Pipelets are plugins to the Squirro pipeline, used to customize the data processing.
Table of Contents
Overview
Items that are processed by Squirro go through a pipeline process before they are indexed. In that process a number of built-in enrichments are executed. On top of that, custom enrichment steps can be inserted in the form of pipelets. These pipelets are written in the Python programming language. Pipelets can be uploaded to the Squirro server and then be configured in the user interface (Enrichments tab) or through the API.
This reference documentation covers the basic workflow of working with pipelets and the interface that pipelets need to implement.
Writing Pipelets
Pipelets are written in Python. They need to inherit from the squirro.sdk.PipeletV1
class and implement the consume
method. The simplest possible Pipelet looks like this:
from squirro.sdk import PipeletV1 class NoopPipelet(PipeletV1): def consume(self, item): return item
As it name says it does nothing but return the item unchanged. The item can be modified before it is returned. For example:
from squirro.sdk import PipeletV1 class ModifyTitlePipelet(PipeletV1): def consume(self, item): item['title'] = item.get('title', '') + ' - Hello, World!' return item
This pipelet will modify each item it processes, appending the string "Hello, World!" to the title. All the item's fields can be modified. The available fields are documented in the Item Format reference.
Returning multiple items
The pipelet is always called for each item individually. But in some use cases the pipelet should not just return one item but multiple ones. In those cases use the Python yield
statement to return each individual item. For example:
from squirro.sdk import PipeletV1 class ExtendTitlePipelet(PipeletV1): def consume(self, item): for i in range(10): new_item = dict(item) new_item['title'] = '{0} ({1})'.format(item.get('title', ''), i) yield new_item
Dependencies
Pipelets are limited in what you can do. For example the print
statement is disallowed and you can not import any external libraries except squirro.sdk
. If you do need access to external libraries, you need to use the @require
decorator. For example to log some output:
from squirro.sdk import PipeletV1, require @require('log') class LoggingPipelet(PipeletV1): def consume(self, item): self.log.debug('Processing item: %r', item['id']) return item
As seen from the example, the @require
decorator takes a name of a dependency. That dependency is then made available to the pipelet class.
HTTP requests can be executed by using the requests
dependency. The following pipelet shows an example for sentiment detection:
from squirro.sdk import PipeletV1, require @require('requests') class SentimentPipelet(PipeletV1): def consume(self, item): text_content = ' '.join([item.get('title', ''), item.get('body', '')]) res = self.requests.post('http://example.com/detect', data={'text': text_content}, headers={'Accept': 'application/json'}) sentiment = res.json()['sentiment'] item.setdefault('keywords', {})['sentiment'] = [sentiment] return item
Available Dependencies
The following dependencies can be requested:
Dependency | Description |
---|---|
cache | Non-persisted cache. |
log | A logging.Logger instance from Python's standard logging framework. |
requests | Python requests library for to execute HTTP requests. |