This is archived documentation for InfluxData product versions that are no longer maintained. For newer documentation, see the latest InfluxData documentation.
Everyone has their own anomaly detection algorithm, so we have built Kapacitor to integrate easily with which ever algorithm fits your domain. Kapacitor calls these custom algorithms UDFs for User Defined Functions. This guide will walk through the necessary steps for writing and using your own UDFs within Kapacitor.
If you haven’t already, we recommend following the getting started guide for Kapacitor prior to continuing.
If you own or have recently purchased a 3D printer, you may know that 3D printing requires the environment to be at certain temperatures in order to ensure quality prints. Prints can also take a long time (some can take more than 24 hours), so you can’t just watch the temperature graphs the whole time to make sure the print is going well. Also, if a print goes bad early, you want to make sure and stop it so that you can restart it, and not waste materials on continuing a bad print.
Due to the physical limitations of 3D printing, the printer software is typically designed to keep the temperatures within certain tolerances. For the sake of argument, let’s say that you don’t trust the software to do it’s job (or want to create your own), and want to be alerted when the temperature reaches an abnormal level.
There are three temperatures when it comes to 3D printing:
- The temperature of the hot end (where the plastic is melted before being printed).
- The temperature of the bed (where the part is being printed).
- The temperature of the ambient air (the air around the printer).
All three of these temperatures affect the quality of the print (some being more important than others), but we want to make sure and track all of them.
To keep our anomaly detection algorithm simple, let’s compute a
p-value for each window of data we receive, and then emit a single
data point with that
p-value. To compute the
p-value, we will use
Welch’s t-test. For
a null hypothesis, we will state that a new window is from the same
population as the historical windows. If the
p-value drops low
enough, we can reject the null hypothesis and conclude that the window
must be from something different than the historical data population, or
an anomaly. This is an oversimplified approach, but we are learning
how to write UDFs, not statistics.
Writing a UDF
Now that we have an idea of what we want to do, let’s understand how
Kapacitor wants to communicate with our process. From the UDF
we learn that Kapacitor will spawn a process called an
agent is responsible for describing what options it has, and then
initializing itself with a set of options. As data is received by the
agent performs its computation and then returns the
resulting data to Kapacitor. All of this communication occurs over
STDIN and STDOUT using protocol buffers. As of this writing, Kapacitor
has agents implemented in Go and Python that take care of the
communication details and expose an interface for doing the actual
work. For this guide, we will be using the Python agent.
The Handler Interface
Here is the Python handler interface for the agent:
# The Agent calls the appropriate methods on the Handler as requests are read off STDIN. # # Throwing an exception will cause the Agent to stop and an ErrorResponse to be sent. # Some *Response objects (like SnapshotResponse) allow for returning their own error within the object itself. # These types of errors will not stop the Agent and Kapacitor will deal with them appropriately. # # The Handler is called from a single thread, meaning methods will not be called concurrently. # # To write Points/Batches back to the Agent/Kapacitor use the Agent.write_response method, which is thread safe. class Handler(object): def info(self): pass def init(self, init_req): pass def snapshot(self): pass def restore(self, restore_req): pass def begin_batch(self): pass def point(self): pass def end_batch(self, end_req): pass
The Info Method
Let’s start with the
info method. When Kapacitor starts up it will
info and expect in return some information about how this UDF
behaves. Specifically, Kapacitor expects the kind of edge the UDF
wants and provides.
Remember: within Kapacitor, data is transported in streams or batches, so the UDF must declare what it expects.
In addition, UDFs can accept certain options so that they are
individually configurable. The
info response can contain a list of
options, their names, and expected arguments.
For our example UDF, we need to know three things:
- The field to operate on.
- The size of the historical window to keep.
- The significance level or
Below we have the implementation of the
info method for our handler that defines the edge types and options available:
... def info(self): """ Respond with which type of edges we want/provide and any options we have. """ response = udf_pb2.Response() # We will consume batch edges aka windows of data. response.info.wants = udf_pb2.BATCH # We will produce single points of data aka stream. response.info.provides = udf_pb2.STREAM # Here we can define options for the UDF. # Define which field we should process. response.info.options['field'].valueTypes.append(udf_pb2.STRING) # Since we will be computing a moving average let's make the size configurable. # Define an option 'size' that takes one integer argument. response.info.options['size'].valueTypes.append(udf_pb2.INT) # We need to know the alpha level so that we can ignore bad windows. # Define an option 'alpha' that takes one double valued argument. response.info.options['alpha'].valueTypes.append(udf_pb2.DOUBLE) return response ...
When Kapacitor starts, it will spawn our UDF process and request
info data and then shutdown the process. Kapacitor will
remember this information for each UDF. This way, Kapacitor can
understand the available options for a given UDF before its executed
inside of a task.
The Init Method
Next let’s implement the
init method, which is called once the task
starts executing. The
init method receives a list of chosen
options, which are then used to configure the handler appropriately.
In response, we indicate whether the
init request was successful,
and, if not, any error messages if the options were invalid.
... def init(self, init_req): """ Given a list of options initialize this instance of the handler """ success = True msg = '' size = 0 for opt in init_req.options: if opt.name == 'field': self._field = opt.values.stringValue elif opt.name == 'size': size = opt.values.intValue elif opt.name == 'alpha': self._alpha = opt.values.doubleValue if size <= 1: success = False msg += ' must supply window size > 1' if self._field == '': success = False msg += ' must supply a field name' if self._alpha == 0: success = False msg += ' must supply an alpha value' # Initialize our historical window # We will define MovingStats in the next step self._history = MovingStats(size) response = udf_pb2.Response() response.init.success = success response.init.error = msg[1:] return response ...
When a task starts, Kapacitor spawns a new process for the UDF and
init, passing any specified options from the TICKscript. Once
initialized, the process will remain running and Kapacitor will begin
sending data as it arrives.
The Batch and Point Methods
Our task wants a
batch edge, meaning it expects to get data in
batches or windows. To send a batch of data to the UDF process,
Kapacitor first calls the
begin_batch method, which indicates that all
subsequent points belong to a batch. Once the batch is complete, the
end_batch method is called with some metadata about the batch.
At a high level, this is what our UDF code will do for each of the
begin_batch– mark the start of a new batch and initialize a structure for it
point– store the point
end_batch– perform the
t-testand then update the historical data
The Complete UDF Script
What follows is the complete UDF implementation with our
init, and batching methods (as well as everything else we need).
from kapacitor.udf.agent import Agent, Handler from scipy import stats import math import kapacitor.udf.udf_pb2 import sys class TTestHandler(Handler): """ Keep a rolling window of historically normal data When a new window arrives use a two-sided t-test to determine if the new window is statistically significantly different. """ def __init__(self, agent): self._agent = agent self._field = '' self._history = None self._batch = None self._alpha = 0.0 def info(self): """ Respond with which type of edges we want/provide and any options we have. """ response = udf_pb2.Response() # We will consume batch edges aka windows of data. response.info.wants = udf_pb2.BATCH # We will produce single points of data aka stream. response.info.provides = udf_pb2.STREAM # Here we can define options for the UDF. # Define which field we should process response.info.options['field'].valueTypes.append(udf_pb2.STRING) # Since we will be computing a moving average let's make the size configurable. # Define an option 'size' that takes one integer argument. response.info.options['size'].valueTypes.append(udf_pb2.INT) # We need to know the alpha level so that we can ignore bad windows # Define an option 'alpha' that takes one double argument. response.info.options['alpha'].valueTypes.append(udf_pb2.DOUBLE) return response def init(self, init_req): """ Given a list of options initialize this instance of the handler """ success = True msg = '' size = 0 for opt in init_req.options: if opt.name == 'field': self._field = opt.values.stringValue elif opt.name == 'size': size = opt.values.intValue elif opt.name == 'alpha': self._alpha = opt.values.doubleValue if size <= 1: success = False msg += ' must supply window size > 1' if self._field == '': success = False msg += ' must supply a field name' if self._alpha == 0: success = False msg += ' must supply an alpha value' # Initialize our historical window self._history = MovingStats(size) response = udf_pb2.Response() response.init.success = success response.init.error = msg[1:] return response def begin_batch(self, begin_req): # create new window for batch self._batch = MovingStats(-1) def point(self, point): self._batch.update(point.fieldsDouble[self._field]) def end_batch(self, batch_meta): pvalue = 1.0 if self._history.n != 0: # Perform Welch's t test t, pvalue = stats.ttest_ind_from_stats( self._history.mean, self._history.stddev(), self._history.n, self._batch.mean, self._batch.stddev(), self._batch.n, equal_var=False) # Send pvalue point back to Kapacitor response = udf_pb2.Response() response.point.time = batch_meta.tmax response.point.name = batch_meta.name response.point.group = batch_meta.group response.point.tags.update(batch_meta.tags) response.point.fieldsDouble["t"] = t response.point.fieldsDouble["pvalue"] = pvalue self._agent.write_response(response) # Update historical stats with batch, but only if it was normal. if pvalue > self._alpha: for value in self._batch._window: self._history.update(value) class MovingStats(object): """ Calculate the moving mean and variance of a window. Uses Welford's Algorithm. """ def __init__(self, size): """ Create new MovingStats object. Size can be -1, infinite size or > 1 meaning static size """ self.size = size if not (self.size == -1 or self.size > 1): raise Exception("size must be -1 or > 1") self._window =  self.n = 0.0 self.mean = 0.0 self._s = 0.0 def stddev(self): """ Return the standard deviation """ if self.n == 1: return 0.0 return math.sqrt(self._s / (self.n - 1)) def update(self, value): # 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 = TTestHandler(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"
That was a lot, but now we are ready to configure Kapacitor to run our code. Create a scratch dir for working through the rest of this guide:
mkdir /tmp/kapacitor_udf cd /tmp/kapacitor_udf
Save the above UDF python script into
Configuring Kapacitor for our UDF
Add this snippet to your Kapacitor configuration file (typically located at
[udf] [udf.functions] [udf.functions.tTest] # Run python prog = "/usr/bin/python2" # Pass args to python # -u for unbuffered STDIN and STDOUT # and the path to the script args = ["-u", "/tmp/kapacitor_udf/ttest.py"] # If the python process is unresponsive for 10s kill it timeout = "10s" # Define env vars for the process, in this case the PYTHONPATH [udf.functions.tTest.env] PYTHONPATH = "/tmp/kapacitor_udf/kapacitor/udf/agent/py"
In the configuration we called the function
tTest. That is also how
we will reference it in the TICKscript.
Notice that our Python script imported the
Agent object, and we set
PYTHONPATH in the configuration. Clone the Kapacitor source
into the tmp dir so we can point the
PYTHONPATH at the necessary
python code. This is typically overkill since it’s just two Python
files, but it makes it easy to follow:
git clone https://github.com/influxdata/kapacitor.git /tmp/kapacitor_udf/kapacitor
Running Kapacitor with the UDF
Restart the Kapacitor daemon to make sure everything is configured correctly:
service kapacitor restart
Check the logs (
/var/log/kapacitor/) to make sure you see a
Listening for signals line and that no errors occurred. If you
don’t see the line, it’s because the UDF process is hung and not
responding. It should be killed after a timeout, so give it a moment
to stop properly. Once stopped, you can fix any errors and try again.
If everything was started correctly, then it’s time to write our
TICKscript to use the
tTest UDF method:
// This TICKscript monitors the three temperatures for a 3d printing job, // and triggers alerts if the temperatures start to experience abnormal behavior. // Define our desired significance level. var alpha = 0.001 // Select the temperatures measurements var data = stream |from() .measurement('temperatures') |window() .period(5m) .every(5m) data //Run our tTest UDF on the hotend temperature @tTest() // specify the hotend field .field('hotend') // Keep a 1h rolling window .size(3600) // pass in the alpha value .alpha(alpha) |alert() .id('hotend') .crit(lambda: "pvalue" < alpha) .log('/tmp/kapacitor_udf/hotend_failure.log') // Do the same for the bed and air temperature. data @tTest() .field('bed') .size(3600) .alpha(alpha) |alert() .id('bed') .crit(lambda: "pvalue" < alpha) .log('/tmp/kapacitor_udf/bed_failure.log') data @tTest() .field('air') .size(3600) .alpha(alpha) |alert() .id('air') .crit(lambda: "pvalue" < alpha) .log('/tmp/kapacitor_udf/air_failure.log')
Notice that we have called
tTest three times. This means that
Kapacitor will spawn three different Python processes and pass the
respective init option to each one.
Save this script as
/tmp/kapacitor_udf/print_temps.tick and define
the Kapacitor task:
kapacitor define print_temps -type stream -dbrp printer.autogen -tick print_temps.tick
Generating Test Data
To simulate our printer for testing, we will write a simple Python script to generate temperatures. This script generates random temperatures that are normally distributed around a target temperature. At specified times, the variation and offset of the temperatures changes, creating an anomaly.
Don’t worry too much about the details here. It would be much better to use real data for testing our TICKscript and UDF, but this is faster (and much cheaper than a 3D printer).
#!/usr/bin/python2 from numpy import random from datetime import timedelta, datetime import sys import time import requests # Target temperatures in C hotend_t = 220 bed_t = 90 air_t = 70 # Connection info write_url = 'http://localhost:9092/write?db=printer&rp=autogen&precision=s' measurement = 'temperatures' def temp(target, sigma): """ Pick a random temperature from a normal distribution centered on target temperature. """ return random.normal(target, sigma) def main(): hotend_sigma = 0 bed_sigma = 0 air_sigma = 0 hotend_offset = 0 bed_offset = 0 air_offset = 0 # Define some anomalies by changing sigma at certain times # list of sigma values to start at a specified iteration hotend_anomalies =[ (0, 0.5, 0), # normal sigma (3600, 3.0, -1.5), # at one hour the hotend goes bad (3900, 0.5, 0), # 5 minutes later recovers ] bed_anomalies =[ (0, 1.0, 0), # normal sigma (28800, 5.0, 2.0), # at 8 hours the bed goes bad (29700, 1.0, 0), # 15 minutes later recovers ] air_anomalies = [ (0, 3.0, 0), # normal sigma (10800, 5.0, 0), # at 3 hours air starts to fluctuate more (43200, 15.0, -5.0), # at 12 hours air goes really bad (45000, 5.0, 0), # 30 minutes later recovers (72000, 3.0, 0), # at 20 hours goes back to normal ] # Start from 2016-01-01 00:00:00 UTC # This makes it easy to reason about the data later now = datetime(2016, 1, 1) second = timedelta(seconds=1) epoch = datetime(1970,1,1) # 24 hours of temperatures once per second points =  for i in range(60*60*24+2): # update sigma values if len(hotend_anomalies) > 0 and i == hotend_anomalies: hotend_sigma = hotend_anomalies hotend_offset = hotend_anomalies hotend_anomalies = hotend_anomalies[1:] if len(bed_anomalies) > 0 and i == bed_anomalies: bed_sigma = bed_anomalies bed_offset = bed_anomalies bed_anomalies = bed_anomalies[1:] if len(air_anomalies) > 0 and i == air_anomalies: air_sigma = air_anomalies air_offset = air_anomalies air_anomalies = air_anomalies[1:] # generate temps hotend = temp(hotend_t+hotend_offset, hotend_sigma) bed = temp(bed_t+bed_offset, bed_sigma) air = temp(air_t+air_offset, air_sigma) points.append("%s hotend=%f,bed=%f,air=%f %d" % ( measurement, hotend, bed, air, (now - epoch).total_seconds(), )) now += second # Write data to Kapacitor r = requests.post(write_url, data='\n'.join(points)) if r.status_code != 204: print >> sys.stderr, r.text return 1 return 0 if __name__ == '__main__': exit(main())
Save the above script as
This Python script has two Python dependencies:
numpy. They can easily be installed via
pipor your package manager.
At this point we have a task ready to go, and a script to generate some fake data with anomalies. Now we can create a recording of our fake data so that we can easily iterate on the task:
# Start the recording in the background kapacitor record stream -task print_temps -duration 24h -no-wait # Grab the ID from the output and store it in a var rid=7bd3ced5-5e95-4a67-a0e1-f00860b1af47 # Run our python script to generate data chmod +x ./printer_data.py ./printer_data.py
We can verify it worked by listing information about the recording.
Our recording came out to
1.6MB, so yours should come out somewhere
close to that:
$ kapacitor list recordings $rid ID Type Status Size Date 7bd3ced5-5e95-4a67-a0e1-f00860b1af47 stream finished 1.6 MB 04 May 16 11:44 MDT
Finally, let’s run the play against our task and see how it works:
kapacitor replay -task print_temps -recording $rid -rec-time
Check the various log files to see if the algorithm caught the anomalies:
Based on the
printer_data.py script above, there should be anomalies at:
- 1hr – hotend
- 8hr – bed
- 12hr – air
There may be some false positives as well, but, since we want this to work with real data (not our nice clean fake data), it doesn’t help much to tweak it at this point.
Well, there we have it. We can now get alerts when the temperatures for our prints deviates from the norm. Hopefully you now have a better understanding of how Kapacitor UDFs work, and have a good working example as a launching point into further work with UDFS.
The framework is in place, now go plug in a real anomaly detection algorithm that works for your domain!
Extending This Example
There are a few things that we have left as exercises to the reader:
Snapshot/Restore – Kapacitor will regularly snapshot the state of your UDF process so that it can be restored if the process is restarted. The examples here have implementations for the
restoremethods. Implement them for the
TTestHandlerhandler as an exercise.
Change the algorithm from a t-test to something more fitting for your domain. Both
scipyhave a wealth of algorithms.
The options returned by the
inforequest can contain multiple arguments. Modify the
fieldoption to accept three field names and change the
TTestHandlerto maintain historical data and batches for each field instead of just the one. That way only one ttest.py process needs to be running.