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Incremental crawler with Scrapy and MongoDB


In this post I will show you how to scrape a website incrementally. Each new scraping session will only scrape new items. We will be crawling Techcrunch blog posts as an example here.

This tutorial will use Scrapy, a great Python scraping library. It's simple yet very powerful. If you don't know it, have a look at their overview page.

We will also use MongoDB, the famous NoSQL DB, but it would be a similar process with any DB you want.

TLDR; if you already know Scrapy, head to the last part about incremental scraping. You can find the full code for this project here on GitHub.

Setup your Scrapy spider #

Start by installing Scrapy

pip3 install scrapy

(in a real project, you would use a virtualenv and a requirements.txt file)

and initialize your project with:

scrapy startproject tc_scraper
cd tc_scraper
scrapy genspider techcrunch techcrunch.com

Scrape the posts #

Play around in the shell #

First have a look at the DOM structure on https://www.techcrunch.com, using your browser's developer tools.

Make sure to disable Javascript, because the scraper will not execute it by default. It's doable with Scrapy, but it's not the point of this tutorial. I'm using this extension to easily disable JS on Firefox.

inspection of Techcrunch's DOM in Firefox

You can then open a Scrapy shell with

scrapy shell https://www.techcrunch.com

This shell is very helpful to play around and figure out how to extract the data. Here are some commands you can try one by one:

response.css(".post-block")
posts = response.css(".post-block")
posts[0]
posts[0].css(".post-block__title__link")
title = posts[0].css(".post-block__title__link")
title
title.css("::attr(href)").extract()
title.css("::attr(href)").extract_first()

We are using CSS selectors, and the attr function on CSS3 pseudo-elements. Learn more about this extraction part in the scrapy docs.

Scrapy architecture #

scrapy architecture

This diagram from scrapy docs is a quick overview of how Scrapy works:

In this tutorial, we will not touch the Scheduler, nor the Downloader.

We will only write a Spider and tweak the Item Pipeline.

Scrape the list pages #

So let's write the first part of the scraper:

# spiders/techcrunch.py

import scrapy


class TechcrunchSpider(scrapy.Spider):
    name = 'techcrunch'
    allowed_domains = ['techcrunch.com']
    start_urls = ['https://techcrunch.com/']

    def parse(self, response):
        for post in response.css(".post-block"):
            title = post.css(".post-block__title__link")
            url = title.css("::attr(href)").extract_first()
            yield scrapy.Request(url, callback=self.parse_post)
        if response.css(".load-more"):
            next_page_url = response.css(".load-more::attr(href)").extract_first()
            yield scrapy.Request(next_page_url)

    def parse_post(self, response):
        pass

Here is a walkthrough of this spider:

You can run it with scrapy crawl techcrunch, but be aware that it will go through ALL the pages (thousands here), so be ready to hit CTRL+C to stop it!

Add a pages limit argument #

In order to avoid this problem, let's add a pages limit argument to our spider right now:

# spiders/techcrunch.py

import scrapy
import re


class TechcrunchSpider(scrapy.Spider):
    # ...

    def __init__(self, limit_pages=None, *args, **kwargs):
        super(TechcrunchSpider, self).__init__(*args, **kwargs)
        if limit_pages is not None:
            self.limit_pages = int(limit_pages)
        else:
            self.limit_pages = 0

    def parse(self, response):
        # ...
        if response.css(".load-more"):
            next_page_url = response.css(".load-more::attr(href)").extract_first()
            # urls look like https://techcrunch.com/page/4/
            match = re.match(r".*\/page\/(\d+)\/", next_page_url)
            next_page_number = int(match.groups()[0])
            if next_page_number <= self.limit_pages:
                yield scrapy.Request(next_page_url)
    # ...

In the constructor, we allow passing a new kwarg called limit_pages, which we cast to an integer. In the parse method, we extract the next page number thanks to a regex on the url. Then we compare it to the limit_pages argument, and only if it's below, we enqueue the next page request.

You can now run the spider safely with:

scrapy crawl techcrunch -a limit_pages=2

Scrape the post pages #

So far, we have left the scrape_post request empty, so our spider is not actually scraping anything. Here is what post pages look like (again, without JS):

a Techcrunch post page

Before writing the scraper method, we need to declare the items that we are going to scrape:

# items.py
import scrapy

class BlogPost(scrapy.Item):
    url = scrapy.Field()
    title = scrapy.Field()
    author = scrapy.Field()
    content = scrapy.Field()
    published_at = scrapy.Field()

Note: There is now a simple way to have a dynamic schema without manually declaring all the fields. I will show how to use it in a next article

You can open a new Scrapy shell to play around on any post page and figure out the selectors you are going to use. For example:

scrapy shell https://techcrunch.com/2017/05/01/awesomeness-is-launching-a-news-division-aimed-at-gen-z/

And once you have figured them out, you can write the scraper's missing method:

# spiders/techcrunch.py

from tc_scraper.items import BlogPost
import datetime


class TechcrunchSpider(scrapy.Spider):

    # ...

    def parse_post(self, response):
        item = BlogPost(
            title=response.css("h1::text").extract_first(),
            author=response.css(".article__byline>a::text").extract_first().strip(),
            published_at=self.extract_post_date(response),
            content=self.extract_content(response),
            url=response.url
        )
        yield(item)

    def extract_post_date(self, response):
        date_text = response.css("meta[name='sailthru.date']::attr(content)").extract_first()
        return datetime.datetime.strptime(date_text, "%Y-%m-%d %H:%M:%S" )

    def extract_content(self, response):
        paragraphs_texts = [
        p.css(" ::text").extract()
            for p in response.css(".article-content>p")
        ]
        paragraphs = ["".join(p) for p in paragraphs_texts]
        paragraphs = [re.subn("\n", "", p)[0] for p in paragraphs]
        paragraphs = [p for p in paragraphs if p.strip() != ""]
        return "\n\n".join(paragraphs)

So what's going here?

You can now run the spider with:

scrapy crawl techcrunch -a limit_pages=2 -o posts.json

and Scrapy will generate a nice posts.json file with all the scraped items. Yay!

display posts.json contents

Incremental Scraping #

Store items in MongoDB #

So in the last step we exported the items to a JSON file. For long term storage and re-use, it's more convenient to use a database. We will use MongoDB here, but you could use a regular SQL database too. I personally find it convenient on scraping projects to use a NoSQL database because of the frequent schema changes, especially as you initially iterate on the scraper.

If you are on Mac OS X, these commands will install MongoDB server and start it as a service:

brew install mongodb
brew services start mongodb

We are going to create a Scrapy pipeline so that each yielded item will get saved to MongoDB. This process is well documented in Scrapy's docs.

# pipelines.py

import pymongo


class MongoPipeline(object):

    collection_name = 'tc_posts'

    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', 'tc_scraper')
        )

    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

and activate it in the settings:

# settings.py

ITEM_PIPELINES = {
   'tc_scraper.pipelines.MongoPipeline': 300,
}

Last step is to install the new pymongo dependency:

pip3 install pymongo

You can now re-run the spider:

scrapy crawl techcrunch -a limit_pages=2

Feel free to open a mongo shell and check that the items were indeed inserted:

mongo localhost/tc_scraper
> db.tc_posts.count()
40
> db.tc_posts.findOne()
{
	"_id": ObjectId("5c09294a7fa9c70f84e43322"),
	"url": "https://techcrunch.com/2018/12/06/looker-looks-to-future-with-103m-investment-on-1-6b-valuation/",
	"author": "Ron Miller",

  ...

Update existing items #

If you run the spider again, you will notice that you now have 80 items in your database. Let's update the Pipeline so that it does not insert a new post each time, but rather updates the existing one if it already exists.

# pipelines.py

...

    def process_item(self, item, spider):
        self.db[self.collection_name].find_one_and_update(
            {"url": item["url"]},
            {"$set": dict(item)},
            upsert=True
        )
        return item

Here we are using the url as the key, because unfortunately there does not seem to be a more canonical ID in the DOM. It should work as long as Techcrunch does not change their posts slugs or published dates often.

You can drop all items and re-run the spider twice:

echo "db.tc_posts.drop();" | mongo localhost/tc_scraper
scrapy crawl techcrunch -a limit_pages=2
scrapy crawl techcrunch -a limit_pages=2
echo "db.tc_posts.count();" | mongo localhost/tc_scraper

You should now have only 40 items in the database.

Note: If you are scared about running so many requests against Techcrunch.com, be aware that by default, Scrapy will use a cache. For most requests, it will not re-run them every single time, but instead re-use the previous response

Limit crawls to new items #

So far our solution is almost complete, but it will re-scrape all items every time you start it. In this step, we will make sure that we don't re-scrape items uselessly, in order to have faster scraping sessions, and to limit our requests rate to the website.

What we will do is to update the spider so that it prevents requests to items that were already scraped before and are in the database.

First let's extract the MongoDB connection logic from the pipeline in order to re-use it in the spider:

# mongo_provider.py (new file)

import pymongo


class MongoProvider(object):

    collection_name = 'tc_posts'

    def __init__(self, uri, database):
        self.mongo_uri = uri
        self.mongo_db = database or 'tc_scraper'

    def get_collection(self):
        self.client = pymongo.MongoClient(self.mongo_uri)
        return self.client[self.mongo_db][self.collection_name]

    def close_connection(self):
        self.client.close()

and update the pipeline accordingly:

# pipelines.py

from tc_scraper.mongo_provider import MongoProvider


class MongoPipeline(object):

    def __init__(self, settings):
        self.mongo_provider = MongoProvider(
            settings.get('MONGO_URI'),
            settings.get('MONGO_DATABASE')
        )

    @classmethod
    def from_crawler(cls, crawler):
        return cls(crawler.settings)

    def open_spider(self, spider):
        self.collection = self.mongo_provider.get_collection()

    def close_spider(self, spider):
        self.mongo_provider.close_connection()

    def process_item(self, item, spider):
        self.collection.find_one_and_update(
            {"url": item["url"]},
            {"$set": dict(item)},
            upsert=True
        )
        return item

You can re-run the scraper at this checkpoint, nothing should have changed

We can now update the spider so that it uses this mongo provider:

# spiders/techcrunch.py

...
from tc_scraper.mongo_provider import MongoProvider


class TechcrunchSpider(scrapy.Spider):
    ...
    @classmethod
    def from_crawler(cls, crawler, *args, **kwargs):
        kwargs['mongo_uri'] = crawler.settings.get("MONGO_URI")
        kwargs['mongo_database'] = crawler.settings.get('MONGO_DATABASE')
        return super(TechcrunchSpider, cls).from_crawler(crawler, *args, **kwargs)

    def __init__(self, limit_pages=None, mongo_uri=None, mongo_database=None, *args, **kwargs):
        ...
        self.mongo_provider = MongoProvider(mongo_uri, mongo_database)
        self.collection = self.mongo_provider.get_collection()
        last_items = self.collection.find().sort("published_at", -1).limit(1)
        self.last_scraped_url = last_items[0]["url"] if last_items.count() else None

    def parse(self, response):
        for post in response.css(".post-block"):
            ...
            if url == self.last_scraped_url:
                print("reached last item scraped, breaking loop")
                return
            else:
                yield scrapy.Request(url, callback=self.parse_post)

Here is a quick breakdown of what we are doing here:

You can now perform a few tests, drop some of the last items from MongoDB and re-scrape, you will see that it only scrapes the missing items and stops. Success!

mongo localhost/tc_scraper
> last_item = db.tc_posts.find().sort({published_at: -1})[0]
> db.tc_posts.remove({_id: last_item["_id"]})
> exit
scrapy crawl techcrunch -a limit_pages=2

Conclusion #

We now have a scraper that will do the least amount of work possible on each new run. I hope you enjoyed this tutorial, and that this gave you new ideas for scraping projects!

You can find the full code for this project here on GitHub: adipasquale/techcrunch-incremental-scrapy-spider-with-mongodb.

You can deploy your spider to ScrapingHub cloud, and schedule it to run daily on their servers. I'm not affiliated to them in any way, it's just an awesome product and their free plan already does a lot. By the way, ScrapingHub is the main contributor to the fully open-source Scrapy project that we just used.

To go further, you can implement a new force_rescrape argument, that will bypass our limit and force going through all the items again. This could be useful if you update the scrape_post method, or if Techcrunch changes their DOM structure.

Let me know if you use this technique in one of your projects!

Discuss on Hacker News