Data Browsing Guide
Core FeatureMaster the art of searching, filtering, and exploring your Firestore documents with advanced browsing capabilities.
• Advanced search techniques
• Filtering and sorting options
• Pagination strategies
• Data type formatting
• Performance optimization
• Best practices for large datasets
Getting Started with Data Browsing
Access the Data Browser
There are two ways to access the data browsing interface:
• From Schema View: Click "View Records" button next to any collection
• Direct Access: Navigate to /projects/[projectId]/collections
The data browser includes a "Back to Schema" button for easy navigation between schema analysis and data exploration.
Understanding the Interface
The data browsing interface consists of several key components:
• Field selector dropdown
• Search input with fuzzy matching
• Clear/reset button
• Sort field and direction controls
• Page size selector (10-100 records)
• Load more / pagination buttons
Browse Your Data
Documents are displayed as individual cards showing:
• Document ID: Unique identifier in monospace font
• Document Path: Full Firestore path to the document
• Field Data: All document fields with proper type formatting
Document ID: user_12345
Path: /users/user_12345
Fields:
• email: "john.doe@example.com" (string)
• firstName: "John" (string)
• lastName: "Doe" (string)
• createdAt: "2024-01-15T10:30:00Z" (timestamp)
• isActive: true (boolean)
• profile: { avatar: "https://...", bio: "..." } (object)Search Features
How it works:
Search finds partial matches within field values, case-insensitive.
Examples:
• Search "john" finds "John Doe", "Johnson", "johnny@email.com"
• Search "gmail" finds all Gmail email addresses
• Search "2024" finds dates and IDs containing "2024"
Available fields:
Dropdown populated with all fields found in the collection.
Field types supported:
• String fields (names, emails, descriptions)
• Number fields (IDs, ages, prices)
• All searchable Firestore types
Single-field search only: You can search one field at a time, not multiple fields simultaneously. This is different from full-text search engines.
Pagination impact: Search results may break normal pagination flow, as filtering happens client-side after fetching from Firestore.
Sorting & Pagination
How it works:
Uses Firestore's orderBy() for database-level sorting, ensuring efficient performance and proper pagination.
Supported directions:
• Ascending: A → Z, 0 → 9, oldest → newest
• Descending: Z → A, 9 → 0, newest → oldest
Data type handling:
• Strings: Alphabetical order
• Numbers: Numerical order
• Timestamps: Chronological order
• Booleans: false before true
How it works:
Uses Firestore's startAfter() with document cursors for efficient pagination through large datasets.
Page size options:
• 10 records (fastest loading)
• 25 records (balanced)
• 50 records (more data per page)
• 100 records (maximum, slower on mobile)
Performance benefits:
• Consistent performance regardless of collection size
• Efficient memory usage
• Real-time data consistency
Firestore requires indexes for sorting operations. If you see an error about missing indexes, Firestore will provide a link to automatically create the required composite index.
Common scenario: Sorting by a field while also using .where()clauses requires a composite index.
Data Type Formatting
Firebase Schema Viewer automatically formats different Firestore data types for optimal readability:
profile: {
personal: {
firstName: "John",
lastName: "Doe",
dateOfBirth: "1990-05-15T00:00:00Z"
},
preferences: {
theme: "dark",
notifications: true,
language: "en"
},
social: {
twitter: "@johndoe",
linkedin: "linkedin.com/in/johndoe"
}
}Performance Tips
• Page Size: Start with 25 records, increase if needed
• Sorting: Use indexed fields for faster sorting
• Search: Be specific with field selection
• Navigation: Use "Load More" rather than large page sizes
• Filter Early: Use search to narrow results
• Sort Strategically: Choose meaningful sort orders
• Monitor Performance: Watch for slow loading indicators
• Index Management: Create composite indexes as needed
Common Use Cases
Quickly find and examine problematic documents in your database.
• Search for specific user IDs or email addresses
• Filter by date ranges to find recent changes
• Look for null or missing values in required fields
• Examine data type inconsistencies across documents
Explore patterns and trends in your application data.
• Sort by creation date to see usage patterns
• Search for feature flags or configuration values
• Examine user behavior through activity logs
• Validate data migrations and transformations
Quickly look up customer information and resolve support issues.
• Search by customer email or ID
• Review order history and transaction details
• Check account status and preferences
• Verify data consistency across related collections
Troubleshooting Common Issues
Possible causes:
- Search term doesn't exist in the selected field
- Case sensitivity (though search is case-insensitive)
- Selected field doesn't contain text data
Solutions: Try different fields, check spelling, or browse without search first.
Cause: Firestore needs a composite index for the sort operation.
Solution: Click the provided link in the error message to create the index automatically.
Causes: Large documents, high page size, or complex queries.
Solutions: Reduce page size, sort by indexed fields, or use more specific searches.
• Practice searching with different field types
• Try various page sizes and sort orders
• Explore complex nested objects
• Test performance with different collection sizes
Now that you've mastered data browsing, explore advanced features:
AI Relationship Discovery
Use AI to discover hidden connections between your collections.
Explore AI Features →