This Scrapy project uses Redis and Kafka to create a distributed on demand scraping cluster.

The goal is to distribute seed URLs among many waiting spider instances, whose requests are coordinated via Redis. Any other crawls those trigger, as a result of frontier expansion or depth traversal, will also be distributed among all workers in the cluster.

The input to the system is a set of Kafka topics and the output is a set of Kafka topics. Raw HTML and assets are crawled interactively, spidered, and output to the log. For easy local development, you can also disable the Kafka portions and work with the spider entirely via Redis, although this is not recommended due to the serialization of the crawl requests.

Please see the requirements.txt within each sub project for Pip package dependencies.

Other important components required to run the cluster

• Python 2.7: https://www.python.org/downloads/
• Redis: http://redis.io
• Zookeeper: https://zookeeper.apache.org
• Kafka: http://kafka.apache.org

This project tries to bring together a bunch of new concepts to Scrapy and large scale distributed crawling in general. Some bullet points include:

• The spiders are dynamic and on demand, meaning that they allow the arbitrary collection of any web page that is submitted to the scraping cluster
• Scale Scrapy instances across a single machine or multiple machines
• Coordinate and prioritize their scraping effort for desired sites
• Persist data across scraping jobs
• Execute multiple scraping jobs concurrently
• Allows for in depth access into the information about your scraping job, what is upcoming, and how the sites are ranked
• Allows you to arbitrarily add/remove/scale your scrapers from the pool without loss of data or downtime
• Utilizes Apache Kafka as a data bus for any application to interact with the scraping cluster (submit jobs, get info, stop jobs, view results)
• Allows for coordinated throttling of crawls from independent spiders on separate machines, but behind the same IP Address
• Enables completely different spiders to yield crawl requests to each other, giving flexibility to how the crawl job is tackled

To set up a pre-canned Scrapy Cluster test environment, make sure you have the latest Virtualbox + Vagrant >= 1.7.4 installed. Vagrant will automatically mount the base scrapy-cluster directory to the /vagrant directory, so any code changes you make will be visible inside the VM. Please note that at time of writing this will not work on a Windows machine.

1. vagrant up in base scrapy-cluster directory.
2. vagrant ssh to ssh into the VM.
3. sudo supervisorctl status to check that everything is running.
4. virtualenv sc to create a virtual environment
5. source sc/bin/activate to activate the virtual environment
6. cd /vagrant to get to the scrapy-cluster directory.
7. pip install -r requirements.txt to install Scrapy Cluster dependencies.
8. ./run_offline_tests.sh to run offline tests.
9. ./run_online_tests.sh to run online tests (relies on kafka, zookeeper, redis).

Please check out the official Scrapy Cluster 1.2.1 documentation for more information on how everything works!

The master branch of this repository contains the latest stable release code for Scrapy Cluster 1.2.1.

The dev branch contains bleeding edge code and is currently working towards Scrapy Cluster 1.3. Please note that not everything may be documented, finished, tested, or finalized but we are happy to help guide those who are interested.