def test_constructor(self):
settings = {
'CACHE_WRITE_STRATEGY': 'max',
}
settings_patch = patch.dict('carbon.conf.settings', settings)
settings_patch.start()
cache = MetricCache()
self.assertNotEqual(cache, None)
self.assertTrue(isinstance(cache.strategy, MaxStrategy))
def stringReceived(self, rawRequest):
request = self.unpickler.loads(rawRequest)
cache = MetricCache()
if request['type'] == 'cache-query':
metric = request['metric']
datapoints = list(cache.get(metric, {}).items())
result = dict(datapoints=datapoints)
if settings.LOG_CACHE_HITS:
log.query('[%s] cache query for \"%s\" returned %d values' %
(self.peerAddr, metric, len(datapoints)))
instrumentation.increment('cacheQueries')
elif request['type'] == 'cache-query-bulk':
datapointsByMetric = {}
metrics = request['metrics']
for metric in metrics:
datapointsByMetric[metric] = list(
cache.get(metric, {}).items())
result = dict(datapointsByMetric=datapointsByMetric)
if settings.LOG_CACHE_HITS:
log.query(
'[%s] cache query bulk for \"%d\" metrics returned %d values'
% (self.peerAddr, len(metrics),
sum([
len(datapoints)
for datapoints in datapointsByMetric.values()
])))
instrumentation.increment('cacheBulkQueries')
instrumentation.append('cacheBulkQuerySize', len(metrics))
elif request['type'] == 'get-metadata':
result = management.getMetadata(request['metric'], request['key'])
elif request['type'] == 'set-metadata':
result = management.setMetadata(request['metric'], request['key'],
request['value'])
else:
result = dict(error="Invalid request type \"%s\"" %
request['type'])
response = pickle.dumps(result, protocol=2)
self.sendString(response)
def optimalWriteOrder():
"""Generates metrics with the most cached values first and applies a soft
rate limit on new metrics"""
cache = MetricCache()
while cache:
(metric, datapoints) = cache.drain_metric()
dbFileExists = state.database.exists(metric)
if not dbFileExists and CREATE_BUCKET:
# If our tokenbucket has enough tokens available to create a new metric
# file then yield the metric data to complete that operation. Otherwise
# we'll just drop the metric on the ground and move on to the next
# metric.
# XXX This behavior should probably be configurable to no tdrop metrics
# when rate limitng unless our cache is too big or some other legit
# reason.
if CREATE_BUCKET.drain(1):
yield (metric, datapoints, dbFileExists)
continue
yield (metric, datapoints, dbFileExists)
def writeCachedDataPoints():
"Write datapoints until the MetricCache is completely empty"
cache = MetricCache()
while cache:
(metric, datapoints) = cache.drain_metric()
if metric is None:
# end the loop
break
dbFileExists = state.database.exists(metric)
if not dbFileExists:
if CREATE_BUCKET and not CREATE_BUCKET.drain(1):
# If our tokenbucket doesn't have enough tokens available to create a new metric
# file then we'll just drop the metric on the ground and move on to the next
# metric.
# XXX This behavior should probably be configurable to no tdrop metrics
# when rate limitng unless our cache is too big or some other legit
# reason.
instrumentation.increment('droppedCreates')
continue
archiveConfig = None
xFilesFactor, aggregationMethod = None, None
for schema in SCHEMAS:
if schema.matches(metric):
if settings.LOG_CREATES:
log.creates('new metric %s matched schema %s' % (metric, schema.name))
archiveConfig = [archive.getTuple() for archive in schema.archives]
break
for schema in AGGREGATION_SCHEMAS:
if schema.matches(metric):
if settings.LOG_CREATES:
log.creates('new metric %s matched aggregation schema %s'
% (metric, schema.name))
xFilesFactor, aggregationMethod = schema.archives
break
if not archiveConfig:
raise Exception(("No storage schema matched the metric '%s',"
" check your storage-schemas.conf file.") % metric)
if settings.LOG_CREATES:
log.creates("creating database metric %s (archive=%s xff=%s agg=%s)" %
(metric, archiveConfig, xFilesFactor, aggregationMethod))
try:
state.database.create(metric, archiveConfig, xFilesFactor, aggregationMethod)
if settings.ENABLE_TAGS:
tagQueue.add(metric)
instrumentation.increment('creates')
except Exception as e:
log.err()
log.msg("Error creating %s: %s" % (metric, e))
instrumentation.increment('errors')
continue
# If we've got a rate limit configured lets makes sure we enforce it
waitTime = 0
if UPDATE_BUCKET:
t1 = time.time()
yield UPDATE_BUCKET.drain(1, blocking=True)
waitTime = time.time() - t1
try:
t1 = time.time()
# If we have duplicated points, always pick the last. update_many()
# has no guaranted behavior for that, and in fact the current implementation
# will keep the first point in the list.
datapoints = dict(datapoints).items()
state.database.write(metric, datapoints)
if settings.ENABLE_TAGS:
tagQueue.update(metric)
updateTime = time.time() - t1
except Exception as e:
log.err()
log.msg("Error writing to %s: %s" % (metric, e))
instrumentation.increment('errors')
else:
pointCount = len(datapoints)
instrumentation.increment('committedPoints', pointCount)
instrumentation.append('updateTimes', updateTime)
if settings.LOG_UPDATES:
if waitTime > 0.001:
log.updates("wrote %d datapoints for %s in %.5f seconds after waiting %.5f seconds" % (
pointCount, metric, updateTime, waitTime))
else:
log.updates("wrote %d datapoints for %s in %.5f seconds" % (
pointCount, metric, updateTime))
def writeCachedDataPoints():
"Write datapoints until the MetricCache is completely empty"
cache = MetricCache()
while cache:
dataWritten = False
for (metric, datapoints, dbFileExists) in optimalWriteOrder():
dataWritten = True
if not dbFileExists:
archiveConfig = None
xFilesFactor, aggregationMethod = None, None
for schema in SCHEMAS:
if schema.matches(metric):
if settings.LOG_CREATES:
log.creates('new metric %s matched schema %s' %
(metric, schema.name))
archiveConfig = [
archive.getTuple() for archive in schema.archives
]
break
for schema in AGGREGATION_SCHEMAS:
if schema.matches(metric):
if settings.LOG_CREATES:
log.creates(
'new metric %s matched aggregation schema %s' %
(metric, schema.name))
xFilesFactor, aggregationMethod = schema.archives
break
if not archiveConfig:
raise Exception(
"No storage schema matched the metric '%s', check your storage-schemas.conf file."
% metric)
if settings.LOG_CREATES:
log.creates(
"creating database metric %s (archive=%s xff=%s agg=%s)"
% (metric, archiveConfig, xFilesFactor,
aggregationMethod))
try:
state.database.create(metric, archiveConfig, xFilesFactor,
aggregationMethod)
instrumentation.increment('creates')
except Exception, e:
log.err()
log.msg("Error creating %s: %s" % (metric, e))
instrumentation.increment('errors')
continue
# If we've got a rate limit configured lets makes sure we enforce it
if UPDATE_BUCKET:
UPDATE_BUCKET.drain(1, blocking=True)
try:
t1 = time.time()
# If we have duplicated points, always pick the last. update_many()
# has no guaranted behavior for that, and in fact the current implementation
# will keep the first point in the list.
datapoints = dict(datapoints).items()
state.database.write(metric, datapoints)
updateTime = time.time() - t1
except Exception, e:
log.err()
log.msg("Error writing to %s: %s" % (metric, e))
instrumentation.increment('errors')
else:
pointCount = len(datapoints)
instrumentation.increment('committedPoints', pointCount)
instrumentation.append('updateTimes', updateTime)
if settings.LOG_UPDATES:
log.updates("wrote %d datapoints for %s in %.5f seconds" %
(pointCount, metric, updateTime))
