Large-Scale + Unstructured Web Crawling

May 20, 2025

Welcome to the final section in our hands-on web crawling tutorial series. Instead of a traditional lesson, we're taking a different approach. For this section, I've built the Housefly Metascraper—a crawler in the ./apps/metascraper directory that demonstrates how to apply everything we've learned in a real-world scenario.

The Metascraper demonstrates how our step-by-step journey—from scraping simple static HTML, navigating JavaScript-rendered content, to interacting with APIs and overcoming crawling defenses—culminates in a tool that can handle the unstructured, diverse, and chaotic web at scale.

We'll explore how to crawl across a wide variety of websites—without knowing in advance what kind of data structures to expect—and introduce AI-assisted parsing, dynamic schema detection, and the techniques needed for scaling to thousands (or millions) of pages without falling apart.

What Does "Unstructured" and "Large-Scale" Really Mean?

In previous sections, we often knew:

  • What sites we were targeting.
  • What data we wanted (e.g., tables, lists, JSON responses).
  • How many pages we needed to visit.

But in large-scale unstructured crawling:

  • The websites vary wildly: some are structured, others are blogs with irregular formatting.
  • Paths and URLs are unpredictable.
  • Schema is inconsistent or non-existent.
  • We want to crawl thousands of pages, potentially across multiple domains.

Think:

  • Research crawlers gathering data across academic websites.
  • AI assistants indexing blogs for topic-specific knowledge.
  • Search engines that must generalize across the entire public internet.

This is the final boss of web crawling.

Part 1: Architecting for Large-Scale Crawling

Let's talk about how to scale up your crawler before we worry about parsing.

Design Patterns

To build a large-scale crawler, your architecture should be:

  • Queue-driven: Use a message queue (like Redis, RabbitMQ, or Kafka) to store pending URLs.
  • Worker-based: Separate crawlers into worker processes that pull from the queue and process jobs independently.
  • Deduplicated: Maintain a fingerprinted index (e.g., SHA1 of URL or HTML content) to avoid processing the same page twice.
  • Resumable: Persist crawl state so it can recover from crashes.

Here's a minimal design diagram:

Part 2: Cutting-Edge Techniques for 2025

Residential Proxies

One of the most significant advances in large-scale scraping is the use of residential proxies. Unlike datacenter IPs which websites can easily detect and block, residential proxies route your requests through real consumer IP addresses, making your scraper appear as a legitimate user.

AI-Powered Autonomous Agents

The most revolutionary advancement in 2025 is agentic scraping. Rather than hard-coding scrapers for each site format:

  • LLMs with vision capabilities can understand and extract data from previously unseen layouts
  • AI agents can autonomously navigate complex sites by mimicking human browsing patterns
  • Adaptive parsing automatically adjusts to layout changes without requiring code updates

Part 3: AI-Assisted Parsing

Fine-tune or prompt-engineer a model to output clean JSON:

{
  "name": "Dr. Maria Lopez",
  "title": "Climate Scientist",
  "organization": "Stanford",
  "topic": "2023 UN Climate Summit, AI in climate modeling"
}

Part 4: Storing, Indexing, and Searching the Data

You'll collect lots of heterogeneous data. Choose your storage based on your goals and how structured the data is.

Storage Strategies

  • PostgreSQL or SQLite: Best for structured tabular data where you know the schema (e.g., articles, prices, timestamps). You can use indexes, foreign keys, and full-text search (FTS).
  • MongoDB or Elasticsearch: Great for semi-structured or flexible data formats like JSON blobs where schema may vary across records.
  • S3 / IPFS / File System: Ideal for raw HTML snapshots, images, PDFs, and large binary files. Store the metadata in a database and link to the file location.

Use UUIDs or URL hashes as primary keys so you can de-duplicate and track previously crawled items.

Making It Searchable

Once stored, you'll want to explore and query the content.

Options include:

  • PostgreSQL FTS (Full-Text Search): Use tsvector and tsquery to build robust keyword search capabilities with ranking.
  • Typesense or Meilisearch: Lightweight, schema-flexible full-text search engines perfect for fast indexing and fuzzy search.
  • Elasticsearch: Best for more complex search use cases or logs, with powerful filtering and analytics.

You should index fields like:

  • Title
  • Author
  • Date published
  • Keywords or tags (if extracted)
  • Main content
  • Domain / source

Semantic Search with Embeddings

For deeper understanding and retrieval (beyond keywords), use text embeddings:

  1. Use a model like OpenAI's text-embedding-3-small or open-source alternatives like bge-small-en.
  2. Convert your crawled content into embedding vectors.
  3. Store them in a vector database like:
    • Qdrant
    • Weaviate
    • Pinecone
    • FAISS (for local / in-memory use)

This enables semantic queries like:

"Show me articles where someone talks about bike-friendly urban development in cold climates."

By comparing the query embedding to stored embeddings, your crawler becomes a knowledge engine.

Metadata & Enrichment

Finally, enrich your data with additional metadata:

  • Language detection (e.g., with langdetect or fastText).
  • Content category classification using zero-shot models or fine-tuned classifiers.
  • Named Entity Recognition (NER) to extract people, organizations, and places.
  • Geotagging based on content or source.

Store this alongside the main data so you can filter and sort by it later.

Part 5: Search-Driven Discovery Crawlers

The most advanced approach to large-scale crawling doesn't even begin with URLs—instead, it starts with search queries.

Inspired by tools like SearchXNG and Perplexity, the Metascraper demonstrates a search-first strategy where the crawler:

  1. Begins with a topic or question rather than a seed URL list
  2. Uses search engine APIs to discover relevant pages in real-time
  3. Dynamically builds its crawl queue based on search results
  4. Intelligently follows citations and references to expand knowledge

This approach offers several advantages:

  • Targeted Exploration: Rather than exhaustive crawling, you only visit pages likely to contain relevant information
  • Up-to-date Results: Each crawl starts fresh with current search results
  • Domain-agnostic: Not limited to pre-defined sites or URL patterns
  • Intention-driven: Aligns with how humans actually research topics

The Metascraper's search-driven mode demonstrates how to combine search APIs, prioritization algorithms, and context-aware extraction to build knowledge graphs from dynamically discovered content without knowing in advance which URLs you'll visit.

Happy scraping