> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dotportion.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Schema Canvas
> Design and manage database schemas visually for Platform Database or MongoDB using the Schema Canvas.
# Schema Canvas
The **Schema Canvas** allows you to **design database schemas visually** in DotPortion—without writing database migration code.
It provides a **GUI-based schema builder**, similar to tools like MongoDB Compass or ORM schema designers, but tightly integrated with workflows and database operations.
***
## What Is the Schema Canvas?
The Schema Canvas is a visual editor where you can:
* Select a database (Platform DB or MongoDB)
* Create and manage collections
* Define fields and their constraints
* Maintain a clear data structure for your project
Schemas created here act as the **single source of truth** for your data model.
***
## Supported Database Types
When opening the Schema Canvas, you can choose between:
### Platform Database
* Managed by DotPortion
* No external credentials required
* Best for quick setup and internal tooling
### MongoDB Database
* Connects via MongoDB URL secret
* Works with your existing MongoDB clusters
* Full compatibility with MongoDB collections
Both options provide the **same schema design experience**.
***
## Schema Canvas UI
> 🧩 **Schema Canvas (UI Reference)**
The canvas displays:
* Selected database
* List of collections
* Fields inside each collection
* Field-level configuration options
***
## Creating a Collection
To create a new collection:
1. Select the database
2. Click **Add Collection**
3. Provide a collection name
4. Start defining fields
Each collection represents a logical data entity (e.g. `users`, `orders`, `products`).
***
## Adding Fields
> 🧱 **Field Configuration Panel (UI Reference)**
Each field in a collection can be configured with the following options:
***
## Field Options Explained
### Field Name
**Purpose:**\
The key name used to store data in the document.
**Example:**
```text theme={null}
email
```
Field names should be:
* Lowercase
* Descriptive
* Consistent across collections
***
### Type
**Purpose:**\
Defines the data type of the field.
**Common types include:**
* String
* Number
* Boolean
* Date
* Object
* Array
The selected type helps ensure data consistency.
***
### Required
**Purpose:**\
Marks the field as mandatory.
* Enabled → field must be present
* Disabled → field is optional
This helps prevent incomplete records.
***
### Unique
**Purpose:**\
Ensures the field value is unique across the collection.
**Common use cases:**
* Email
* Username
* External IDs
This creates a uniqueness constraint at the database level.
***
### Index
**Purpose:**\
Creates an index on the field for faster queries.
Indexes improve performance for:
* Frequent lookups
* Filtering
* Sorting
⚠️ Use indexes thoughtfully to avoid unnecessary overhead.
***
### Default Value
**Purpose:**\
Sets a default value when no value is provided.
**Examples:**
```
true
```
```
CURRENT_TIMESTAMP
```
This is useful for flags, timestamps, and status fields.
***
### Description
**Purpose:**\
Adds documentation for the field.
Descriptions help:
* Team members understand data intent
* Future maintenance
* Auto-generated docs
Descriptions do not affect runtime behavior.
***
## Visual Schema Editing Benefits
Using the Schema Canvas:
* Reduces schema errors
* Improves team collaboration
* Makes data models self-documenting
* Aligns workflows with database structure
***
## Relationship with Workflows
* Database Nodes respect schema definitions
* Field constraints guide workflow data shape
* Schema updates reflect immediately in DB operations
* Helps catch invalid data early
***
## Best Practices
* Design schemas before building workflows
* Use `required` and `unique` constraints wisely
* Add descriptions for clarity
* Index fields used in queries
* Keep schemas simple and intentional
***
## Important Notes
* Schema changes affect live data
* Existing documents are not auto-migrated
* Deleting fields does not delete data automatically
* Always test schema changes in non-production environments
***
## Summary
| Feature | Supported |
| :------------------- | :-------- |
| Visual schema design | Yes |
| Platform Database | Yes |
| MongoDB Database | Yes |
| Field constraints | Yes |
| Indexing | Yes |
| Documentation | Yes |
The Schema Canvas helps you **design reliable data models visually**, keeping your database, workflows, and APIs aligned.