After 6 years and 21 incremental “zero dot” releases, we are excited to announce the first major release of ObjectBox, the high-performance embedded database for C++ and C. As a faster alternative to SQLite, ObjectBox delivers more than just speed – it’s object-oriented, highly efficient, and offers advanced features like data synchronization and vector search. It is the perfect choice for on-device databases, especially in resource-constrained environments or in cases with real-time requirements.
What is ObjectBox?
ObjectBox is a free embedded database designed for object persistence. With “object” referring to instances of C++ structs or classes, it is built for objects from scratch with zero overhead — no SQL or ORM layer is involved, resulting in outstanding object performance.
The ObjectBox C++ database offers advanced features, such as relations and ACID transactions, to ensure data consistency at all times. Store your data privately on-device across a wide range of hardware, from low-profile ARM platforms and mobile devices to high-speed servers. It’s a great fit for edge devices, iOS or Android apps, and server backends. Plus, ObjectBox is multi-platform (any POSIX will do, e.g. iOS, Android, Linux, Windows, or QNX) and multi-language: e.g., on mobile, you can work with Kotlin, Java or Swift objects. This cross-platform compatibility is no coincidence, as ObjectBox Sync will seamlessly synchronize data across devices and platforms.
Why should C and C++ Developers care?
ObjectBox deeply integrates with C and C++. Persisting C or C++ structs is as simple as a single line of code, with no need to interact with unfamiliar database APIs that disrupt the natural flow of C++. There’s also no data transformation (e.g. SQL, rows & columns) required, and interacting with the database feels seamless and intuitive.
As a C or C++ developer, you likely value performance. ObjectBox delivers exceptional speed (at least we haven’t tested against a faster DB yet). Having several 100,000s CRUD operations per second on commodity hardware is no sweat. Our unique advantage is that, if you want to, you can read raw objects from “mmapped” memory (directly from disk!). This offers true “zero copy” data access without any throttling layers between you and the data.
Finally, CMake support makes integration straightforward, starting with FetchContent support so you can easily get the library. But there’s more: we offer code generation for entity structs, which takes only a single CMake command.
“ObjectBox++”: A quick Walk-Through
Once ObjectBox is set up for CMake, the first step is to define the data model using FlatBuffers schema files. FlatBuffers is a building block within ObjectBox and is also widely used in the industry. For those familiar with Protocol Buffers, FlatBuffers are its parser-less (i.e., faster) cousin. Here’s an example of a “Task” entity defined in a file named “task.fbs”:
| 1 2 3 4 | table Task { id: ulong; text: string; } |
And with that file, you can generate code using the following CMake command:
| 1 | add_obx_schema(TARGET ${PROJECT_NAME} SCHEMA_FILES tasks.fbs INSOURCE) |
Among other things, code generation creates a C++ struct for Task data, which is used to interact with the ObjectBox API. The struct is a straightforward C++ representation of the data model:
| 1 2 3 4 | struct Task { obx_id id; // uint64_t std::string text; }; |
The code generation also provides some internal “glue code” including the method create_obx_model() that defines the data model internally. With this, you can open the store and insert a task object in just three lines of code:
| 1 2 3 | obx::Store store(create_obx_model()); // Create the database obx::Box<Task> box(store); // Main API for a type obx_id id = box.put({.text = "Buy milk"}); // Object is persisted |
And that’s all it takes to get a database running in C++. This snippet essentially covers the basics of the getting started guide and this example project on GitHub.
Vector Embeddings for C++ AI Applications
Even if you don’t have an immediate use case, ObjectBox is fully equipped for vectors and AI applications. As a “vector database,” ObjectBox is ready for use in high-dimensional vector similarity searches, employing the HNSW algorithm for highly scalable performance beyond millions of vectors.
Vectors can represent semantics within a context (e.g. objects in a picture) or even documents and paragraphs to “capture” their meaning. This is typically used for RAG (Retrieval-Augmented Generation) applications that interact with LLMs. Basically, RAG allows AI to work with specific data, e.g. documents of a department or company and thus individualizes the created content.
To quickly illustrate vector search, imagine a database of cities including their location as a 2-dimensional vector. To enable nearest neighbor search, all you need to do is to define a HNSW index on the location property, which enables the nearestNeighbors query condition used like this:
| 1 2 3 | float madrid[] {40.416775F, -3.703790F}; obx::Query query = cityBox.query(City_::location.nearestNeighbors(madrid, 2)).build(); std::vector<City> cities = query.findWithScores(); |
For more details, refer to the vector search doc pages or the full city vector search example on GitHub.
store.close(); // Some closing words
This release marks an important milestone for ObjectBox, delivering significant improvements in speed, usability, and features. We’re excited to see how these enhancements will help you create even better, feature-rich applications.
There’s so much to explore! Please follow the links to dive deeper into topics like queries, relations, transactions, and, of course, ObjectBox Sync.
As always, we’re here to listen to your feedback and are committed to continually evolving ObjectBox to meet your needs. Don’t hesitate to reach out to us at any time.
P.S. Are you looking for a new job? We have a vacant C++ position to build the future of ObjectBox with us. We are looking forward to receiving your application! 🙂