Mastering Depth-First Search for Python Web Crawlers: A Step‑by‑Step Guide

After announcing the winners of a recent book giveaway, the article returns to its main topic: a concise introduction to the depth‑first search (DFS) algorithm used in web crawling.

Websites are typically organized hierarchically: a top‑level domain, followed by sub‑domains, and further nested sub‑domains, each potentially containing multiple sibling domains and inter‑linked URLs, forming a complex network.

When a site has a large number of URLs, careful design of the URL structure is essential to avoid confusion during maintenance or development.

DFS explores this structure by treating the site map as a binary tree. Starting from the root node A , it follows links to child nodes B and C . After fully processing B , the crawler proceeds to its children D and E before backtracking to C , and so on, ultimately visiting nodes in the order: A, B, D, E, I, C, F, G, H (assuming left‑hand links are visited first).

The popular Scrapy framework implements this DFS strategy by default, and the algorithm can be expressed recursively.

The following image illustrates a typical DFS code implementation:

The recursive function prints the current node (e.g., A ), then recursively visits its left child ( B ) and right child ( C ), continuing this process until all nodes are visited or a stopping condition is met. The right‑hand side of the tree follows the same logic.

Because recursion can become deep, developers should be aware of the risk of stack overflow and consider iterative alternatives for very large crawls.

DFS and breadth‑first search (BFS) are fundamental algorithms frequently asked about in technical interviews; the next article will cover BFS.