Mastering Breadth-First Search for Web Crawling: Concepts and Code

Previously we introduced the depth‑first search (DFS) algorithm for web crawling and its code implementation; now we turn to the breadth‑first search (BFS) algorithm and its Python implementation.

BFS works opposite to DFS: starting from the top‑level domain A, it extracts links B and C, processes all nodes at the current depth before moving to the next level, and uses a queue to manage the order of traversal.

Visually, BFS captures nodes layer by layer: first all nodes at depth 1, then depth 2, and so on, until the crawl finishes or a stopping condition is met. The traversal order for the example binary tree is A, B, C, D, E, F, G, H, I (assuming left links are visited first). This behavior is naturally implemented with a queue.

The following diagram shows the BFS code implementation.

The algorithm starts by enqueuing the root node (link A). While the queue is not empty, it dequeues a node, processes it, and enqueues its left and right children (links B and C) if they exist. This process repeats, ensuring a level‑order crawl that is simpler than the recursive DFS approach.

Both depth‑first and breadth‑first searches are fundamental algorithms in data structures and are frequently asked in technical interviews, so mastering them is highly recommended.