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Category: open-source-2

Aduana: Link Analysis with Frontera

It's not uncommon to need to crawl a large number of unfamiliar websites when gathering content. Page ranking algorithms are incredibly useful in these scenarios as it can be tricky to determine which pages are relevant to the content you're looking for.

Frontera: The Brain Behind the Crawls

At Scrapinghub we're always building and running large crawls–last year we had 11 billion requests made on Scrapy Cloud alone. Crawling millions of pages from the internet requires more sophistication than getting a few contacts of a list, as we need to make sure that we get reliable data, up to date lists of item pages and are able to optimise our crawl as much as possible.

The History of Scrapinghub

Joanne O’Flynn meets with Pablo Hoffman and Shane Evans to find out what inspired them to set up web crawling company Scrapinghub.

Skinfer: A Tool for Inferring JSON Schemas

Imagine that you have a lot of samples for a certain kind of data in JSON format. Maybe you want to have a better feel of it, know which fields appear in all records, which appear only in some and what are their types. In other words, you want to know the schema for the data that you have.

Handling JavaScript in Scrapy with Splash

A common roadblock when developing spiders is dealing with sites that use a heavy amount of JavaScript. Many modern websites run entirely on JavaScript, and require scripts to be run in order for the page to render properly. In many cases, pages also present modals and other dialogues that need to be interacted with to show the full page. In this post we’re going to show you how you can use...

Introducing ScrapyRT: An API for Scrapy spiders

We’re proud to announce our new open source project, ScrapyRT! ScrapyRT, short for Scrapy Real Time, allows you to extract data from a single web page via an API using your existing Scrapy spiders.

Optimizing Memory Usage of Scikit-Learn Models Using Succinct Tries

We use the scikit-learn library for various machine-learning tasks at Scrapinghub. For example, for text classification we'd typically build a statistical model using sklearn's Pipeline, FeatureUnion, some classifier (e.g. LinearSVC) + feature extraction and preprocessing classes. The model is usually trained on a developers machine, then serialized (using pickle/joblib) and uploaded to a server...

How to Fill Login Forms Automatically

We often have to write spiders that need to login to sites, in order to scrape data from them. Our customers provide us with the site, username and password, and we do the rest.

Introducing w3lib and scrapely

In an effort to make Scrapy code smaller and more reusable, we’ve been working on splitting the Scrapy codebase into two different modules:

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