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scrapy/docs/topics/item-pipeline.rst

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.. _topics-item-pipeline:
=============
Item Pipeline
=============
After an item has been scraped by a spider, it is sent to the Item Pipeline
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which processes it through several components that are executed sequentially.
Each item pipeline component (sometimes referred as just "Item Pipeline") is a
Python class that implements a simple method. They receive an item and perform
an action over it, also deciding if the item should continue through the
pipeline or be dropped and no longer processed.
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Typical uses of item pipelines are:
* cleansing HTML data
* validating scraped data (checking that the items contain certain fields)
* checking for duplicates (and dropping them)
* storing the scraped item in a database
Writing your own item pipeline
==============================
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Each item pipeline component is a Python class that must implement the following method:
.. method:: process_item(self, item, spider)
This method is called for every item pipeline component and must either return
a dict with data, :class:`~scrapy.item.Item` (or any descendant class) object
or raise a :exc:`~scrapy.exceptions.DropItem` exception. Dropped items are no longer
processed by further pipeline components.
:param item: the item scraped
:type item: :class:`~scrapy.item.Item` object or a dict
:param spider: the spider which scraped the item
:type spider: :class:`~scrapy.spiders.Spider` object
Additionally, they may also implement the following methods:
.. method:: open_spider(self, spider)
This method is called when the spider is opened.
:param spider: the spider which was opened
:type spider: :class:`~scrapy.spiders.Spider` object
.. method:: close_spider(self, spider)
This method is called when the spider is closed.
:param spider: the spider which was closed
:type spider: :class:`~scrapy.spiders.Spider` object
.. method:: from_crawler(cls, crawler)
If present, this classmethod is called to create a pipeline instance
from a :class:`~scrapy.crawler.Crawler`. It must return a new instance
of the pipeline. Crawler object provides access to all Scrapy core
components like settings and signals; it is a way for pipeline to
access them and hook its functionality into Scrapy.
:param crawler: crawler that uses this pipeline
:type crawler: :class:`~scrapy.crawler.Crawler` object
Item pipeline example
=====================
Price validation and dropping items with no prices
--------------------------------------------------
Let's take a look at the following hypothetical pipeline that adjusts the
``price`` attribute for those items that do not include VAT
(``price_excludes_vat`` attribute), and drops those items which don't
contain a price::
from scrapy.exceptions import DropItem
class PricePipeline(object):
vat_factor = 1.15
def process_item(self, item, spider):
if item['price']:
if item['price_excludes_vat']:
item['price'] = item['price'] * self.vat_factor
return item
else:
raise DropItem("Missing price in %s" % item)
Write items to a JSON file
--------------------------
The following pipeline stores all scraped items (from all spiders) into a a
single ``items.jl`` file, containing one item per line serialized in JSON
format::
import json
class JsonWriterPipeline(object):
def __init__(self):
self.file = open('items.jl', 'wb')
def process_item(self, item, spider):
line = json.dumps(dict(item)) + "\n"
self.file.write(line)
return item
.. note:: The purpose of JsonWriterPipeline is just to introduce how to write
item pipelines. If you really want to store all scraped items into a JSON
file you should use the :ref:`Feed exports <topics-feed-exports>`.
Write items to MongoDB
----------------------
In this example we'll write items to MongoDB_ using pymongo_.
MongoDB address and database name are specified in Scrapy settings;
MongoDB collection is named after item class.
The main point of this example is to show how to use :meth:`from_crawler`
method and how to clean up the resources properly.
.. note::
Previous example (JsonWriterPipeline) doesn't clean up resources properly.
Fixing it is left as an exercise for the reader.
::
import pymongo
class MongoPipeline(object):
collection_name = 'scrapy_items'
def __init__(self, mongo_uri, mongo_db):
self.mongo_uri = mongo_uri
self.mongo_db = mongo_db
@classmethod
def from_crawler(cls, crawler):
return cls(
mongo_uri=crawler.settings.get('MONGO_URI'),
mongo_db=crawler.settings.get('MONGO_DATABASE', 'items')
)
def open_spider(self, spider):
self.client = pymongo.MongoClient(self.mongo_uri)
self.db = self.client[self.mongo_db]
def close_spider(self, spider):
self.client.close()
def process_item(self, item, spider):
self.db[self.collection_name].insert(dict(item))
return item
.. _MongoDB: http://www.mongodb.org/
.. _pymongo: http://api.mongodb.org/python/current/
Duplicates filter
-----------------
A filter that looks for duplicate items, and drops those items that were
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already processed. Let's say that our items have a unique id, but our spider
returns multiples items with the same id::
from scrapy.exceptions import DropItem
class DuplicatesPipeline(object):
def __init__(self):
self.ids_seen = set()
def process_item(self, item, spider):
if item['id'] in self.ids_seen:
raise DropItem("Duplicate item found: %s" % item)
else:
self.ids_seen.add(item['id'])
return item
Activating an Item Pipeline component
=====================================
To activate an Item Pipeline component you must add its class to the
:setting:`ITEM_PIPELINES` setting, like in the following example::
ITEM_PIPELINES = {
'myproject.pipelines.PricePipeline': 300,
'myproject.pipelines.JsonWriterPipeline': 800,
}
The integer values you assign to classes in this setting determine the
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order in which they run: items go through from lower valued to higher
valued classes. It's customary to define these numbers in the 0-1000 range.