Breadth-First Crawling vs. Depth-First Crawling
TL;DR
Breadth-first and depth-first crawling are two fundamental strategies for navigating websites. Breadth-first explores all links on the current level before going deeper, while depth-first follows a single link chain as far as it goes before backtracking. Each approach has trade-offs for coverage, speed, and resource usage.
Two Ways to Explore a Website
When a web crawler lands on a page and finds dozens of links, it faces a fundamental decision: should it visit all the links on this page first, or follow one link deep into the site before coming back to try the others?
This choice defines the two classic crawl strategies:
- Breadth-first crawling (BFS) — visit all links at the current depth level before moving to the next level.
- Depth-first crawling (DFS) — follow a single path of links as deep as possible, then backtrack and try the next path.
How Breadth-First Crawling Works
Imagine you're on a homepage with 20 links. A breadth-first crawler visits all 20 linked pages first (level 1). Then it collects every link found on those 20 pages and visits all of those (level 2). The process continues level by level.
Advantages:
- Discovers the most important pages quickly — pages closer to the homepage tend to be higher-value
- Provides broad coverage early in the crawl
- Naturally prioritizes well-linked, authoritative content
- Works well for building search indexes where freshness and importance matter
Disadvantages:
- Uses more memory, since it has to track all URLs at each level before proceeding
- Can be slower to reach deep, niche content buried many levels down
How Depth-First Crawling Works
A depth-first crawler picks one link from the homepage and follows it. On the next page, it picks another link and follows that. It keeps going deeper until it hits a dead end (no new links), then backtracks to the last page with unexplored links and tries a different path.
Advantages:
- Uses less memory — it only needs to track the current path, not an entire level
- Reaches deep content faster if that's where your target data lives
- Simpler to implement for basic crawling tasks
Disadvantages:
- Can get trapped in deep, low-value sections of a site (infinite pagination, calendar archives, etc.)
- May miss important pages near the surface if it dives too deep too quickly
- Risk of unbalanced coverage — one section gets fully crawled while others are barely touched
When to Use Which Strategy
| Scenario | Best Strategy | Why |
|---|---|---|
| Building a search index | Breadth-first | Surfaces the most important, well-linked pages first |
| Scraping a specific section | Depth-first | Drills into the target area without wasting time on unrelated content |
| Full-site archiving | Breadth-first | Ensures even coverage across all site sections |
| Crawling paginated listings | Depth-first | Follows "next page" links sequentially through the entire list |
| General-purpose data collection | Hybrid | Combines breadth-first for discovery with depth limits to avoid rabbit holes |
The Hybrid Approach
In practice, most production crawlers — including search engines — use a hybrid strategy. They start breadth-first to discover the site's structure, then apply heuristics to decide which paths to explore more deeply. Common signals include:
- Link popularity — pages linked from many places get priority
- Content freshness — recently updated pages are crawled sooner
- URL patterns — known high-value patterns (e.g.,
/products/) get deeper crawling - Crawl budget constraints — the crawler switches strategies based on remaining budget
How This Relates to Web Crawling APIs
When using a web crawling API, you typically don't need to implement BFS or DFS yourself. The API manages the crawl frontier and traversal strategy internally. What you do control is:
- Crawl depth — how many levels deep the crawler should go from the seed URL
- URL filters — patterns to include or exclude, which indirectly shapes the traversal
- Page limits — total pages to crawl before stopping
With Olostep, you set these parameters via the API and the crawler optimizes its traversal strategy to maximize coverage within your constraints — so you get the benefits of intelligent crawling without managing the graph-traversal logic yourself.
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Start using the Olostep API to implement breadth-first crawling vs. depth-first crawling in your application.