How to use Scrapy with Django Application(转自medium)


There are 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 triggets 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 database. (4)
  • django fetches that data from 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 don’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 database.


Well eventually, in 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 client when crawling completed.


We can start sending requests on 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:

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 progam.

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

Creating Django Project

Create a django project with an app named  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:

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 Scrapy Project\

It is better if we create 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:

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

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:

Now we should have a folder structure like this:

Connecting Scrapy to Django

In order to have access 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:

$ 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 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:

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. 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:

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 website
  • Put them into a list
  • Save the list to 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 have build 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 simplified version of my React Component:



You 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.








参考了面向新人的 Python 爬虫学习资料


一: 简单的定向脚本爬虫( request — bs4 — re )

二: 大型框架式爬虫( Scrapy 框架为主)

三:浏览器模拟爬虫 ( Mechanize 模拟 和 Selenium 模拟)








所以初级的爬虫的难度就仅仅在于找规律。。。然后配合chrome 开发者工具的模拟点击功能和 xpath这种文本解析工具… 就可以搞定了。。。