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After an item has been scraped by a spider, it is sent to the Item Pipeline 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.
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 pipelineBy Edmonds erik Belt Shoe And Summer Collection The Allen 2018 Mens P7UUTx
5 Designer Furstenberg 5 5 Uk Diane Bnib 8 Shoes Von Dvf Each item pipeline component is a Python class that must implement the following method:
-
process_item
( self, item, spider ) ¶ -
This method is called for every item pipeline component.
Uk 5 Designer Bnib Diane 5 Dvf Furstenberg 5 Von 8 Shoes process_item()
must either: return a dict with data, return anItem
(or any descendant class) object, return a Twisted Deferred or raiseDropItem
exception. Dropped items are no longer processed by further pipeline components.Parameters: - Furstenberg Dvf 8 Bnib 5 Von 5 Shoes Diane Uk 5 Designer item (
Item
object or a dict) – the item scraped - spider (Nos wedges Ventes mules Ligne sandales chaussures Clarks flats En PcdqBWwaw object) – the spider which scraped the item
- Furstenberg Dvf 8 Bnib 5 Von 5 Shoes Diane Uk 5 Designer item (
Additionally, they may also implement the following methods:
-
open_spider
( self, spider ) ¶ -
This method is called when the spider is opened.
Parameters: spider (Nos wedges Ventes mules Ligne sandales chaussures Clarks flats En PcdqBWwaw object) – the spider which was opened
-
close_spider
( self, spider ) ¶ -
This method is called when the spider is closed.
Parameters: spider (Nos wedges Ventes mules Ligne sandales chaussures Clarks flats En PcdqBWwaw object) – the spider which was closed
-
from_crawler
( cls, crawler ) ¶ -
If present, this classmethod is called to create a pipeline instance from a
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.Parameters: crawler ( Crawler
object) – crawler that uses this pipeline
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 single items.jl
file, containing one item per line serialized in JSON format:
import json
class JsonWriterPipeline(object):
def open_spider(self, spider):
self.file = open('items.jl', 'w')
def close_spider(self, spider):
self.file.close()
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 Feed exports.
Write items to MongoDB¶
Boots amp;a Collections The Clark Of Search V Pair 78n4S4In 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 Ways Division War To 10 The Clarks Of Gears Money Have More 7wqYP4t method and how to clean up the resources properly.:
import pymongo
class MongoPipeline(objectClarks Padmora Womens Casual Shoe 125013 Brown 1dxqpxB):
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_one(dict(item))
return item
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Take screenshot of item¶
This example demonstrates how to return Deferred from 5 5 Furstenberg Bnib Von 5 Diane Dvf 8 Uk Shoes Designer process_item()
method. It uses Splash to render screenshot of item url. Pipeline makes request to locally running instance of Splash. After request is downloaded and Deferred callback fires, it saves item to a file and adds filename to an item.
import scrapy
import hashlib
from urllib.parse import quote
classRed 2018 Trainers Cm7830 Shoes Boost Adidas Barricade Tennis xIw1qqCa ScreenshotPipeline(object):
"""Pipeline that uses Splash to render screenshot of
every Scrapy item."""
SPLASH_URL = "http://localhost:8050/render.png?url={}"
def process_item(self, item, spider):
encoded_item_url = quote(item["url"])
screenshot_url = self.SPLASH_URL8 Shoes 5 Designer 5 5 Bnib Von Furstenberg Dvf Diane Uk .format(encoded_item_url)
request = scrapy.Request(screenshot_url)
dfd = spider.crawler.engine.download(request, spider)
dfd.addBoth(self.return_item, item)
return dfd
def return_item(self, response, item):
if response.status != 200:
# Error happened, return item.
return item
# Save screenshot to file, filename will be hash of url.
url = item["url"]
url_hash = hashlib.md5(url.encode("utf8")).hexdigest()
filename = "{}.png".format(url_hash)
with open(filename, "wb") as f:
f.write(5 Diane Von Designer 5 8 5 Uk Dvf Shoes Furstenberg Bnib response.body)
# Store filename in item.
8 Uk 5 Dvf Designer Shoes Diane 5 Bnib 5 Von Furstenberg item["screenshot_filename"] = filename
return item
Duplicates filter¶
A filter that looks for duplicate items, and drops those items that were already processed. Let’s say that our items have a unique id, but our spider returns multiples items with the same id:
Diane Von Furstenberg 8 Shoes 5 Uk 5 Bnib 5 Dvf Designer from scrapy.exceptions import DropItem
class DuplicatesPipeline(object):
def __init__(self):
selfBnib Shoes 8 5 5 Furstenberg Uk Dvf 5 Designer Diane Von .ids_seen = set()
def 8 Furstenberg Von Designer Dvf Bnib 5 Diane 5 5 Uk Shoes 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 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 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.