def _parallel_process_vessels_from_list(vessel_list, process_func, lockserver_handle, *args):
"""
Obtain locks on all of the nodes of vessels in vessel_list, get fresh vessel
objects from the databae, and then parallelize a call to process_func to
process each vessel in vessel_list (passing the additional *args to
process_func).
"""
node_id_list = []
for vessel in vessel_list:
node_id = maindb.get_node_identifier_from_vessel(vessel)
# Lock names must be unique, and there could be multiple vessels from the
# same node in the vessel_list.
if node_id not in node_id_list:
node_id_list.append(node_id)
# Lock the nodes that these vessels are on.
lockserver.lock_multiple_nodes(lockserver_handle, node_id_list)
try:
# Get new vessel objects from the db now that we have node locks.
new_vessel_list = []
for vessel in vessel_list:
node_id = maindb.get_node_identifier_from_vessel(vessel)
new_vessel_list.append(maindb.get_vessel(node_id, vessel.name))
# Have the list object the caller may still be using contain the actual
# vessel objects we have processed. That is, we've just replaced the
# caller's list's contents with new vessel objects for the same vessels.
vessel_list[:] = new_vessel_list[:]
return parallel.run_parallelized(vessel_list, process_func, *args)
finally:
# Unlock the nodes.
lockserver.unlock_multiple_nodes(lockserver_handle, node_id_list)
def sync_user_keys_of_vessels():
"""
This function is started as separate thread. It continually checks whether
there are vessels needing their user keys sync'd and initiates the user key
sync as needed.
"""
log.info("[sync_user_keys_of_vessels] thread started.")
# Run forever.
while True:
try:
# Sleep a few seconds for those times where we don't have any vessels to clean up.
time.sleep(5)
# We shouldn't be running the backend in production with
# settings.DEBUG = True. Just in case, though, tell django to reset its
# list of saved queries each time through the loop.
if settings.DEBUG:
django.db.reset_queries()
# Get a list of vessels that need to have user keys sync'd. This doesn't
# include nodes known to be inactive as we would just continue failing to
# communicate with nodes that are down.
vessellist = maindb.get_vessels_needing_user_key_sync()
if len(vessellist) == 0:
continue
log.info("[sync_user_keys_of_vessels] " + str(len(vessellist)) +
" vessels to have user keys sync'd: " + str(vessellist))
parallel_results = parallel.run_parallelized(
vessellist, _sync_user_keys_of_single_vessel)
if len(parallel_results["exception"]) > 0:
for vessel, exception_message in parallel_results["exception"]:
log_message = "Unhandled exception during parallelized vessel user key sync: " + exception_message
log.critical(log_message)
# Raise the last exceptions so that the admin gets an email.
raise InternalError(log_message)
except:
message = "[sync_user_keys_of_vessels] Something very bad happened: " + traceback.format_exc(
)
log.critical(message)
# Send an email to the addresses listed in settings.ADMINS
if not settings.DEBUG:
subject = "Critical SeattleGeni backend error"
django.core.mail.mail_admins(subject, message)
# Sleep for ten minutes to make sure we don't flood the admins with error
# report emails.
time.sleep(600)
def sync_user_keys_of_vessels():
"""
This function is started as separate thread. It continually checks whether
there are vessels needing their user keys sync'd and initiates the user key
sync as needed.
"""
log.info("[sync_user_keys_of_vessels] thread started.")
# Run forever.
while True:
try:
# Sleep a few seconds for those times where we don't have any vessels to clean up.
time.sleep(5)
# We shouldn't be running the backend in production with
# settings.DEBUG = True. Just in case, though, tell django to reset its
# list of saved queries each time through the loop.
if settings.DEBUG:
django.db.reset_queries()
# Get a list of vessels that need to have user keys sync'd. This doesn't
# include nodes known to be inactive as we would just continue failing to
# communicate with nodes that are down.
vessellist = maindb.get_vessels_needing_user_key_sync()
if len(vessellist) == 0:
continue
log.info("[sync_user_keys_of_vessels] " + str(len(vessellist)) +
" vessels to have user keys sync'd: " + str(vessellist))
parallel_results = parallel.run_parallelized(vessellist, _sync_user_keys_of_single_vessel)
if len(parallel_results["exception"]) > 0:
for vessel, exception_message in parallel_results["exception"]:
log_message = "Unhandled exception during parallelized vessel user key sync: " + exception_message
log.critical(log_message)
# Raise the last exceptions so that the admin gets an email.
raise InternalError(log_message)
except:
message = "[sync_user_keys_of_vessels] Something very bad happened: " + traceback.format_exc()
log.critical(message)
# Send an email to the addresses listed in settings.ADMINS
if not settings.DEBUG:
subject = "Critical SeattleGeni backend error"
django.core.mail.mail_admins(subject, message)
# Sleep for ten minutes to make sure we don't flood the admins with error
# report emails.
time.sleep(600)
def cleanup_vessels():
"""
This function is started as separate thread. It continually checks whether
there are vessels needing to be cleaned up and initiates cleanup as needed.
"""
log.info("[cleanup_vessels] cleanup thread started.")
# Start a transaction management.
django.db.transaction.enter_transaction_management()
# Run forever.
while True:
try:
# Sleep a few seconds for those times where we don't have any vessels to clean up.
time.sleep(5)
# We shouldn't be running the backend in production with
# settings.DEBUG = True. Just in case, though, tell django to reset its
# list of saved queries each time through the loop. Note that this is not
# specific to the cleanup thread as other parts of the backend are using
# the maindb, as well, so we're overloading the purpose of the cleanup
# thread by doing this here. This is just a convenient place to do it.
# See http://docs.djangoproject.com/en/dev/faq/models/#why-is-django-leaking-memory
# for more info.
if settings.DEBUG:
django.db.reset_queries()
# First, make it so that expired vessels are seen as dirty. We aren't
# holding a lock on the nodes when we do this. It's possible that we do
# this while someone else has a lock on the node. What would result?
# I believe the worst result is that a user has their vessel marked as
# dirty after they renewed in the case where they are renewing it just
# as it expires (with some exceptionally bad timing involved). And,
# that's not really very bad as if the user is trying to renew at the
# exact moment it expires, their trying their luck with how fast their
# request gets processed, anyways. In short, I don't think it's important
# enough to either obtain locks to do this or to rewrite the code to
# avoid any need for separately marking expired vessels as dirty rather
# than just trying to process expired vessels directly in the code below.
date_started=datetime.datetime.now()
expired_list = maindb.mark_expired_vessels_as_dirty()
if len(expired_list) > 0:
log.info("[cleanup_vessels] " + str(len(expired_list)) +
" expired vessels have been marked as dirty: " + str(expired_list))
maindb.create_action_log_event("mark_expired_vessels_as_dirty", user=None, second_arg=None,
third_arg=None, was_successful=True, message=None,
date_started=date_started, vessel_list=expired_list)
# Get a list of vessels to clean up. This doesn't include nodes known to
# be inactive as we would just continue failing to communicate with nodes
# that are down.
cleanupvessellist = maindb.get_vessels_needing_cleanup()
if len(cleanupvessellist) == 0:
continue
log.info("[cleanup_vessels] " + str(len(cleanupvessellist)) + " vessels to clean up: " + str(cleanupvessellist))
parallel_results = parallel.run_parallelized(cleanupvessellist, _cleanup_single_vessel)
if len(parallel_results["exception"]) > 0:
for vessel, exception_message in parallel_results["exception"]:
log_message = "Unhandled exception during parallelized vessel cleanup: " + exception_message
log.critical(log_message)
# Raise the last exceptions so that the admin gets an email.
raise InternalError(log_message)
except:
message = "[cleanup_vessels] Something very bad happened: " + traceback.format_exc()
log.critical(message)
# Send an email to the addresses listed in settings.ADMINS
if not settings.DEBUG:
subject = "Critical SeattleGeni backend error"
django.core.mail.mail_admins(subject, message)
# Sleep for ten minutes to make sure we don't flood the admins with error
# report emails.
time.sleep(600)
finally:
# Manually commit the transaction to prevent caching.
django.db.transaction.commit()
def cleanup_vessels():
"""
This function is started as separate thread. It continually checks whether
there are vessels needing to be cleaned up and initiates cleanup as needed.
"""
log.info("[cleanup_vessels] cleanup thread started.")
# Start a transaction management.
django.db.transaction.enter_transaction_management()
# Run forever.
while True:
try:
# Sleep a few seconds for those times where we don't have any vessels to clean up.
time.sleep(5)
# We shouldn't be running the backend in production with
# settings.DEBUG = True. Just in case, though, tell django to reset its
# list of saved queries each time through the loop. Note that this is not
# specific to the cleanup thread as other parts of the backend are using
# the maindb, as well, so we're overloading the purpose of the cleanup
# thread by doing this here. This is just a convenient place to do it.
# See http://docs.djangoproject.com/en/dev/faq/models/#why-is-django-leaking-memory
# for more info.
if settings.DEBUG:
django.db.reset_queries()
# First, make it so that expired vessels are seen as dirty. We aren't
# holding a lock on the nodes when we do this. It's possible that we do
# this while someone else has a lock on the node. What would result?
# I believe the worst result is that a user has their vessel marked as
# dirty after they renewed in the case where they are renewing it just
# as it expires (with some exceptionally bad timing involved). And,
# that's not really very bad as if the user is trying to renew at the
# exact moment it expires, their trying their luck with how fast their
# request gets processed, anyways. In short, I don't think it's important
# enough to either obtain locks to do this or to rewrite the code to
# avoid any need for separately marking expired vessels as dirty rather
# than just trying to process expired vessels directly in the code below.
date_started = datetime.datetime.now()
expired_list = maindb.mark_expired_vessels_as_dirty()
if len(expired_list) > 0:
log.info("[cleanup_vessels] " + str(len(expired_list)) +
" expired vessels have been marked as dirty: " +
str(expired_list))
maindb.create_action_log_event("mark_expired_vessels_as_dirty",
user=None,
second_arg=None,
third_arg=None,
was_successful=True,
message=None,
date_started=date_started,
vessel_list=expired_list)
# Get a list of vessels to clean up. This doesn't include nodes known to
# be inactive as we would just continue failing to communicate with nodes
# that are down.
cleanupvessellist = maindb.get_vessels_needing_cleanup()
if len(cleanupvessellist) == 0:
continue
log.info("[cleanup_vessels] " + str(len(cleanupvessellist)) +
" vessels to clean up: " + str(cleanupvessellist))
parallel_results = parallel.run_parallelized(
cleanupvessellist, _cleanup_single_vessel)
if len(parallel_results["exception"]) > 0:
for vessel, exception_message in parallel_results["exception"]:
log_message = "Unhandled exception during parallelized vessel cleanup: " + exception_message
log.critical(log_message)
# Raise the last exceptions so that the admin gets an email.
raise InternalError(log_message)
except:
message = "[cleanup_vessels] Something very bad happened: " + traceback.format_exc(
)
log.critical(message)
# Send an email to the addresses listed in settings.ADMINS
if not settings.DEBUG:
subject = "Critical SeattleGeni backend error"
django.core.mail.mail_admins(subject, message)
# Sleep for ten minutes to make sure we don't flood the admins with error
# report emails.
time.sleep(600)
finally:
# Manually commit the transaction to prevent caching.
django.db.transaction.commit()