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estela Documentation


estela is an open-source application created by Bitmaker. Clients and users interested in web scraping can orchestrate and run their spiders (e.g., Scrapy spiders), similar to Zyte’s Scrapy Cloud. estela aims to be versatile, to the point that you can deploy it in-home.

The estela CLI is in charge of Scrapy based projects deployment (currently only supports Scrapy projects) and uses a REST API client to upload projects. These projects are then built into docker images and added to a Docker Registry.

The estela API endpoint creates a Kubernetes job to complete every spider job (it could also be a spider cronjob). Currently, estela only works with Kubernetes. However, you can add your engine to make it work with Docker or other engines. Jobs send spider jobs data to a queue platform, currently Kafka, through extensions and middlewares implemented in the entry point. The queue consumers read the information and send data to a data storage (e.g., MongoDB).


estela Architecture


  • estela: It is composed of modules that work independently of the rest, and can be changed. E.g., use RabbitMQ instead of Kafka.
    • REST API: Built with the Django REST framework toolkit, which exposes several endpoints to manage projects, spiders, and jobs. It uses Celery for task processing and takes care of deploying your Scrapy projects, among other things.
    • Queueing: estela needs a high-throughput, low-latency platform that controls real-time data feeds in a producer-consumer architecture. In this module, you will find a consumer used to collect and transport the information from the spiders into a database.
    • Web: A front-end project implemented with React Framework (with Ant Design) and Typescript. This module implements a user-friendly environment that communicates with the API and lets you manage your spiders and scraping projects.
  • estela CLI: This is the command line client to interact with estela API.
  • estela Entrypoint: This is a package that implements a wrapper layer to extract job data from the environment, prepare the job properly, and execute it using Scrapy.

Get Started

From here, we recommend reading through the following docs: