MongoDB

How to use MongoDB in a web scraping pipeline

MongoDB stores records as BSON documents, including nested fields and arrays. It can hold extracted page entities whose structure varies across source sites while preserving a consistent envelope of provenance fields.

A practical workflow

1. A separate HTTP or browser scraper emits a record with an entity key, source URL, capture time and extracted attributes.
2. Your loader validates the shared fields and writes the document using an explicit policy for updating current state or retaining observations.
3. Application queries read those documents, while references to raw captures support debugging when a source layout changes.

When it fits

Use it when collected entities naturally contain nested values, such as product variants or article metadata. A document model can retain that shape without making every source-specific field a separate relational table.

Boundaries

Flexible document structure does not remove the need for validation, indexes or identity rules. MongoDB does not fetch or render pages. Keep oversized raw artifacts outside the record model, and distinguish missing extraction fields from legitimate empty values before overwriting a previously useful document.