def push_queue_worker_async():
""" push_queue_worker_async() VS push_queue_worker_async2()
(1) if a function uses "yield" it should ndb.tasklet, ndb.synctasklet, or ndb.toplevel.
(2) a function wrapped in ndb.tasklet returns a Future (and you can either yield it or call get_result() explicitly).
(3) ndb.synctasklet is like wrapping it in ndb.tasklet but implicitly calling get_result().
(4) ndb.toplevel is just like ndb.synctasklet but also waits for all pending operations to complete.
ndb.tasklet function returns a future object which can be "yield" or get_result()
As shown in push_queue_worker_async2(), if ndb.toplevel is not used, just explicitly call get_result() to get the
result from a future object. If the result is not available, get_result() will block the thread until the result is
ready.
"""
print "push_queue_worker_async() @ the backend module is called! " + request.values['email']
ctx = ndb.get_context()
ctx.set_memcache_policy(False) # disable memcache
ctx.set_cache_policy(lambda key: key.kind() == 'UserAccount') # enable in-context cache only for UserAccount
email = request.values['email']
user_account = yield data_store.query_user_async(email)
if not user_account:
user_account = UserAccount(email=email, login_count=1)
else:
user_account.login_count += 1
user_account_key = user_account.put()
raise ndb.Return("ok")
def push_queue_worker_sync():
print "push_queue_worker_sync() @ the backend module is called! " + request.values['email']
email = request.values['email']
user_account = data_store.query_user(email)
if not user_account:
user_account = UserAccount(email=email, login_count=1)
else:
user_account.login_count += 1
user_account_key = user_account.put()
return "ok"