Airflow deprecation warning Invalid arguments were passed
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0
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I have the following code on Airflow 1.9:
import_op = MySqlToGoogleCloudStorageOperator(
task_id='import',
mysql_conn_id='oproduction',
google_cloud_storage_conn_id='gcpm',
provide_context=True,
approx_max_file_size_bytes = 100000000, #100MB per file
sql = 'import.sql',
params='next_to_import': NEXT_TO_IMPORT, 'table_name' : TABLE_NAME,
bucket=GCS_BUCKET_ID,
filename=file_name_orders,
dag=dag)
Why does it genereates:
/usr/local/lib/python2.7/dist-packages/airflow/models.py:2160:
PendingDeprecationWarning: Invalid arguments were passed to
MySqlToGoogleCloudStorageOperator. Support for passing such arguments
will be dropped in Airflow 2.0. Invalid arguments were:
*args: ()
**kwargs: 'provide_context': True category=PendingDeprecationWarning
What is the problem with the provide_context
? To the best of my knowledge it is needed for the usage of params
.
airflow
add a comment |
up vote
0
down vote
favorite
I have the following code on Airflow 1.9:
import_op = MySqlToGoogleCloudStorageOperator(
task_id='import',
mysql_conn_id='oproduction',
google_cloud_storage_conn_id='gcpm',
provide_context=True,
approx_max_file_size_bytes = 100000000, #100MB per file
sql = 'import.sql',
params='next_to_import': NEXT_TO_IMPORT, 'table_name' : TABLE_NAME,
bucket=GCS_BUCKET_ID,
filename=file_name_orders,
dag=dag)
Why does it genereates:
/usr/local/lib/python2.7/dist-packages/airflow/models.py:2160:
PendingDeprecationWarning: Invalid arguments were passed to
MySqlToGoogleCloudStorageOperator. Support for passing such arguments
will be dropped in Airflow 2.0. Invalid arguments were:
*args: ()
**kwargs: 'provide_context': True category=PendingDeprecationWarning
What is the problem with the provide_context
? To the best of my knowledge it is needed for the usage of params
.
airflow
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have the following code on Airflow 1.9:
import_op = MySqlToGoogleCloudStorageOperator(
task_id='import',
mysql_conn_id='oproduction',
google_cloud_storage_conn_id='gcpm',
provide_context=True,
approx_max_file_size_bytes = 100000000, #100MB per file
sql = 'import.sql',
params='next_to_import': NEXT_TO_IMPORT, 'table_name' : TABLE_NAME,
bucket=GCS_BUCKET_ID,
filename=file_name_orders,
dag=dag)
Why does it genereates:
/usr/local/lib/python2.7/dist-packages/airflow/models.py:2160:
PendingDeprecationWarning: Invalid arguments were passed to
MySqlToGoogleCloudStorageOperator. Support for passing such arguments
will be dropped in Airflow 2.0. Invalid arguments were:
*args: ()
**kwargs: 'provide_context': True category=PendingDeprecationWarning
What is the problem with the provide_context
? To the best of my knowledge it is needed for the usage of params
.
airflow
I have the following code on Airflow 1.9:
import_op = MySqlToGoogleCloudStorageOperator(
task_id='import',
mysql_conn_id='oproduction',
google_cloud_storage_conn_id='gcpm',
provide_context=True,
approx_max_file_size_bytes = 100000000, #100MB per file
sql = 'import.sql',
params='next_to_import': NEXT_TO_IMPORT, 'table_name' : TABLE_NAME,
bucket=GCS_BUCKET_ID,
filename=file_name_orders,
dag=dag)
Why does it genereates:
/usr/local/lib/python2.7/dist-packages/airflow/models.py:2160:
PendingDeprecationWarning: Invalid arguments were passed to
MySqlToGoogleCloudStorageOperator. Support for passing such arguments
will be dropped in Airflow 2.0. Invalid arguments were:
*args: ()
**kwargs: 'provide_context': True category=PendingDeprecationWarning
What is the problem with the provide_context
? To the best of my knowledge it is needed for the usage of params
.
airflow
airflow
asked Nov 11 at 12:27
Luis
557
557
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
up vote
0
down vote
accepted
provide_context
is not needed for params
.
params
parameter (dict
type) can be passed to any Operator.
You would mostly use provide_context
with PythonOperator
, BranchPythonOperator
. A good example is https://airflow.readthedocs.io/en/latest/howto/operator.html#pythonoperator.
MySqlToGoogleCloudStorageOperator
has no parameter provide_context
, hence it is passed in **kwargs
and you get Deprecation warning.
If you check docstring of PythonOperator
for provide_context
:
if set to true, Airflow will pass a set of keyword arguments that can
be used in your function. This set of kwargs correspond exactly to
what you can use in your jinja templates. For this to work, you need
to define**kwargs
in your function header.
It has the following code if you check the source code:
if self.provide_context:
context.update(self.op_kwargs)
context['templates_dict'] = self.templates_dict
self.op_kwargs = context
So in simple terms, it passes the following dictionary with templates_dict
to your function pass in python_callable
:
'END_DATE': ds,
'conf': configuration,
'dag': task.dag,
'dag_run': dag_run,
'ds': ds,
'ds_nodash': ds_nodash,
'end_date': ds,
'execution_date': self.execution_date,
'latest_date': ds,
'macros': macros,
'params': params,
'run_id': run_id,
'tables': tables,
'task': task,
'task_instance': self,
'task_instance_key_str': ti_key_str,
'test_mode': self.test_mode,
'ti': self,
'tomorrow_ds': tomorrow_ds,
'tomorrow_ds_nodash': tomorrow_ds_nodash,
'ts': ts,
'ts_nodash': ts_nodash,
'yesterday_ds': yesterday_ds,
'yesterday_ds_nodash': yesterday_ds_nodash,
So this can be used in the function as follows:
def print_context(ds, **kwargs):
pprint(kwargs)
ti = context['task_instance']
exec_date = context['execution_date']
print(ds)
return 'Whatever you return gets printed in the logs'
run_this = PythonOperator(
task_id='print_the_context',
provide_context=True,
python_callable=print_context,
dag=dag,
)
OK. So can you please explain why provide_context is even needed? provide_context will always be true when params is added for PythonOperator. It seems like a parameter that Airflow can figure out it's value by it's own... It gives nothing to ask the user to specify it
– Luis
Nov 12 at 9:51
You would useprovide_context
so that it passes the variables to the function passed inpython_callable
inPythonOperator
– kaxil
Nov 12 at 11:06
I have updated the answer with this info
– kaxil
Nov 12 at 11:18
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
provide_context
is not needed for params
.
params
parameter (dict
type) can be passed to any Operator.
You would mostly use provide_context
with PythonOperator
, BranchPythonOperator
. A good example is https://airflow.readthedocs.io/en/latest/howto/operator.html#pythonoperator.
MySqlToGoogleCloudStorageOperator
has no parameter provide_context
, hence it is passed in **kwargs
and you get Deprecation warning.
If you check docstring of PythonOperator
for provide_context
:
if set to true, Airflow will pass a set of keyword arguments that can
be used in your function. This set of kwargs correspond exactly to
what you can use in your jinja templates. For this to work, you need
to define**kwargs
in your function header.
It has the following code if you check the source code:
if self.provide_context:
context.update(self.op_kwargs)
context['templates_dict'] = self.templates_dict
self.op_kwargs = context
So in simple terms, it passes the following dictionary with templates_dict
to your function pass in python_callable
:
'END_DATE': ds,
'conf': configuration,
'dag': task.dag,
'dag_run': dag_run,
'ds': ds,
'ds_nodash': ds_nodash,
'end_date': ds,
'execution_date': self.execution_date,
'latest_date': ds,
'macros': macros,
'params': params,
'run_id': run_id,
'tables': tables,
'task': task,
'task_instance': self,
'task_instance_key_str': ti_key_str,
'test_mode': self.test_mode,
'ti': self,
'tomorrow_ds': tomorrow_ds,
'tomorrow_ds_nodash': tomorrow_ds_nodash,
'ts': ts,
'ts_nodash': ts_nodash,
'yesterday_ds': yesterday_ds,
'yesterday_ds_nodash': yesterday_ds_nodash,
So this can be used in the function as follows:
def print_context(ds, **kwargs):
pprint(kwargs)
ti = context['task_instance']
exec_date = context['execution_date']
print(ds)
return 'Whatever you return gets printed in the logs'
run_this = PythonOperator(
task_id='print_the_context',
provide_context=True,
python_callable=print_context,
dag=dag,
)
OK. So can you please explain why provide_context is even needed? provide_context will always be true when params is added for PythonOperator. It seems like a parameter that Airflow can figure out it's value by it's own... It gives nothing to ask the user to specify it
– Luis
Nov 12 at 9:51
You would useprovide_context
so that it passes the variables to the function passed inpython_callable
inPythonOperator
– kaxil
Nov 12 at 11:06
I have updated the answer with this info
– kaxil
Nov 12 at 11:18
add a comment |
up vote
0
down vote
accepted
provide_context
is not needed for params
.
params
parameter (dict
type) can be passed to any Operator.
You would mostly use provide_context
with PythonOperator
, BranchPythonOperator
. A good example is https://airflow.readthedocs.io/en/latest/howto/operator.html#pythonoperator.
MySqlToGoogleCloudStorageOperator
has no parameter provide_context
, hence it is passed in **kwargs
and you get Deprecation warning.
If you check docstring of PythonOperator
for provide_context
:
if set to true, Airflow will pass a set of keyword arguments that can
be used in your function. This set of kwargs correspond exactly to
what you can use in your jinja templates. For this to work, you need
to define**kwargs
in your function header.
It has the following code if you check the source code:
if self.provide_context:
context.update(self.op_kwargs)
context['templates_dict'] = self.templates_dict
self.op_kwargs = context
So in simple terms, it passes the following dictionary with templates_dict
to your function pass in python_callable
:
'END_DATE': ds,
'conf': configuration,
'dag': task.dag,
'dag_run': dag_run,
'ds': ds,
'ds_nodash': ds_nodash,
'end_date': ds,
'execution_date': self.execution_date,
'latest_date': ds,
'macros': macros,
'params': params,
'run_id': run_id,
'tables': tables,
'task': task,
'task_instance': self,
'task_instance_key_str': ti_key_str,
'test_mode': self.test_mode,
'ti': self,
'tomorrow_ds': tomorrow_ds,
'tomorrow_ds_nodash': tomorrow_ds_nodash,
'ts': ts,
'ts_nodash': ts_nodash,
'yesterday_ds': yesterday_ds,
'yesterday_ds_nodash': yesterday_ds_nodash,
So this can be used in the function as follows:
def print_context(ds, **kwargs):
pprint(kwargs)
ti = context['task_instance']
exec_date = context['execution_date']
print(ds)
return 'Whatever you return gets printed in the logs'
run_this = PythonOperator(
task_id='print_the_context',
provide_context=True,
python_callable=print_context,
dag=dag,
)
OK. So can you please explain why provide_context is even needed? provide_context will always be true when params is added for PythonOperator. It seems like a parameter that Airflow can figure out it's value by it's own... It gives nothing to ask the user to specify it
– Luis
Nov 12 at 9:51
You would useprovide_context
so that it passes the variables to the function passed inpython_callable
inPythonOperator
– kaxil
Nov 12 at 11:06
I have updated the answer with this info
– kaxil
Nov 12 at 11:18
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
provide_context
is not needed for params
.
params
parameter (dict
type) can be passed to any Operator.
You would mostly use provide_context
with PythonOperator
, BranchPythonOperator
. A good example is https://airflow.readthedocs.io/en/latest/howto/operator.html#pythonoperator.
MySqlToGoogleCloudStorageOperator
has no parameter provide_context
, hence it is passed in **kwargs
and you get Deprecation warning.
If you check docstring of PythonOperator
for provide_context
:
if set to true, Airflow will pass a set of keyword arguments that can
be used in your function. This set of kwargs correspond exactly to
what you can use in your jinja templates. For this to work, you need
to define**kwargs
in your function header.
It has the following code if you check the source code:
if self.provide_context:
context.update(self.op_kwargs)
context['templates_dict'] = self.templates_dict
self.op_kwargs = context
So in simple terms, it passes the following dictionary with templates_dict
to your function pass in python_callable
:
'END_DATE': ds,
'conf': configuration,
'dag': task.dag,
'dag_run': dag_run,
'ds': ds,
'ds_nodash': ds_nodash,
'end_date': ds,
'execution_date': self.execution_date,
'latest_date': ds,
'macros': macros,
'params': params,
'run_id': run_id,
'tables': tables,
'task': task,
'task_instance': self,
'task_instance_key_str': ti_key_str,
'test_mode': self.test_mode,
'ti': self,
'tomorrow_ds': tomorrow_ds,
'tomorrow_ds_nodash': tomorrow_ds_nodash,
'ts': ts,
'ts_nodash': ts_nodash,
'yesterday_ds': yesterday_ds,
'yesterday_ds_nodash': yesterday_ds_nodash,
So this can be used in the function as follows:
def print_context(ds, **kwargs):
pprint(kwargs)
ti = context['task_instance']
exec_date = context['execution_date']
print(ds)
return 'Whatever you return gets printed in the logs'
run_this = PythonOperator(
task_id='print_the_context',
provide_context=True,
python_callable=print_context,
dag=dag,
)
provide_context
is not needed for params
.
params
parameter (dict
type) can be passed to any Operator.
You would mostly use provide_context
with PythonOperator
, BranchPythonOperator
. A good example is https://airflow.readthedocs.io/en/latest/howto/operator.html#pythonoperator.
MySqlToGoogleCloudStorageOperator
has no parameter provide_context
, hence it is passed in **kwargs
and you get Deprecation warning.
If you check docstring of PythonOperator
for provide_context
:
if set to true, Airflow will pass a set of keyword arguments that can
be used in your function. This set of kwargs correspond exactly to
what you can use in your jinja templates. For this to work, you need
to define**kwargs
in your function header.
It has the following code if you check the source code:
if self.provide_context:
context.update(self.op_kwargs)
context['templates_dict'] = self.templates_dict
self.op_kwargs = context
So in simple terms, it passes the following dictionary with templates_dict
to your function pass in python_callable
:
'END_DATE': ds,
'conf': configuration,
'dag': task.dag,
'dag_run': dag_run,
'ds': ds,
'ds_nodash': ds_nodash,
'end_date': ds,
'execution_date': self.execution_date,
'latest_date': ds,
'macros': macros,
'params': params,
'run_id': run_id,
'tables': tables,
'task': task,
'task_instance': self,
'task_instance_key_str': ti_key_str,
'test_mode': self.test_mode,
'ti': self,
'tomorrow_ds': tomorrow_ds,
'tomorrow_ds_nodash': tomorrow_ds_nodash,
'ts': ts,
'ts_nodash': ts_nodash,
'yesterday_ds': yesterday_ds,
'yesterday_ds_nodash': yesterday_ds_nodash,
So this can be used in the function as follows:
def print_context(ds, **kwargs):
pprint(kwargs)
ti = context['task_instance']
exec_date = context['execution_date']
print(ds)
return 'Whatever you return gets printed in the logs'
run_this = PythonOperator(
task_id='print_the_context',
provide_context=True,
python_callable=print_context,
dag=dag,
)
edited Nov 12 at 11:18
answered Nov 11 at 19:25
kaxil
2,133627
2,133627
OK. So can you please explain why provide_context is even needed? provide_context will always be true when params is added for PythonOperator. It seems like a parameter that Airflow can figure out it's value by it's own... It gives nothing to ask the user to specify it
– Luis
Nov 12 at 9:51
You would useprovide_context
so that it passes the variables to the function passed inpython_callable
inPythonOperator
– kaxil
Nov 12 at 11:06
I have updated the answer with this info
– kaxil
Nov 12 at 11:18
add a comment |
OK. So can you please explain why provide_context is even needed? provide_context will always be true when params is added for PythonOperator. It seems like a parameter that Airflow can figure out it's value by it's own... It gives nothing to ask the user to specify it
– Luis
Nov 12 at 9:51
You would useprovide_context
so that it passes the variables to the function passed inpython_callable
inPythonOperator
– kaxil
Nov 12 at 11:06
I have updated the answer with this info
– kaxil
Nov 12 at 11:18
OK. So can you please explain why provide_context is even needed? provide_context will always be true when params is added for PythonOperator. It seems like a parameter that Airflow can figure out it's value by it's own... It gives nothing to ask the user to specify it
– Luis
Nov 12 at 9:51
OK. So can you please explain why provide_context is even needed? provide_context will always be true when params is added for PythonOperator. It seems like a parameter that Airflow can figure out it's value by it's own... It gives nothing to ask the user to specify it
– Luis
Nov 12 at 9:51
You would use
provide_context
so that it passes the variables to the function passed in python_callable
in PythonOperator
– kaxil
Nov 12 at 11:06
You would use
provide_context
so that it passes the variables to the function passed in python_callable
in PythonOperator
– kaxil
Nov 12 at 11:06
I have updated the answer with this info
– kaxil
Nov 12 at 11:18
I have updated the answer with this info
– kaxil
Nov 12 at 11:18
add a comment |
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