def test_cache_incremented_by_key(self, cache):
"""Ensure that the cache object is incremented by the key passed in."""
from furious.batcher import bump_batch
cache.incr.return_value = 2
val = bump_batch('group')
self.assertEqual(val, 2)
cache.incr.assert_called_once_with('agg-batch-group')
def test_cache_incremented_by_key(self, cache):
"""Ensure that the cache object is incremented by the key passed in."""
from furious.batcher import bump_batch
cache.incr.return_value = 2
val = bump_batch('group')
self.assertEqual(val, 2)
cache.incr.assert_called_once_with('agg-batch-group')
def process_messages(tag, retries=0):
"""Processes the messages pulled fromm a queue based off the tag passed in.
Will insert another processor if any work was processed or the retry count
is under the max retry count. Will update a aggregated stats object with
the data in the payload of the messages processed.
:param tag: :class: `str` Tag to query the queue on
:param retry: :class: `int` Number of retries the job has processed
"""
from furious.batcher import bump_batch
from furious.batcher import MESSAGE_DEFAULT_QUEUE
from furious.batcher import MessageIterator
from furious.batcher import MessageProcessor
from google.appengine.api import memcache
# since we don't have a flag for checking complete we'll re-insert a
# processor task with a retry count to catch any work that may still be
# filtering in. If we've hit our max retry count we just bail out and
# consider the job complete.
if retries > 5:
logging.info("Process messages hit max retry and is exiting")
return
# create a message iteragor for the tag in batches of 500
message_iterator = MessageIterator(tag, MESSAGE_DEFAULT_QUEUE, 500)
client = memcache.Client()
# get the stats object from cache
stats = client.gets(tag)
# json decode it if it exists otherwise get the default state.
stats = json.loads(stats) if stats else get_default_stats()
work_processed = False
# loop through the messages pulled from the queue.
for message in message_iterator:
work_processed = True
value = int(message.get("value", 0))
color = message.get("color").lower()
# update the total stats with the value pulled
set_stats(stats["totals"], value)
# update the specific color status via the value pulled
set_stats(stats["colors"][color], value)
# insert the stats back into cache
json_stats = json.dumps(stats)
# try and do an add first to see if it's new. We can't trush get due to
# a race condition.
if not client.add(tag, json_stats):
# if we couldn't add than lets do a compare and set to safely
# update the stats
if not client.cas(tag, json_stats):
raise Exception("Transaction Collision.")
# bump the process batch id
bump_batch(tag)
if work_processed:
# reset the retries as we've processed work
retries = 0
else:
# no work was processed so increment the retries
retries += 1
# insert another processor
processor = MessageProcessor(target=process_messages,
args=("colors", ),
kwargs={'retries': retries},
tag="colors")
processor.start()
def process_messages(tag, retries=0):
"""Processes the messages pulled fromm a queue based off the tag passed in.
Will insert another processor if any work was processed or the retry count
is under the max retry count. Will update a aggregated stats object with
the data in the payload of the messages processed.
:param tag: :class: `str` Tag to query the queue on
:param retry: :class: `int` Number of retries the job has processed
"""
from furious.batcher import bump_batch
from furious.batcher import MESSAGE_DEFAULT_QUEUE
from furious.batcher import MessageIterator
from furious.batcher import MessageProcessor
from google.appengine.api import memcache
# since we don't have a flag for checking complete we'll re-insert a
# processor task with a retry count to catch any work that may still be
# filtering in. If we've hit our max retry count we just bail out and
# consider the job complete.
if retries > 5:
logging.info("Process messages hit max retry and is exiting")
return
# create a message iteragor for the tag in batches of 500
message_iterator = MessageIterator(tag, MESSAGE_DEFAULT_QUEUE, 500)
client = memcache.Client()
# get the stats object from cache
stats = client.gets(tag)
# json decode it if it exists otherwise get the default state.
stats = json.loads(stats) if stats else get_default_stats()
work_processed = False
# loop through the messages pulled from the queue.
for message in message_iterator:
work_processed = True
value = int(message.get("value", 0))
color = message.get("color").lower()
# update the total stats with the value pulled
set_stats(stats["totals"], value)
# update the specific color status via the value pulled
set_stats(stats["colors"][color], value)
# insert the stats back into cache
json_stats = json.dumps(stats)
# try and do an add first to see if it's new. We can't trush get due to
# a race condition.
if not client.add(tag, json_stats):
# if we couldn't add than lets do a compare and set to safely
# update the stats
if not client.cas(tag, json_stats):
raise Exception("Transaction Collision.")
# bump the process batch id
bump_batch(tag)
if work_processed:
# reset the retries as we've processed work
retries = 0
else:
# no work was processed so increment the retries
retries += 1
# insert another processor
processor = MessageProcessor(
target=process_messages, args=("colors",),
kwargs={'retries': retries}, tag="colors")
processor.start()