Prepare Task for Execution."""
 from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
 Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
 Stats.incr("ti_successes", tags=self.stats_tags)

 return result

 def _execute_task(self, context, task_orig):
 """
 Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down

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// Force GitHub README to respect dark mode
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var style = document.createElement('style');
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}
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WIP/Idea: Pass task output as outlet to dataset trigger params
#37888
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11 changes: 9 additions & 2 deletions
airflow/example_dags/example_datasets.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
 schedule=None,
 tags=["produces", "dataset-scheduled"],
) as dag2:
 BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

 def some_python_callable():
 return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

 PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down

8 changes: 8 additions & 0 deletions
airflow/jobs/scheduler_job_runner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
 events=dataset_events,
 )

 run_conf = {}
 for item in dataset_events:
 event: DatasetEvent = item
 extra: dict | None = event.extra
 if extra:
 run_conf.update(extra)

Comment on lines

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 to 
1291

@uranusjruranusjrMar 5, 2024

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This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)
@jscheffljschefflMar 5, 2024

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ContributorAuthor
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The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D
Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.
conf vs. params:
Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.

During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.

Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174

 dag_run = dag.create_dagrun(
 run_id=run_id,
 run_type=DagRunType.DATASET_TRIGGERED,
 conf=run_conf,
 execution_date=exec_date,
 data_interval=data_interval,
 state=DagRunState.QUEUED,
Expand Down

13 changes: 9 additions & 4 deletions
airflow/models/taskinstance.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

 try:
 if not mark_success:
 self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
 if not test_mode:
 self.refresh_from_db(lock_for_update=True, session=session)
 self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
 session.add(Log(self.state, self))
 session.merge(self).task = self.task
 if self.state == TaskInstanceState.SUCCESS:
 self._register_dataset_changes(session=session)
 self._register_dataset_changes(result, session=session)

 session.commit()
 if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

 return None

 def _register_dataset_changes(self, *, session: Session) -> None:
 def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
 for obj in self.task.outlets or []:
 self.log.debug("outlet obj %s", obj)
 # Lineage can have other types of objects besides datasets
 if isinstance(obj, Dataset):
 dataset_manager.register_dataset_change(
 task_instance=self,
 dataset=obj,
 extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
 session=session,
 )

 def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
 def _execute_task_with_callbacks(
 self, context: Context, test_mode: bool = False, *, session: Session
 ) -> Any:
 """Prepare Task for Execution."""
 from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
 Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
 Stats.incr("ti_successes", tags=self.stats_tags)

 return result

 def _execute_task(self, context, task_orig):
 """
 Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
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WIP/Idea: Pass task output as outlet to dataset trigger params
#37888
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11 changes: 9 additions & 2 deletions
airflow/example_dags/example_datasets.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
 schedule=None,
 tags=["produces", "dataset-scheduled"],
) as dag2:
 BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

 def some_python_callable():
 return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

 PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down

8 changes: 8 additions & 0 deletions
airflow/jobs/scheduler_job_runner.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
 events=dataset_events,
 )

 run_conf = {}
 for item in dataset_events:
 event: DatasetEvent = item
 extra: dict | None = event.extra
 if extra:
 run_conf.update(extra)

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1291

@uranusjruranusjrMar 5, 2024

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This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)
@jscheffljschefflMar 5, 2024

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The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D
Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.
conf vs. params:
Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.

During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.

Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174

 dag_run = dag.create_dagrun(
 run_id=run_id,
 run_type=DagRunType.DATASET_TRIGGERED,
 conf=run_conf,
 execution_date=exec_date,
 data_interval=data_interval,
 state=DagRunState.QUEUED,
Expand Down

13 changes: 9 additions & 4 deletions
airflow/models/taskinstance.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

 try:
 if not mark_success:
 self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
 if not test_mode:
 self.refresh_from_db(lock_for_update=True, session=session)
 self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
 session.add(Log(self.state, self))
 session.merge(self).task = self.task
 if self.state == TaskInstanceState.SUCCESS:
 self._register_dataset_changes(session=session)
 self._register_dataset_changes(result, session=session)

 session.commit()
 if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

 return None

 def _register_dataset_changes(self, *, session: Session) -> None:
 def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
 for obj in self.task.outlets or []:
 self.log.debug("outlet obj %s", obj)
 # Lineage can have other types of objects besides datasets
 if isinstance(obj, Dataset):
 dataset_manager.register_dataset_change(
 task_instance=self,
 dataset=obj,
 extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
 session=session,
 )

 def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
 def _execute_task_with_callbacks(
 self, context: Context, test_mode: bool = False, *, session: Session
 ) -> Any:
 """Prepare Task for Execution."""
 from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
 Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
 Stats.incr("ti_successes", tags=self.stats_tags)

 return result

 def _execute_task(self, context, task_orig):
 """
 Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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WIP/Idea: Pass task output as outlet to dataset trigger params#37888

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WIP/Idea: Pass task output as outlet to dataset trigger params
#37888
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11 changes: 9 additions & 2 deletions
airflow/example_dags/example_datasets.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
 schedule=None,
 tags=["produces", "dataset-scheduled"],
) as dag2:
 BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

 def some_python_callable():
 return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

 PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down

8 changes: 8 additions & 0 deletions
airflow/jobs/scheduler_job_runner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
 events=dataset_events,
 )

 run_conf = {}
 for item in dataset_events:
 event: DatasetEvent = item
 extra: dict | None = event.extra
 if extra:
 run_conf.update(extra)

Comment on lines

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 to 
1291

@uranusjruranusjrMar 5, 2024

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This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)
@jscheffljschefflMar 5, 2024

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ContributorAuthor
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The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D
Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.
conf vs. params:
Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.

During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.

Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174

 dag_run = dag.create_dagrun(
 run_id=run_id,
 run_type=DagRunType.DATASET_TRIGGERED,
 conf=run_conf,
 execution_date=exec_date,
 data_interval=data_interval,
 state=DagRunState.QUEUED,
Expand Down

13 changes: 9 additions & 4 deletions
airflow/models/taskinstance.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

 try:
 if not mark_success:
 self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
 if not test_mode:
 self.refresh_from_db(lock_for_update=True, session=session)
 self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
 session.add(Log(self.state, self))
 session.merge(self).task = self.task
 if self.state == TaskInstanceState.SUCCESS:
 self._register_dataset_changes(session=session)
 self._register_dataset_changes(result, session=session)

 session.commit()
 if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

 return None

 def _register_dataset_changes(self, *, session: Session) -> None:
 def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
 for obj in self.task.outlets or []:
 self.log.debug("outlet obj %s", obj)
 # Lineage can have other types of objects besides datasets
 if isinstance(obj, Dataset):
 dataset_manager.register_dataset_change(
 task_instance=self,
 dataset=obj,
 extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
 session=session,
 )

 def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
 def _execute_task_with_callbacks(
 self, context: Context, test_mode: bool = False, *, session: Session
 ) -> Any:
 """Prepare Task for Execution."""
 from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
 Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
 Stats.incr("ti_successes", tags=self.stats_tags)

 return result

 def _execute_task(self, context, task_orig):
 """
 Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
WIP/Idea: Pass task output as outlet to dataset trigger params#37888

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 Closed

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WIP/Idea: Pass task output as outlet to dataset trigger params
#37888
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11 changes: 9 additions & 2 deletions
airflow/example_dags/example_datasets.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
 schedule=None,
 tags=["produces", "dataset-scheduled"],
) as dag2:
 BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

 def some_python_callable():
 return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

 PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down

8 changes: 8 additions & 0 deletions
airflow/jobs/scheduler_job_runner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
 events=dataset_events,
 )

 run_conf = {}
 for item in dataset_events:
 event: DatasetEvent = item
 extra: dict | None = event.extra
 if extra:
 run_conf.update(extra)

Comment on lines

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 to 
1291

@uranusjruranusjrMar 5, 2024

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This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)
@jscheffljschefflMar 5, 2024

Copy link


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ContributorAuthor
Choose a reason for hiding this comment

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The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D
Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.
conf vs. params:
Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.

During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.

Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174

 dag_run = dag.create_dagrun(
 run_id=run_id,
 run_type=DagRunType.DATASET_TRIGGERED,
 conf=run_conf,
 execution_date=exec_date,
 data_interval=data_interval,
 state=DagRunState.QUEUED,
Expand Down

13 changes: 9 additions & 4 deletions
airflow/models/taskinstance.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

 try:
 if not mark_success:
 self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
 if not test_mode:
 self.refresh_from_db(lock_for_update=True, session=session)
 self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
 session.add(Log(self.state, self))
 session.merge(self).task = self.task
 if self.state == TaskInstanceState.SUCCESS:
 self._register_dataset_changes(session=session)
 self._register_dataset_changes(result, session=session)

 session.commit()
 if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

 return None

 def _register_dataset_changes(self, *, session: Session) -> None:
 def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
 for obj in self.task.outlets or []:
 self.log.debug("outlet obj %s", obj)
 # Lineage can have other types of objects besides datasets
 if isinstance(obj, Dataset):
 dataset_manager.register_dataset_change(
 task_instance=self,
 dataset=obj,
 extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
 session=session,
 )

 def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
 def _execute_task_with_callbacks(
 self, context: Context, test_mode: bool = False, *, session: Session
 ) -> Any:
 """Prepare Task for Execution."""
 from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
 Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
 Stats.incr("ti_successes", tags=self.stats_tags)

 return result

 def _execute_task(self, context, task_orig):
 """
 Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
WIP/Idea: Pass task output as outlet to dataset trigger params#37888

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WIP/Idea: Pass task output as outlet to dataset trigger params
#37888
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11 changes: 9 additions & 2 deletions
airflow/example_dags/example_datasets.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
 schedule=None,
 tags=["produces", "dataset-scheduled"],
) as dag2:
 BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

 def some_python_callable():
 return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

 PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down

8 changes: 8 additions & 0 deletions
airflow/jobs/scheduler_job_runner.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
 events=dataset_events,
 )

 run_conf = {}
 for item in dataset_events:
 event: DatasetEvent = item
 extra: dict | None = event.extra
 if extra:
 run_conf.update(extra)

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@uranusjruranusjrMar 5, 2024

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This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)
@jscheffljschefflMar 5, 2024

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The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D
Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.
conf vs. params:
Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.

During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.

Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174

 dag_run = dag.create_dagrun(
 run_id=run_id,
 run_type=DagRunType.DATASET_TRIGGERED,
 conf=run_conf,
 execution_date=exec_date,
 data_interval=data_interval,
 state=DagRunState.QUEUED,
Expand Down

13 changes: 9 additions & 4 deletions
airflow/models/taskinstance.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

 try:
 if not mark_success:
 self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
 if not test_mode:
 self.refresh_from_db(lock_for_update=True, session=session)
 self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
 session.add(Log(self.state, self))
 session.merge(self).task = self.task
 if self.state == TaskInstanceState.SUCCESS:
 self._register_dataset_changes(session=session)
 self._register_dataset_changes(result, session=session)

 session.commit()
 if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

 return None

 def _register_dataset_changes(self, *, session: Session) -> None:
 def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
 for obj in self.task.outlets or []:
 self.log.debug("outlet obj %s", obj)
 # Lineage can have other types of objects besides datasets
 if isinstance(obj, Dataset):
 dataset_manager.register_dataset_change(
 task_instance=self,
 dataset=obj,
 extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
 session=session,
 )

 def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
 def _execute_task_with_callbacks(
 self, context: Context, test_mode: bool = False, *, session: Session
 ) -> Any:
 """Prepare Task for Execution."""
 from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
 Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
 Stats.incr("ti_successes", tags=self.stats_tags)

 return result

 def _execute_task(self, context, task_orig):
 """
 Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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WIP/Idea: Pass task output as outlet to dataset trigger params#37888

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WIP/Idea: Pass task output as outlet to dataset trigger params
#37888
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11 changes: 9 additions & 2 deletions
airflow/example_dags/example_datasets.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -51,18 +51,21 @@
"""
from __future__ import annotations

import random

import pendulum

from airflow.datasets import Dataset
from airflow.models.dag import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.timetables.datasets import DatasetOrTimeSchedule
from airflow.timetables.trigger import CronTriggerTimetable

# [START dataset_def]
dag1_dataset = Dataset("s3://dag1/output_1.txt", extra={"hi": "bye"})
# [END dataset_def]
dag2_dataset = Dataset("s3://dag2/output_1.txt", extra={"hi": "bye"})
dag2_dataset = Dataset("s3://dag2/output_1.txt")
dag3_dataset = Dataset("s3://dag3/output_3.txt", extra={"hi": "bye"})

with DAG(
Expand All@@ -83,7 +86,11 @@
 schedule=None,
 tags=["produces", "dataset-scheduled"],
) as dag2:
 BashOperator(outlets=[dag2_dataset], task_id="producing_task_2", bash_command="sleep 5")

 def some_python_callable():
 return {"some_context": "dynamic data 123", "random_number": random.randint(1, 100)}

 PythonOperator(python_callable=some_python_callable, outlets=[dag2_dataset], task_id="producing_task_2")

# [START dag_dep]
with DAG(
Expand Down

8 changes: 8 additions & 0 deletions
airflow/jobs/scheduler_job_runner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1283,9 +1283,17 @@ def _create_dag_runs_dataset_triggered(
 events=dataset_events,
 )

 run_conf = {}
 for item in dataset_events:
 event: DatasetEvent = item
 extra: dict | None = event.extra
 if extra:
 run_conf.update(extra)

Comment on lines

1286
 to 
1291

@uranusjruranusjrMar 5, 2024

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Member
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This feels a bit too heavy-handed, but I like the idea of passing in event extras as the downstream DAG run parameters. (Should we use conf or params for this?)
@jscheffljschefflMar 5, 2024

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The code was just an idea - not finally thought through. If you say "heavy handed"... what do you mean? too brute force and the user does not know what comes out? Or do you mean we need a better merging mechanism? Or some hooking to be able to inject a custom merging strategy? Or just "code is ugly" :-D
Background: I would assume that in 90% of cases a single DAG triggers a dataset. THeremight be cases where multiple events come together to trigger. In such case we need to merge extras. I'd assume most times it is "conflict free" but you never know. Might be a feature to have it "last property wins" to collect events but otherwise if users feel there are too many conflicts, individual extras can also be produced "conflict free" with individual keys.
conf vs. params:
Yes, params and dag_run.conf somehow should be merged. I believe this is a leftover in the API from the past. CONF is the dict which is used to trigger a DAG. The conf is persisted as blob with the DagRun.

During runtime the conf is available in the context as dict, representing 1:1 the conf used to trigger. No validation. Just a dict.
params in contrast have default values, conf is setting values on top and the result is JSON validated.

Both üaramsand confare available in the context and can be used. I believre mid-term we should deprecate the usage of conf in the DAG and consolidate to the (more and better functional) params. But for today params only exist during runtime.
@hussein-awala did an attempt here but it dd not make it to finish line: #29174

 dag_run = dag.create_dagrun(
 run_id=run_id,
 run_type=DagRunType.DATASET_TRIGGERED,
 conf=run_conf,
 execution_date=exec_date,
 data_interval=data_interval,
 state=DagRunState.QUEUED,
Expand Down

13 changes: 9 additions & 4 deletions
airflow/models/taskinstance.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -2374,7 +2374,7 @@ def _run_raw_task(

 try:
 if not mark_success:
 self._execute_task_with_callbacks(context, test_mode, session=session)
result = self._execute_task_with_callbacks(context, test_mode, session=session)
 if not test_mode:
 self.refresh_from_db(lock_for_update=True, session=session)
 self.state = TaskInstanceState.SUCCESS
Expand DownExpand Up@@ -2462,7 +2462,7 @@ def _run_raw_task(
 session.add(Log(self.state, self))
 session.merge(self).task = self.task
 if self.state == TaskInstanceState.SUCCESS:
 self._register_dataset_changes(session=session)
 self._register_dataset_changes(result, session=session)

 session.commit()
 if self.state == TaskInstanceState.SUCCESS:
Expand All@@ -2472,18 +2472,21 @@ def _run_raw_task(

 return None

 def _register_dataset_changes(self, *, session: Session) -> None:
 def _register_dataset_changes(self, result: Any, *, session: Session) -> None:
 for obj in self.task.outlets or []:
 self.log.debug("outlet obj %s", obj)
 # Lineage can have other types of objects besides datasets
 if isinstance(obj, Dataset):
 dataset_manager.register_dataset_change(
 task_instance=self,
 dataset=obj,
 extra=obj.extra or result if isinstance(result, dict) else {self.task_id: result},
 session=session,
 )

 def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
 def _execute_task_with_callbacks(
 self, context: Context, test_mode: bool = False, *, session: Session
 ) -> Any:
 """Prepare Task for Execution."""
 from airflow.models.renderedtifields import RenderedTaskInstanceFields

Expand DownExpand Up@@ -2571,6 +2574,8 @@ def signal_handler(signum, frame):
 Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
 Stats.incr("ti_successes", tags=self.stats_tags)

 return result

 def _execute_task(self, context, task_orig):
 """
 Execute Task (optionally with a Timeout) and push Xcom results.
Expand Down