Hello fellow fabric users.
During parallel runs, I would like to be able to collect information in
some sort of shared memory space to review after I'm done.
This seems to be tricky when running in parallel. (assumably due to the
threading)
Example use cases:
tracking success vs failed counts after a run
stuffing output in a tidy coalesced log after the job runs
I tried stuffing the output in to env, but that's apparently not a shared
global space....
Any guidance on how this might be done?
(calling out to a DB/sqllite or even a lockfile wrapped pickle seem like
passable solutions, but I'm hoping there's an easier way to collect data
during a parallel run and summarize it later...)
Help?
Is there a cleanup method on exit where I should be looking at env?
Thanks,
Joel Krauska
code snippet:
from fabric.api import *
from fabric.colors import red, green
env.mydata={}
env.failcount=0
env.goodcount=0
@task
def mytask():
with settings(
hide('warnings', 'running', 'stdout', 'stderr','output'),
):
result=run("uname")
print '-'*80
print env.host_string,
if result.failed:
print red('FAILED')
env.failcount+=1
else:
print green('OK')
env.goodcount+=1
print result
env.mydata[env.host_string]=str(result)
print env.mydata
# These aren't holding anything useful...
print env.mydata
print env.failcount
print env.goodcount
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