Realtime Database Security¶

This section covers security best practices for Firebase Realtime Database, including the unique challenges of cascading security rules.

Overview¶

Firebase Realtime Database uses a different security model than Firestore, with rules that cascade down the data tree. Understanding this cascading behavior is crucial for proper security implementation.

Common Security Issues¶

Critical Access Control Issues¶

• Publicly Accessible Database - Universal read/write access
• Overly Broad Auth User Access - All authenticated users have access

Rule Inheritance Problems¶

• Ineffective Security Rule Due to Cascading - Child rules overridden by parent rules
• Improper Rule Inheritance - Misunderstanding cascading behavior

Validation and Performance¶

• Write Lacks Data Validation - No server-side validation
• Inefficient Value Index - Poor query performance

Key Differences from Firestore¶

Rule Cascading¶

• Realtime Database: Permissive parent rules override restrictive child rules
• Firestore: Each document/collection has independent rules

Data Structure¶

• Realtime Database: Single large JSON tree
• Firestore: Collection/document hierarchy

Querying¶

• Realtime Database: Limited querying capabilities
• Firestore: Rich querying with indexes

Best Practices¶

Rule Design¶

1. Understand cascading behavior - Permissions flow down the tree
2. Design data structure with security in mind
3. Use specific paths rather than broad wildcards
4. Implement validation rules for all write operations

Access Control¶

1. Start with restrictive rules at the root level
2. Grant permissions at the most specific level possible
3. Validate user ownership in path structure
4. Use authentication context for access control

Performance Optimization¶

1. Avoid deep nesting when possible
2. Use appropriate indexing for queries
3. Denormalize data for better performance
4. Monitor database usage and costs

Security Rule Examples¶

{
  "rules": {
    // Users can only access their own data
    "users": {
      "$uid": {
        ".read": "auth != null && auth.uid === $uid",
        ".write": "auth != null && auth.uid === $uid",
        ".validate": "newData.hasChildren(['name', 'email'])"
      }
    },

    // Public read-only data
    "public": {
      ".read": true,
      ".write": "auth != null && auth.token.admin === true"
    },

    // Shared data with proper validation
    "shared": {
      "$itemId": {
        ".read": "auth != null",
        ".write": "auth != null && (
          !data.exists() || 
          data.child('owner').val() === auth.uid
        )",
        ".validate": "newData.hasChildren(['title', 'owner']) && 
                     newData.child('owner').val() === auth.uid"
      }
    }
  }
}

Common Pitfalls¶

1. Cascading override - Child restrictive rules ignored if parent allows
2. Wildcard overuse - Overly broad path matching
3. Missing validation - No server-side data validation
4. Deep nesting - Performance and security issues
5. Index misuse - Inefficient query patterns