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Collector Base Classes

This section takes a quick look at each of the possible base classes of your collector to choose from. Each subsection will familiarize you with a new class, starting from an abstract interface and then discussing more specialized ones.

Each description has a step by step implementation guide which shows how to implement your own collector using one of those classes as its base.

Definitions for every class described here can be found in the N6Core library in the n6.collectors.generic module (along with some documentation in docstrings).


AbstractBaseCollector is a general interface for the collectors’ implementation in the n6.collectors.generic module. However, it should only be used in special cases such as implementing a new base class for collectors to derive from.

When deriving from this class it is important to remember that it does not handle, by itself, the communication with the event queue.

The interface consists of four methods (two of which are abstract and have to be overridden):

  • get_script_init_kwargs - a class method returning a dictionary which will be then passed in as the keyword arguments the collector constructor (when collector initialization has been started from the generated console command; see section Collector command). The default implementation returns an empty dictionary.
  • run_handling - starts a collector and takes care of exiting with Ctrl+C. Default implementation calls run method end expects that it either returns gracefully, or raises KeyboardInterrupt exception (caused by Ctrl+C) upon which it calls the stop method.
  • run - abstract method which should contain the main collector’s activity.
  • stop - abstract method which should clean-up after interruption of the collector’s activity (the case of KeyboardInterrupt propagated from run).

Implementation based on AbstractBaseCollector

Unless there is some really special case you probably should not implement your collector as based on this interface alone. You would need to deal with the queue communication by yourself as well as be wary about n6‘s queue naming conventions, exchange types and so on…

Next class described in this chapter will address these issues so you should not have to bother with them.

But if you really have to use AbstractBaseCollector as your direct base then just implement the run and stop methods.


BaseCollector is the implementation of the AbstractBaseCollector, with configuration and the queue communication already handled. It achieves this by deriving from the QueueBase (see appendix for the QueueBase class) and CollectorConfigMixin classes.

CollectorConfigMixin is a subclass of ConfigMixin. The indirection is needed to provide backwards compatibility with some of the older collectors which used older configuration format; besides that the CollectorConfigMixin adds two helper methods: set_configuration (called on initialization of most collectors) and get_config_spec_format_kwargs (called from set_configuration; overridden by some collectors).

Implementation based on the BaseCollector

Here is how you would implement your own collector when using BaseCollector as your parent class.

Defining the collector’s type

Start by creating empty implementation like so:

class MyCollector(BaseCollector):

    type = 'stream'  # Could be one of 'stream', 'file', 'blacklist'.

The type class attribute tells n6 what kind of data our collector will publish to the RabbitMQ raw queue.

  • The stream type represents relatively small data objects in the json (or bson) format, each ready to be saved in MongoDB as a document.

  • The file type represents (possibly large) data in any format. Data bigger than 16MB must use the file type.

  • blacklist type is special. It tells n6 that data are gathered from a blacklist source, so it should be stored in a form of diff-like deltas.

What is important about blacklist data is the fact that they have different processing pipeline than normal data. Normal data go like so (aggregator is in parenthesis as it only takes part in the high-frequency data processing, more on that later):

source -> collector -> parser -> (aggregator ->) enricher -> filter -> recorder 

…while the blacklist-data-specific pipeline looks like so:

source -> collector -> parser -> enricher -> comparator -> filter -> recorder

As it is expected from blacklist data to gradually change over time, collectors gathering them can just download and pass them on to the pipeline.

On the other hand, for collectors of the other types (non-blacklist ones) it is often not so trivial because, typically, sources expose data that are continually increasing in records over time - so a collector must take care not to download the same records again and again; to cope with that such collectors need to have some internal state that persists throughout collector’s runs. Because of that it is often a bit harder to implement a stream or a file collector than a blacklist one.

Customizing output queue

There is the class attribute output_queue which allows us to customize the collectors output in relation to RabbitMQ queues. We will not cover it in details here but if, for example, you wanted to have two output queues (instead of the default one) you could define that like so:

output_queue = [
        'exchange': 'raw',
        'exchange_type': 'topic',
        'exchange': 'sample',
        'exchange_type': 'topic',

If left unchanged output_queue defaults to:

output_queue = {
    'exchange': 'raw',
    'exchange_type': 'topic',

Defining configuration file structure

As BaseCollector derives from the CollectorConfigMixin class we can use its capabilities to define config file structure using a special syntax. To do so we need to assign our configuration specification as the string to the config_spec class attribute. It is easier to show it rather than to explain it.

So our class would look like so:

class MyCollector(BaseCollector):

    type = 'stream'  # Could be one of 'stream', 'file', 'blacklist'.

    config_spec = '''
        source :: str
        url :: str
        download_retries = 1 :: int

In the code above we first line of the config spec specifies in the square brackets what is the name of our configuration section inside of a configuration file. Then we can define attributes followed by converter tags specifying value types (see the n6lib.config.Config module for the details). We can also specify default values as we did with the download_retries attribute.

We can then retrieve our attributes from the config dictionary when needed:

class MyCollector(BaseCollector):

    type = 'stream'  # Could be one of 'stream', 'file', 'blacklist'.

    config_spec = '''
        source :: str
        url :: str
        download_retries = 1 :: int

    def __init__(self, **kwargs):
        # we don't really need to save it as an instance 
        # attribute but here we do so just as an example
        self._url = self.config['url']

Some of the configuration fields are used by the parent classes, in this case the source field is a field required by the BaseCollector class. (Note: this field should be the first part of the data source identifier.)

As you can see it is important to know what kind of configuration attributes your base class might need to have defined.

There is an older way of defining configuration structure using config_group and config_required class fields but those are deprecated. Still, you could find them in implementations of some existing collectors.

Collector’s logic

What the BaseCollector class does for us is dividing collectors logic into 6 different methods. The method get_output_components glues them back together (yes, it is a template method - if you are familiar with programming design patterns). Its return value contains all you need to export the collected data as a RabbitMQ message; it is a 3-tuple consisting of: the AMQP routing key, the output data body and a dict of AMQP message properties (typically, these three items are ready to become respective arguments of the publish_output method).

The methods we (may) want to concern ourselves with are as following:

  • process_input_data
  • get_source_channel
  • get_source
  • get_output_rk
  • get_output_data_body
  • get_output_prop_kwargs

We will cover all of them.

process_input_data may take some input data (dict of arbitrary keyword arguments – whatever has been passed into the call of get_output_components; often there is no one) and can enrich or replace them if needed. The resultant dict is then passed as keyword arguments to each of the later functions’ calls. The default implementation just returns the given keyword arguments intact.

get_source_channel returns the second part of the source identifier. Here we need to explain it precisely: each source identifier follows the following format: {provider name}.{channel name} (each of the two parts must consist of ASCII letters, digits and hyphens only; and whole identifier must not be longer than 32 characters). So, for example, the identifier of a source whose owner’s name is LegitBL and that provides information about TOR exit nodes could be legit-bl.tor. This method should return the source’s channel part (in the case of this example it would be 'tor' ). This method needs to be overridden in subclasses as the default implementation raises NotImplementedError.

get_source returns whole source identifier as specified above. For most collectors you can just leave the default implementation which uses the source option from the collector’s configuration; so you should specify that option in congig_spec.

get_output_rk returns collector’s routing key and, as with the previous method, the default implementation should suffice.

get_output_data_body is arguably the most important method. Returns collector’s output message data body. In many cases this is the place where most of the collector’s logic is placed. This method needs to be overridden in subclasses as the default implementation raises NotImplementedError.

get_output_prop_kwargs returns a dictionary of custom keyword arguments for the pika.BasicProperties. If the dictionary needs some refinements a subclasses should expand this method using super() - as the dictionary produced by the default implementation rather should not be discarded/replaced completely.

So for the needs of really simple collector we would need to override just the two of the above methods: get_source_channel and get_output_data_body.

What we also need is to implement the start_publishing method (a QueuedBase hook) in which we need to call at lest the following three methods:

  • get_output_components - described above,
  • publish_output (defined in the QueuedBase class) - publishes our data to the output queue(s),
  • inner_stop (defined in the QueuedBase class) - signals to the RabbitMQ connection machinery that it is time to leave the connection’s event loop (so that the collector will exit gracefully).

See the following example:

from n6.collectors.generic import BaseCollector

class MyCollector(BaseCollector):

    type = 'stream'  # Could be one of 'stream', 'file', 'blacklist'.

    config_spec = '''
        source :: str
        url :: str
        download_retries = 1 :: int

    def __init__(self, **kwargs):
        # we don't really need to save it as an instance 
        # attribute but here we do so just as an example
        self._url = self.config['url']

    def start_publishing(self):
        for download_option in ['foo', 'bar']:
             output_prop_kwargs) = self.get_output_components(download_option=download_option)
            self.publish_output(output_rk, output_data_body, output_prop_kwargs)

    def get_source_channel(self, **processed_data):
        return 'tor'  # just exemplary source channel we could use

    def get_output_data_body(self, download_option, **kwargs): 
        data = self._download_data(self._url, download_option, **kwargs)
        assert isinstance(data, bytes) 
        return data

    def _download_data(self, url, download_option, **kwargs):
        # Here we implement data collection


The implementation shown above is really simplified for the purpose of the example. In particular, as said before, stream and file collectors usually have to keep track of the data they already have collected maintaining some persistent state to prevent collecting them again.

Implementing collectors with persistent state will be discussed in the Collector's state chapter of this guide.

A roadmap for extension of this guide…

In the n6.collectors.generic module a few noteworthy, more specialized, collector base classes are also defined:

  • BaseOneShotCollector,
  • BaseEmailSourceCollector,
  • BaseTimeOrderedRowsCollector
  • BaseDownloadingCollector,
  • BaseDownloadingTimeOrderedRowsCollector.

They are to be described in the future version of this guide…

(Also, a few more collector base classes are defined in that module – but those ones are just a legacy stuff and should not be used to implement new collectors.)

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