Version 3.0 introduces C-API and Kotlin support, as well as full support for SQL++
Offline first
Couchbase Lite ensures that apps are always available regardless of network connections. Data is stored and processed when offline, and synchronized with backend data centers when connectivity is available.
Introduction to Couchbase Lite
Developer friendly
Develop faster with full SQL support, simple-to-use APIs for database CRUD, full-text search (FTS) and query operations, and comprehensive platform and language support. Schema-less JSON format simplifies app upgrades without requiring tedious data migration.
Learn more about APIsLightweight
Couchbase Lite has a small footprint, ranging from 1MB – 5MB depending on the platform. It’s well-suited for resource-constrained mobile and embedded devices.
Enter the world of embedded apps
Multiplatform support
Support for mobile platforms including iOS, Android, and Windows. Develop desktop and web server apps with our .NET and Java libraries. C-API support allows developers to build applications in C that run on nearly any embedded platform.
Learn more about multiplatform supportHighlighted features
SQL query
Developers leverage familiar SQL syntax, and can reuse the same queries across platforms and Couchbase Server and Couchbase Lite, reducing the effort required to develop, test, and optimize queries.
Learn moreFull-text search
There's no need to integrate search technology to your apps, full-text search is built-in to Couchbase Lite, making it easy to build compelling user experiences and smarter, richer mobile and edge applications.
Learn morePeer-to-peer sync
Synchronize data directly between Couchbase Lite devices using peer-to-peer sync without the need for a centralized control point.
Learn moreOn-device failover
Synchronize data directly between databases on the same device to enable on-device failover and backup.
Learn moreOn-device encryption
Data is secured on the device using 256-bit AES full database encryption.
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Predictive queries
Use the Predictive Query API to leverage machine learning models within your apps. Enable real-time predictions by analyzing data on-device, at the point of interaction.
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