How to use Scrapy with Django Application

There are a couple of articles on how to integrate Scrapy into a Django Application (or vice versa?). But most of them don’t cover a full complete example that includes triggering spiders from Django views. Since this is a web application, that must be our main goal.

What do we need?

Before we start, it is better to specify what we want and how we want it. Check this diagram:

It shows how our app should work:

  • Client sends a request with a URL to crawl it. (1)
  • Django triggers Scrapy to run a spider to crawl that URL. (2)
  • Django returns a response to tell Client that crawling just started. (3)
  • Scrapy completes crawling and saves extracted data into a database. (4)
  • Django fetches that data from the database and return it to Client. (5) Looks great and simple so far.

A note on that 5th statement

Django fetches that data from database and return it to Client. (5)Neither Django nor client doesn’t know when Scrapy completes crawling. There is a callback method named pipeline_closed, but it belongs to Scrapy project. We can’t return a response from Scrapy pipelines. We use that method only to save extracted data into a database.

Well eventually, somewhere, we have to tell the client :

Hey! Crawling completed and i am sending you crawled data here.There are two possible ways of this (Please comment if you discover more):

We can either use web sockets to inform the client when crawling completed.

Or,

We can start sending requests every 2 seconds (more? or less ?) from client to check crawling status after we get the “crawling started” response.

Web Socket solution sounds more stable and robust. But it requires a second service running separately and means more configuration. I will skip this option for now. But I would choose web sockets for my production-level applications.

Let’s write some code

It’s time to do some real job. Let’s start by preparing our environment.

Installing Dependencies

Create a virtual environment and activate it:

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$ python3.5 -m venv venv  
$ source venv/bin/activate

Then install required dependencies with:

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$ pip install django scrapy scrapyd python-scrapyd-api

Scrapyd is a daemon service for running Scrapy spiders. You can discover its details from here.

python-scrapyd-api is a wrapper allows us to talk scrapyd from our Python program.

Note: I am going to use Python 3.5 for this project.

Creating a Django Project

Create a Django project with an app named main :

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$ django-admin startproject iCrawler  
$ cd iCrawler && python manage.py startapp main

We also need a model to save our scraped data. Let’s keep it simple:

Add main app into INSTALLED_APPS in settings.py:

And as a final step, migrations:

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$ python manage.py makemigrations  
$ python manage.py migrate

Let’s add a view and URL to our main app:

I tried to document the code as much as I can.

But the main trick is, unique_id. Normally, we save an object to database, then we get its ID. In our case, we are specifying its unique_id before creating it. Once crawling completed and client asks for the crawled data; we can create a query with that unique_id and fetch results.

And an URL for this view:

Creating the Scrapy Project

It is better if we create the Scrapy project under (or next to) our Django project. This makes easier to connect them together. So let’s create it under Django project folder:

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$ cd iCrawler  
$ scrapy startproject scrapy_app

Now we need to create our first spider from inside scrapy_app folder:

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$ cd scrapy_app  
$ scrapy genspider -t crawl icrawler https://google.com

I name spider as icrawler. You can name it as anything. Look -t crawl part. We specify a base template for our spider. You can see all available templates with:

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$ scrapy genspider -l

Available templates:
basic
crawl
csvfeed
xmlfeed

Now we should have a folder structure like this:

Connecting Scrapy to Django

In order to have access to Django models from Scrapy, we need to connect them together. Go to settings.py file under scrapy_app/scrapy_app/ and put:

That’s it. Now let’s start scrapyd to make sure everything installed and configured properly. Inside scrapy_app/ folder run:

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$ scrapyd

This will start scrapyd and generate some outputs. Scrapyd also has a very minimal and simple web console. We don’t need it on production but we can use it to watch active jobs while developing. Once you start the scrapyd go to http://127.0.0.1:6800 and see if it is working.

Configuring Our Scrapy Project

Since this post is not about fundamentals of scrapy, I will skip the part about modifying spiders. You can create your spider with official documentation. I will put my example spider here, though:

Our spider file:

Above is icrawler.py file from scrapy_app/scrapy_app/spiders. Attention to __init__ method. It is important. If we want to make a method or property dynamic, we need to define it under __init__ method, so we can pass arguments from Django and use them here.

We also need to create a Item Pipeline for our scrapy project. A pipeline is a class for making actions over scraped items. From documentation:

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

Yay! Storing the scraped item in a database. Now let’s create one. Actually, there is already a file named pipelines.py inside scrapy_project folder. And also that file contains an empty-but-ready pipeline. We just need to modify it a little bit:

And as a final step, we need to enable (uncomment) this pipeline in scrapy settings.py file:

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# Configure item pipelines
# See [http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html](http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html)
ITEM_PIPELINES = {
'scrapy_app.pipelines.ScrapyAppPipeline': 300,
}

Don’t forget to restart scraypd if it is working.

This scrapy project basically,

  • Crawls a website (comes from Django view)
  • Extract all URLs from the website
  • Put them into a list
  • Save the list to the database over Django models.

And that’s all for the back-end part. Django and Scrapy are both integrated and should be working fine.

Notes on Front-End Part

Well, this part is so subjective. We have tons of options. Personally, I built my front-end with React . The only part that is not subjective is usage of setInterval . Yes, let’s remember our options: web sockets and to send requests to server every X seconds.

To clarify base logic, this is a simplified version of my React Component:

My main React ComponentYou can discover the details by comments I added. It is quite simple actually.

Oh, that’s it. It took longer than I expected. Please leave a comment for any kind of feedback.

Sample Projects

Below you can find some example implementations based on this article. If you have one, please mention it as a comment. I will keep updating this section.

This is a nice template app from Adrian Castellanos Zaragoza:

adriancast/Scrapyd-Django-Template
_Basic setup to run ScrapyD + Django and save it in Django Models. You can be up and running in just a few minutes. This…_github.com

This from 심명훈 and also includes frontend part with plain Javascript. Check this out!

copyNdpaste/scrapy-with-django
_CrawlerWithScrapyAndDjango. This crawler crawling posts of board. Site urls are below. ‘p’ parameter means page…_github.com