Crawlab

中文 | English

Celery-based web crawler admin platform for managing distributed web spiders regardless of languages and frameworks.

Demo | Documentation

Pre-requisite

Python 3.6+
Node.js 8.12+
MongoDB
Redis

Installation

Threee methods:

Docker (Recommended)
Direct Deploy
Preview (Quick start)

Screenshot

Home Page

Spider List

Spider Detail - Overview

Spider Detail - Analytics

Task Detail - Results

Cron Schedule

Architecture

Crawlab's architecture is very similar to Celery's, but a few more modules including Frontend, Spiders and Flower are added to feature the crawling management functionality.

Nodes

Nodes are actually the workers defined in Celery. A node is running and connected to a task queue, redis for example, to receive and run tasks. As spiders need to be deployed to the nodes, users should specify their ip addresses and ports before the deployment.

Spiders

The spider source codes and configured crawling rules are stored on App, which need to be deployed to each worker node.

Tasks

Tasks are triggered and run by the workers. Users can view the task status, logs and results in the task detail page.

App

This is a Flask app that provides necessary API for common operations such as CRUD, spider deployment and task running. Each node has to run the flask app to get spiders deployed on this machine. Simply run python manage.py app or python ./bin/run_app.py to start the app.

Broker

Broker is the same as defined in Celery. It is the queue for running async tasks.

Frontend

Frontend is basically a Vue SPA that inherits from Vue-Element-Admin of PanJiaChen. Thanks for his awesome template.

Integration with Other Frameworks

A task is triggered via Popen in python subprocess module. A Task ID is will be defined as a variable CRAWLAB_TASK_ID in the shell environment to link the data to the task.

In your spider program, you should store the CRAWLAB_TASK_ID value in the database with key task_id. Then Crawlab would know how to link those results to a particular task. For now, Crawlab only supports MongoDB.

Scrapy

Below is an example to integrate Crawlab with Scrapy in pipelines.

import os
from pymongo import MongoClient

MONGO_HOST = '192.168.99.100'
MONGO_PORT = 27017
MONGO_DB = 'crawlab_test'

# scrapy example in the pipeline
class JuejinPipeline(object):
    mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)
    db = mongo[MONGO_DB]
    col_name = os.environ.get('CRAWLAB_COLLECTION')
    if not col_name:
        col_name = 'test'
    col = db[col_name]

    def process_item(self, item, spider):
        item['task_id'] = os.environ.get('CRAWLAB_TASK_ID')
        self.col.save(item)
        return item

Comparison with Other Frameworks

There are existing spider management frameworks. So why use Crawlab?

The reason is that most of the existing platforms are depending on Scrapyd, which limits the choice only within python and scrapy. Surely scrapy is a great web crawl frameowrk, but it cannot do everything.

Crawlab is easy to use, general enough to adapt spiders in any language and any framework. It has also a beautiful frontend interface for users to manage spiders much more easily.

Framework	Type	Distributed	Frontend	Scrapyd-Dependent
Crawlab	Admin Platform	Y	Y	N
Gerapy	Admin Platform	Y	Y	Y
SpiderKeeper	Admin Platform	Y	Y	Y
ScrapydWeb	Admin Platform	Y	Y	Y
Scrapyd	Web Service	Y	N	N/A

Community & Sponsorship

If you feel Crawlab could benefit your daily work or your company, please add the author's Wechat account noting "Crawlab" to enter the discussion group. Or you scan the Alipay QR code below to give us a reward to upgrade our teamwork software or buy a coffee.

duanbj/crawlab