Web Scraping with Scrapy pipeline to add crawled data to MongoDB collection [Tutorial]

In this tutorial i want to show you how to add the scraped data from scrapy crawler to a MongoDB database. For this we will use the scrapy crawler pipeline with the correct connection to a localhost server.

This tutorial will walk you through these tasks:

In this Scrapy project I scrape quotes from https://quotes.toscrape.com/

  • Creating a new Scrapy project
  • Setting up the Database connection and spider items.
  • Writing a spider to crawl a site and extract data
  • Exporting the scraped data using the pipeline to a MongoDB database collection

Install Scrapy if dont already have it installed.

pip install scrapy

Before you start scraping, you will have to set up a new Scrapy project. Enter a directory where you’d like to store your code and run:

scrapy startproject mongodbtutorial
command line response after creating a new project with scrapy

This will create a tutorial directory with the following contents:

    scrapy.cfg            # deploy configuration file

    tutorial/             # project's Python module, you'll import your code from here

        items.py          # project items definition file

        middlewares.py    # project middlewares file

        pipelines.py      # project pipelines file

        settings.py       # project settings file

        spiders/          # a directory where you'll later put your spiders

Now you have created a new project and in this step you need to set up the database credentials. For this step open the file mongodbtutorial –> settings.py and add following code to your settings file.

MONGO_SERVER = "". # server ip adress
MONGO_PORT = 27017.  # port of mongodb connection
MONGO_DB = "quotes".   # name of the database
MONGODB_COLLECTION = "quotes"    # name of your mongodb collection

And below the sentences in settings.py you need also configure the Item pipeline:

# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
    'mongodbtutorial.pipelines.MongodbtutorialPipeline': 600,
    'mongodbtutorial.pipelines.MongoDBPipeline': 600,

and enable the spider middleware:

    'mongodbtutorial.middlewares.MongodbtutorialSpiderMiddleware': 543,

after this changes in settings.py, you need to prepare your pipeline file under mongodbtutorial –> pipeline.py

at first import a few libraries and define your pipeline:

from logging import log
import scrapy
from pymongo import MongoClient

from scrapy import settings
from scrapy.exceptions import DropItem

# useful for handling different item types with a single interface
from itemadapter import ItemAdapter

add a new class named MongoDBPipeline below the class MongodbtutorialPipeline in pipeline.

class MongoDBPipeline(object):

    def __init__(self):
        connection = MongoClient()
        connection = MongoClient('localhost', 27017)
        db = connection.quotes
        self.collection = db['quotes']

    def process_item(self, item, spider):
        for data in item:
            if not data:
                raise DropItem("Missing data!")
        self.collection.replace_one({'url': item['url']}, dict(item), upsert=True)
        log.msg("Quote added to MongoDB collection!",
                level=log.DEBUG, spider=spider)
        return item

And now you need define your items in items.py with the field names on your collection.

class MongodbtutorialItem(scrapy.Item):
    text = scrapy.Field()
    author = scrapy.Field()
    tags = scrapy.Field()

then run the following code to run your the spider

scrapy crawl quotes

and now we have all the quotes in our database collection:

scraped data in mongodb collection

Extracted data:

'text': '“A day without sunshine is like, you know, night.”', 
'author': 'Steve Martin', 
'tags': ['humor', 'obvious', 'simile']

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