How to use Scrapy with Django Application(转自medium)

Posted by 111qqz on Tuesday, November 6, 2018



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:

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

$ django-admin startproject iCrawler
$ cd iCrawler && python startapp main

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

import json
from django.db import models
from django.utils import timezone

class ScrapyItem(models.Model):
    unique_id = models.CharField(max_length=100, null=True)
    data = models.TextField() # this stands for our crawled data
    date = models.DateTimeField(
    # This is for basic and custom serialisation to return it to client as a JSON.
    def to_dict(self):
        data = {
            'data': json.loads(,
        return data

    def __str__(self):
        return self.unique_id

Add main app into INSTALLED_APPS in And as a final step, migrations:

$ python makemigrations
$ python migrate

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

from uuid import uuid4
from urllib.parse import urlparse
from django.core.validators import URLValidator
from django.core.exceptions import ValidationError
from django.views.decorators.http import require_POST, require_http_methods
from django.shortcuts import render
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from scrapyd_api import ScrapydAPI
from main.utils import URLUtil
from main.models import ScrapyItem

# connect scrapyd service
scrapyd = ScrapydAPI('http://localhost:6800')

def is_valid_url(url):
    validate = URLValidator()
        validate(url) # check if url format is valid
    except ValidationError:
        return False

    return True

@require_http_methods(['POST', 'GET']) # only get and post
def crawl(request):
    # Post requests are for new crawling tasks
    if request.method == 'POST':

        url = request.POST.get('url', None) # take url comes from client. (From an input may be?)

        if not url:
            return JsonResponse({'error': 'Missing  args'})
        if not is_valid_url(url):
            return JsonResponse({'error': 'URL is invalid'})
        domain = urlparse(url).netloc # parse the url and extract the domain
        unique_id = str(uuid4()) # create a unique ID. 
        # This is the custom settings for scrapy spider. 
        # We can send anything we want to use it inside spiders and pipelines. 
        # I mean, anything
        settings = {
            'unique_id': unique_id, # unique ID for each record for DB
            'USER_AGENT': 'Mozilla/5.0 (compatible; Googlebot/2.1; +'

        # Here we schedule a new crawling task from scrapyd. 
        # Notice that settings is a special argument name. 
        # But we can pass other arguments, though.
        # This returns a ID which belongs and will be belong to this task
        # We are goint to use that to check task's status.
        task = scrapyd.schedule('default', 'icrawler', 
            settings=settings, url=url, domain=domain)

        return JsonResponse({'task_id': task, 'unique_id': unique_id, 'status': 'started' })

    # Get requests are for getting result of a specific crawling task
    elif request.method == 'GET':
        # We were passed these from past request above. Remember ?
        # They were trying to survive in client side.
        # Now they are here again, thankfully. <3
        # We passed them back to here to check the status of crawling
        # And if crawling is completed, we respond back with a crawled data.
        task_id = request.GET.get('task_id', None)
        unique_id = request.GET.get('unique_id', None)

        if not task_id or not unique_id:
            return JsonResponse({'error': 'Missing args'})

        # Here we check status of crawling that just started a few seconds ago.
        # If it is finished, we can query from database and get results
        # If it is not finished we can return active status
        # Possible results are -> pending, running, finished
        status = scrapyd.job_status('default', task_id)
        if status == 'finished':
                # this is the unique_id that we created even before crawling started.
                item = ScrapyItem.objects.get(unique_id=unique_id) 
                return JsonResponse({'data': item.to_dict['data']})
            except Exception as e:
                return JsonResponse({'error': str(e)})
            return JsonResponse({'status': status})

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:

from django.conf import settings
from django.conf.urls import url,static
from django.views.generic import TemplateView
from main import views

urlpatterns = [
    url(r'^$', TemplateView.as_view(template_name='index.html'), name='home'),
    url(r'^api/crawl/', views.crawl, name='crawl'),

# This is required for static files while in development mode. (DEBUG=TRUE)
# No, not relevant to scrapy or crawling :)
if settings.DEBUG:
    urlpatterns += static.static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)
    urlpatterns += static.static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

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:

$ cd iCrawler
$ scrapy startproject scrapy_app

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

$ cd scrapy_app
$ scrapy genspider -t crawl icrawler

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:

$ scrapy genspider -l
Available templates:

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 file under scrapy_app/scrapy_app/ and put:

import os
import sys


# Do not forget the change iCrawler part based on your project name
os.environ['DJANGO_SETTINGS_MODULE'] = 'iCrawler.settings'

# This is required only if Django Version > 1.8
import django


## Rest of settings are below ..

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:

class IcrawlerSpider(CrawlSpider):
    name = 'icrawler'

    def __init__(self, *args, **kwargs):
        # We are going to pass these args from our django view.
        # To make everything dynamic, we need to override them inside __init__ method
        self.url = kwargs.get('url')
        self.domain = kwargs.get('domain')
        self.start_urls = [self.url]
        self.allowed_domains = [self.domain]

        IcrawlerSpider.rules = [
           Rule(LinkExtractor(unique=True), callback='parse_item'),
        super(IcrawlerSpider, self).__init__(*args, **kwargs)

    def parse_item(self, response):
        # You can tweak each crawled page here
        # Don't forget to return an object.
        i = {}
        i['url'] = response.url
        return i

Above is 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 inside scrapy_project folder. And also that file contains an empty-but-ready pipeline. We just need to modify it a little bit:

from main.models import ScrapyItem
import json

class ScrapyAppPipeline(object):
    def __init__(self, unique_id, *args, **kwargs):
        self.unique_id = unique_id
        self.items = []

    def from_crawler(cls, crawler):
        return cls(
            unique_id=crawler.settings.get('unique_id'), # this will be passed from django view

    def close_spider(self, spider):
        # And here we are saving our crawled data with django models.
        item = ScrapyItem()
        item.unique_id = self.unique_id = json.dumps(self.items)

    def process_item(self, item, spider):
        return item

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

# Configure item pipelines
# See
    '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 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:

class Home extends React.Component {
  constructor(props) {
      this.state = {
          url: '',
          crawlingStatus: null,
          data: null,
          taskID: null,
          uniqueID: null
      this.statusInterval = 1

  handleStartButton = (event) => {
    if (!this.state.url) return false;

    // send a post request to client when form button clicked
    // django response back with task_id and unique_id.
    // We have created them in file, remember?
    $.post('/api/crawl/', { url: this.state.url }, resp => {
        if (resp.error) {
        // Update the state with new task and unique id
            taskID: resp.task_id,
            uniqueID: resp.unique_id,
            crawlingStatus: resp.status
        }, () => {
            // ####################### HERE ########################
            // After updating state, 
            // i start to execute checkCrawlStatus method for every 2 seconds
            // Check method's body for more details
            // ####################### HERE ########################
            this.statusInterval = setInterval(this.checkCrawlStatus, 2000)

  componentWillUnmount() {
      // i create this.statusInterval inside constructor method
      // So clear it anyway on page reloads or 

  checkCrawlStatus = () => {
      // this method do only one thing.
      // Making a request to server to ask status of crawling job
            { task_id: this.state.taskID, unique_id: this.state.uniqueID }, resp => {
          if ( {
              // If response contains a data array
              // That means crawling completed and we have results here
              // No need to make more requests.
              // Just clear interval
          } else if (resp.error) {
              // If there is an error
              // also no need to keep requesting
              // just show it to user
              // and clear interval
          } else if (resp.status) {
              // but response contains a `status` key and no data or error
              // that means crawling process is still active and running (or pending)
              // don't clear the interval.
                  crawlingStatus: resp.status
  render () {
    // render componenet
    return (<div></div>)

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.