> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lambdadb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Collections and data model

> Understand the LambdaDB data model where projects contain collections of flexible documents, similar to tables and rows in a relational database.

A LambdaDB collection is a logical namespace that holds a set of documents, where each document is a set of fields — which, in turn, are key-value pairs that contain your data.

## Database structure comparison

LambdaDB's structure differs from traditional relational databases. Here's how they compare:

| RDBMS      | LambdaDB                      |
| :--------- | :---------------------------- |
| Database   | Project                       |
| Table      | Collection                    |
| Column/row | Document with key-value pairs |

Think of a LambdaDB project as a database that can contain many collections (similar to tables), and within each collection, you have many documents containing your actual data as flexible key-value pairs.

<Note>
  Indexes in LambdaDB are not the same as you'd find in a relational database. The whole document is stored as is regardless of its existence in index configurations, but being stored does not necessarily mean it is searchable.
</Note>

## API interaction

LambdaDB provides a RESTful JSON-based API for interacting with document data. You can perform the following operations by sending HTTP requests to the appropriate endpoints:

* **Upsert** documents into collections
* **Search** across documents using various query types
* **Delete** individual documents or entire collections
* **Update** document data and collection configurations

These CRUD-like operations can take place at both individual document level and collection level, giving you flexibility in how you manage your data.

## Zero-copy branching

Zero-copy collection fork is a powerful feature that allows you to create a new collection by referencing an existing collection's data, without physically copying the underlying data.

This feature is useful when you want to create a new collection with the same configurations and data as an existing collection,
in order to load-test, develop, and experiment in an isolated environment.

### Key benefits

* **Instant creation**: Collections are created immediately without waiting for data transfer.
* **Storage efficiency**: No additional storage space required for the forked collection.
* **Isolated environment**: Perfect for load testing, development, and experimentation.
* **Same configurations**: Inherits all index configurations and data from the source collection.

### Use cases

This feature is particularly useful when you want to:

* Create isolated environments for testing without affecting production data.
* Set up staging environments that mirror production collections.
* Experiment with different query patterns or configurations.
* Perform load testing with real data volumes.
* Create backup references for disaster recovery scenarios.

<Info>
  Changes made to documents in a forked collection do not affect the original collection, ensuring complete isolation while maintaining efficiency.
</Info>