Passing args, kwargs, to run_in_executor
up vote
1
down vote
favorite
I am trying to pass arguments to run_in_executor
like so:
loop.run_in_executor(None, update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
})
However, I get the following error:
run_in_executor() got an unexpected keyword argument 'data'
Is there a generic way to pass args to this function?
python python-3.x
add a comment |
up vote
1
down vote
favorite
I am trying to pass arguments to run_in_executor
like so:
loop.run_in_executor(None, update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
})
However, I get the following error:
run_in_executor() got an unexpected keyword argument 'data'
Is there a generic way to pass args to this function?
python python-3.x
what is the definition ofupdate_contacts
?
– Netwave
Nov 19 at 4:35
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I am trying to pass arguments to run_in_executor
like so:
loop.run_in_executor(None, update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
})
However, I get the following error:
run_in_executor() got an unexpected keyword argument 'data'
Is there a generic way to pass args to this function?
python python-3.x
I am trying to pass arguments to run_in_executor
like so:
loop.run_in_executor(None, update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
})
However, I get the following error:
run_in_executor() got an unexpected keyword argument 'data'
Is there a generic way to pass args to this function?
python python-3.x
python python-3.x
asked Nov 19 at 4:12
David L
35010
35010
what is the definition ofupdate_contacts
?
– Netwave
Nov 19 at 4:35
add a comment |
what is the definition ofupdate_contacts
?
– Netwave
Nov 19 at 4:35
what is the definition of
update_contacts
?– Netwave
Nov 19 at 4:35
what is the definition of
update_contacts
?– Netwave
Nov 19 at 4:35
add a comment |
2 Answers
2
active
oldest
votes
up vote
1
down vote
accepted
Use functools.partial
; it's a standard way to do such things, and it's specifically recommended in the docs for loop.run_in_executor
, as well as more generally in the Event Loop docs.
Here's how it might look for you:
import functools # at the top with the other imports
loop.run_in_executor(None, functools.partial(update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
}))
You could also do from functools import partial
, if you like.
add a comment |
up vote
0
down vote
You asked for a "generic way"; the most generic answer is that you create a function for the purpose. If the data
you want to provide is local to the caller, you create that function inside the caller, perhaps as a lambda:
loop.run_in_executor(None,lambda: update_contacts(data={
'email': email,
'access_token': g.tokens['access_token']
})
As given, this is not much different from the functools.partial
answer, and (as the documentation says) it might reduce the utility of debug output, but it lets you do things like compute the data
values on the executor and act on the return value from update_contacts
.
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
Use functools.partial
; it's a standard way to do such things, and it's specifically recommended in the docs for loop.run_in_executor
, as well as more generally in the Event Loop docs.
Here's how it might look for you:
import functools # at the top with the other imports
loop.run_in_executor(None, functools.partial(update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
}))
You could also do from functools import partial
, if you like.
add a comment |
up vote
1
down vote
accepted
Use functools.partial
; it's a standard way to do such things, and it's specifically recommended in the docs for loop.run_in_executor
, as well as more generally in the Event Loop docs.
Here's how it might look for you:
import functools # at the top with the other imports
loop.run_in_executor(None, functools.partial(update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
}))
You could also do from functools import partial
, if you like.
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
Use functools.partial
; it's a standard way to do such things, and it's specifically recommended in the docs for loop.run_in_executor
, as well as more generally in the Event Loop docs.
Here's how it might look for you:
import functools # at the top with the other imports
loop.run_in_executor(None, functools.partial(update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
}))
You could also do from functools import partial
, if you like.
Use functools.partial
; it's a standard way to do such things, and it's specifically recommended in the docs for loop.run_in_executor
, as well as more generally in the Event Loop docs.
Here's how it might look for you:
import functools # at the top with the other imports
loop.run_in_executor(None, functools.partial(update_contacts, data={
'email': email,
'access_token': g.tokens['access_token']
}))
You could also do from functools import partial
, if you like.
answered Nov 19 at 6:16
Cyphase
8,03011627
8,03011627
add a comment |
add a comment |
up vote
0
down vote
You asked for a "generic way"; the most generic answer is that you create a function for the purpose. If the data
you want to provide is local to the caller, you create that function inside the caller, perhaps as a lambda:
loop.run_in_executor(None,lambda: update_contacts(data={
'email': email,
'access_token': g.tokens['access_token']
})
As given, this is not much different from the functools.partial
answer, and (as the documentation says) it might reduce the utility of debug output, but it lets you do things like compute the data
values on the executor and act on the return value from update_contacts
.
add a comment |
up vote
0
down vote
You asked for a "generic way"; the most generic answer is that you create a function for the purpose. If the data
you want to provide is local to the caller, you create that function inside the caller, perhaps as a lambda:
loop.run_in_executor(None,lambda: update_contacts(data={
'email': email,
'access_token': g.tokens['access_token']
})
As given, this is not much different from the functools.partial
answer, and (as the documentation says) it might reduce the utility of debug output, but it lets you do things like compute the data
values on the executor and act on the return value from update_contacts
.
add a comment |
up vote
0
down vote
up vote
0
down vote
You asked for a "generic way"; the most generic answer is that you create a function for the purpose. If the data
you want to provide is local to the caller, you create that function inside the caller, perhaps as a lambda:
loop.run_in_executor(None,lambda: update_contacts(data={
'email': email,
'access_token': g.tokens['access_token']
})
As given, this is not much different from the functools.partial
answer, and (as the documentation says) it might reduce the utility of debug output, but it lets you do things like compute the data
values on the executor and act on the return value from update_contacts
.
You asked for a "generic way"; the most generic answer is that you create a function for the purpose. If the data
you want to provide is local to the caller, you create that function inside the caller, perhaps as a lambda:
loop.run_in_executor(None,lambda: update_contacts(data={
'email': email,
'access_token': g.tokens['access_token']
})
As given, this is not much different from the functools.partial
answer, and (as the documentation says) it might reduce the utility of debug output, but it lets you do things like compute the data
values on the executor and act on the return value from update_contacts
.
answered Nov 19 at 6:54
Davis Herring
7,2891634
7,2891634
add a comment |
add a comment |
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what is the definition of
update_contacts
?– Netwave
Nov 19 at 4:35