def accept(self, conn, addr):
self._count += 1
a = Agent(conn, conn)
h = MirrorHandler(a)
a.handler = h
logger.info("Starting Agent for connection %d", self._count)
a.start()
a.wait()
logger.info("Agent finished connection %d", self._count)
def accept(self, conn, addr):
""" Create a new agent/handler for each new connection.
Count and log each new connection and termination.
"""
self._count += 1
a = Agent(conn, conn)
h = HumidityClassifierHandler(a)
a.handler = h
logger.info("Starting Agent for connection %d", self._count)
a.start()
a.wait()
logger.info("Agent finished connection %d", self._count)
def accept(self, conn, addr):
self._count += 1
a = Agent(conn, conn)
h = MirrorHandler(a)
a.handler = h
logger.info("Starting Agent for connection %d", self._count)
a.start()
a.wait()
logger.info("Agent finished connection %d",self._count)
value = point.fieldsDouble[self._field]
self._state.update(value, point)
def end_batch(self, end_req):
# Get outliers
outliers = self._state.outliers(self._scale)
# Send begin batch with count of outliers
self._begin_response.begin.size = len(outliers)
self._agent.write_response(self._begin_response)
response = udf_pb2.Response()
for outlier in outliers:
response.point.CopyFrom(outlier)
self._agent.write_response(response)
# Send an identical end batch back to Kapacitor
response.end.CopyFrom(end_req)
self._agent.write_response(response)
if __name__ == '__main__':
a = Agent()
h = OutliersHandler(a)
a.handler = h
logger.info("Starting Agent")
a.start()
a.wait()
logger.info("Agent finished")
response.point.CopyFrom(group_buffer._consolidated_point)
self._agent.write_response(response)
self._state.reset()
def point(self, point):
# Points come through in bursts, all from a particular scrape share the same time.
# So once the time changes, we can flush the current cache.
if self._state.time and self._state.time != point.time:
self.flush()
# If the point is from a relevant measurement, cache it; otherwise just pass it through.
# (This is particularly useful if such UDFs exist in a chain, to avoid a cache sequence that
# requires N time changes in order to fully propagate through N nodes.)
if point.tags['__name__'] == self._measurement:
self._state.update(point)
else:
response = udf_pb2.Response()
response.point.CopyFrom(point)
self._agent.write_response(response)
if __name__ == '__main__':
agent = Agent()
handler = KubeStateMetricsConsolidateTrueFalseUnknown(agent)
agent.handler = handler
logger.info("Starting agent")
agent.start()
agent.wait()
logger.info("Agent finished")
self._state.update(value, point)
def end_batch(self, end_req):
# Get outliers
outliers = self._state.outliers(self._scale)
# Send begin batch with count of outliers
self._begin_response.begin.size = len(outliers)
self._agent.write_response(self._begin_response)
response = udf_pb2.Response()
for outlier in outliers:
response.point.CopyFrom(outlier)
self._agent.write_response(response)
# Send an identical end batch back to Kapacitor
response.end.CopyFrom(end_req)
self._agent.write_response(response)
if __name__ == '__main__':
a = Agent()
h = OutliersHandler(a)
a.handler = h
logger.info("Starting Agent")
a.start()
a.wait()
logger.info("Agent finished")
# update stats for new value
self.n += 1.0
diff = (value - self.mean)
self.mean += diff / self.n
self._s += diff * (value - self.mean)
if self.n == self.size + 1:
# update stats for removing old value
old = self._window.pop(0)
oldM = (self.n * self.mean - old)/(self.n - 1)
self._s -= (old - self.mean) * (old - oldM)
self.mean = oldM
self.n -= 1
self._window.append(value)
if __name__ == '__main__':
# Create an agent
agent = Agent()
# Create a handler and pass it an agent so it can write points
h = cycleTimeCIHandler(agent)
# Set the handler on the agent
agent.handler = h
# Anything printed to STDERR from a UDF process gets captured into the Kapacitor logs.
print >> sys.stderr, "Starting agent for TTestHandler"
agent.start()
agent.wait()
print >> sys.stderr, "Agent finished"
