Collecting data with Trackers and Webhooks

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Tracker instances must be initialized with an emitter. This section will go into more depth about the Emitter class and its subclasses.

The basic Emitter class

At its most basic, the Emitter class only needs a collector URI:

from snowplow_tracker import Emitter e = Emitter("")
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This is the signature of the constructor for the base Emitter class:

def __init__(self, endpoint, protocol="http", port=None, method="get", buffer_size=None, on_success=None, on_failure=None):
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endpointThe collector URIYesDict
protocolRequest protocol: HTTP or HTTPSNoList
portThe port to connect toNoPositive integer
methodThe method to use: “get” or “post”NoString
buffer_sizeNumber of events to store before flushingNoPositive integer
on_successCallback executed when a flush is successfulNoFunction taking 1 argument
on_failureCallback executed when a flush is unsuccessfulNoFunction taking 2 arguments
byte_limitNumber of bytes to store before flushingNoPositive integer

protocol defaults to “http” but can also be “https”.

When the emitter receives an event, it adds it to a buffer. When the queue is full, all events in the queue get sent to the collector. The buffer_size argument allows you to customize the queue size. By default, it is 1 for GET requests and 10 for POST requests. (So in the case of GET requests, each event is fired as soon as the emitter receives it.) If the emitter is configured to send POST requests, then instead of sending one for every event in the buffer, it will send a single request containing all those events in JSON format.

on_success is an optional callback that will execute whenever the queue is flushed successfully, that is, whenever every request sent has status code 200. It will be passed one argument: the number of events that were sent.

on_failure is similar, but executes when the flush is not wholly successful. It will be passed two arguments: the number of events that were successfully sent, and an array of unsent requests. (If the emitter is configured to send POST requests, the array will actually be a string, but it can be turned back into an array of Python dictionaries (each corresponding to an event) by using json.loads.)

byte_limit is similar to buffer_size, but instead of counting events – it takes into account only the amount of bytes to be sent over the network. Warning: this limit is approximate with infelicity < 1%.

An example:

def f(x): print(str(x) + " events sent successfully!") unsent_events = [] def g(x, y): print(str(x) + " events sent successfully!") print("These events were not sent successfully and have been stored in unsent_events:") for event_dict in y: print(event_dict) unsent_events.append(event_dict) e = Emitter("", buffer_size=3, on_success=f, on_failure=g) t = Tracker(e) # This doesn't cause the emitter to send a request because the buffer_size was set to 3, not 1 t.track_page_view("") t.track_page_view("") # This does cause the emitter to try to send all 3 events t.track_page_view("") # Since the method is GET by default, 3 separate requests are sent # If any of them are unsuccessful, they will be stored in the unsent_events variable
Code language: PHP (php)

The AsyncEmitter class

from snowplow_tracker import AsyncEmitter e = Emitter("", thread_count=10)
Code language: JavaScript (javascript)

The AsyncEmitter class works just like the Emitter class. It has one advantage, though: HTTP(S) requests are sent asynchronously, so the Tracker won’t be blocked while the Emitter waits for a response. For this reason, the AsyncEmitter is recommended over the base Emitter class.

The AsyncEmitter uses a fixed-size thread pool to perform network I/O. By default, this pool contains only one thread, but you can configure the number of threads in the constructor using the thread_count argument.

The CeleryEmitter class

The CeleryEmitter class works just like the base Emitter class, but it registers sending requests as a task for a Celery worker. If there is a module named on your PYTHONPATH, it will be used as the Celery configuration file; otherwise, a default configuration will be used. You can run the worker using this command:

celery -A snowplow_tracker.emitters worker --loglevel=debug

Note that on_success and on_failure callbacks cannot be supplied to this emitter.

The RedisEmitter class

Use a RedisEmitter instance to store events in a Redis database for later use. This is the RedisEmitter constructor function:

def __init__(self, rdb=None, key="snowplow"):
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rdb should be an instance of either the Redis or StrictRedis class, found in the redis module. If it is not supplied, a default will be used. key is the key used to store events in the database. It defaults to “snowplow”. The format for event storage is a Redis list of JSON strings.

An example:

from snowplow_tracker import RedisEmitter, Tracker import redis rdb = redis.StrictRedis(db=2) e = RedisEmitter(rdb, "my_snowplow_key") t = Tracker(e) t.track_page_view("") # Check that the event was stored in Redis: print(rdb.lrange("my_snowplowkey", 0, -1)) # prints something like: # ['{"tv":"py-0.4.0", "ev": "pv", "url": "", "dtm": 1400252420261, "tid": 7515828, "p": "pc"}']
Code language: PHP (php)

Manual flushing

You can flush the emitter manually using the flush method of the Tracker instance which is sending events to the emitter. This is a blocking call which synchronously sends all events in the emitter’s buffer.

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You can alternatively perform an asynchronous flush, which tells the tracker to send all buffered events but doesn’t wait for the sending to complete:

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If you are using the AsyncEmitter, you shouldn’t perform a synchronous flush inside an on_success or on_failure callback function as this can cause a deadlock.

Multiple emitters

You can configure a tracker instance to send events to multiple emitters by passing the Tracker constructor function an array of emitters instead of a single emitter, or by using the addEmitter method:

from snowplow_tracker import Subject, Tracker, AsyncEmitter, RedisEmitter import redis e1 = AsyncEmitter("", method="get") e1 = AsyncEmitter("", method="post") t = Tracker([e1, e2]) rdb = redis.StrictRedis(db=2) e3 = RedisEmitter(rdb, "my_snowplow_key") t.addEmitter(e3)
Code language: JavaScript (javascript)

Custom emitters

You can create your own custom emitter class, either from scratch or by subclassing one of the existing classes (with the exception of CeleryEmitter, since it uses the pickle module which doesn’t work correctly with a class subclassed from a class located in a different module). The only requirement for compatibility is that is must have an input method which accepts a Python dictionary of name-value pairs.

Automatically retry sending failed events

You can use the following function as the on_failure callback to immediately retry failed events:

def on_failure_retry(failed_event_count, failed_events): # possible backoff-and-retry timeout here for e in failed_events: my_emitter.input(e)
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You may wish to add backoff logic to delay the resending.

Setting flush timer

You can flush your emitter based on some time interval:

e1 = AsyncEmitter("", method="post") e1.set_flush_timer(5) # flush each 5 seconds
Code language: PHP (php)

Automatic flush can also be cancelled:

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