Add the import and annotation to each entity you want to sync:
import io.objectbox.annotation.Sync; // Add this import
@Sync // Add this annotation
@Entity
public class YourEntity {
// Your entity fields (no relationships to non-synced entities)
}
Important: Never close ObjectBox store while sync is active (generally, there is rarely ever need to close the store, so if you feel you need to, be very careful with this)
Only sync entities that don't have relationships to non-synced entities
Vector embeddings are not yet syncable (reach out to us if you need this!)
Keep the store open throughout application lifecycle
To test, run app with different database paths and add data in one instance, verify it syncs to the other
We’re excited to announce the latest updates to ObjectBox Sync Server with our recent 2025-06-02 and 2025-05-27 releases. These updates bring significant improvements to data handling, authentication, and user interface, making your data synchronization experience even smoother.
Powering Up Your Data Flow
Exciting news for developers! Starting from late May 2025, ObjectBox Sync Server trials are publicly available as Docker images on Docker Hub. This means you can now effortlessly pull our Sync Server trial directly with a simple command:
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docker pull objectboxio/sync-server-trial
This provides a straightforward, no-fuss way to start testing the Sync Server with your data. Each trial gives you 30 days per dataset to explore the full spectrum of ObjectBox Sync capabilities, allowing you to experience its power and ease of use firsthand.
New “JSON to Native” External Property Type
Managing complex, nested JSON structures and mapping them to native database objects can be cumbersome and lead to data integrity issues. One of the most powerful additions in the 2025-05-27 release is the new “JSON to native” property type mapping. This feature allows you to convert strings to nested documents in MongoDB, providing a more elegant way to handle complex data structures. Note: This feature requires client version 4.3 or newer to function correctly.
Here’s how you can implement it in your applications:
You can use your preferred JSON API to access the data
It supports nested documents and arrays
The order of keys is preserved, unlike with flex properties
Increased Maximum Sync Message Size
We’ve increased the maximum Sync message size to 32 MB, allowing for larger data transfers between clients and the server. This improvement is particularly useful for applications that need to synchronize larger chunks of data or complex documents. Clients version 4.3.0 and above are required.
Enhanced JWT Authentication
JWT authentication has been improved with more flexible options for public key configurations. Public key URLs can now refer directly to PEM public key or X509 certificate files, in addition to the previously supported JSON formats.
This means you can now use the following formats for your public key URL:
Key-value JSON file
JWKS (JSON Web Key Set)
PEM public keyfile
PEM certificate file
This enhancement provides more flexibility when integrating ObjectBox Sync Server with various authentication providers and existing security infrastructures..
Admin UI Improvements
The 2025-06-02 release includes several user experience improvements to the Admin UI:
Resolved issues on the GraphQL page for more reliable interactions
Enhanced menu UI with improved icons and optimized padding for better visual clarity and navigation
Getting Started with the ObjectBox Sync Server Trial (including the MongoDB Connector)
If you haven’t tried ObjectBox Sync Server yet, now is a great time to start! With our publicly available Docker images, you can quickly set up and start testing (just ensure Docker is installed on your system):
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docker run--rm-it\
--volume"$(pwd):/data"\
--publish127.0.0.1:9999:9999\
--publish127.0.0.1:9980:9980\
--user$UID\
objectboxio/sync-server-trial\
--model/data/objectbox-model.json\
--unsecured-no-authentication\
--admin-bind0.0.0.0:9980
Note: this assumes you already have an existing data model (objectbox-model.json) ready. If you don’t, you can use the existing ObjectBox Sync Examples repository for a quick start.
Then, access the Admin UI by opening your web browser and navigate to http://127.0.0.1:9980
Follow the on-screen instructions in the Admin UI to activate your 30-day trial per dataset.
We’re continuously working to improve ObjectBox Sync to make your data synchronization experience seamless and robust. Stay tuned for more updates and improvements in the coming months!
For detailed information about these features, please refer to our documentation:
Edge AI refers to decentralized artificial intelligence systems that process data locally on in-store devices, e.g. POS terminals, smart shelves, Raspberry Pis, mobile phones, or cameras, rather than relying on distant cloud servers. This architecture works independently from distant cloud servers or internet connectivity, and therefore offline with minimized latency. Both, offline-capability and speed, are critical for applications like fraud detection and checkout automation. Accordingly, IDC emphasizes that 45% of retailers now prioritize “near-the-network” edge deployments. There, AI models run locally on in-store servers or IoT devices, balancing cost and performance.
Key Components of Edge AI Systems
For Edge AI to deliver real-time, offline-capable intelligence, its architecture must integrate on-device databases, local processing, and efficient data synchronization. These three pillars ensure seamless AI-powered retail operations without dependence on the cloud, minimizing latency, costs, and privacy concerns.
Edge AI system architecture in retail, integrating local processing, real-time data sync, and various applications like POS or signage
Retail generates vast real-time data from IoT sensors, POS transactions, smart cameras, and RFID tags. To ensure instant processing and uninterrupted availability you need:
On-device data storage: All kinds of devices from IoT sensors to cameras capture data. Depending on the device capabilities, with small on-device databases, data can be stored and used directly on the devices.
Local central server: A centralized on-premise device (e.g. a PC or Raspberry Pi, or more capable hw) ensures operations continue even if individual devices are resource-limited or offline.
Bi-directional on-premise data sync: Local syncing between devices and with a central on-site server ensures better decisions and fail-safe operations. It keeps all devices up-to-date without internet dependence.
2. Local Data Processing & Real-Time AI Decision-Making
Processing data where it is generated is critical for speed, privacy, and resilience:
On-device AI models: Small, quantized AI models (SLMs) like Microsoft’s Phi-3-mini (3.8B parameters, <2GB memory footprint) can run directly on many devices (e.g. tablets, and POS systems), enabling real-time fraud detection, checkout automation, and personalized recommendations.
Local on-premise AI models: Larger SLMs or LLMs run on the more capable in-store hardware for security, demand forecasting, or store optimization.
On-device & on-premise vector databases: AI models leverage on-device vector databases to structure and index data for real-time AI-driven insights (e.g., fraud detection, smart inventory management), fast similarity searches, and real-time decision-making.
Selective Cloud Sync: Bi-directional cloud data sync extends the on-premise data sync. Select data, such as aggregated insights (e.g., sales trends, shrinkage patterns), payment processing, and select learnings are synced with the cloud to enable Enterprise-wide analytics & compliance, Remote monitoring & additional backup, and Optimized centralized decision-making.
Cloud Database & Backend Infrastructure: A cloud-based database acts as the global repository. It integrates data from multiple locations to store aggregated insights & long-term trends for AI model refinement and enterprise reporting, facilitating cross-location comparisons.
Centralized cloud AI model: A centralized cloud AI model is optional for larger setups. It can be used to continuously learn from local insights, refining AI recommendations and operational efficiencies across all connected stores.
Use Cases of Edge AI for Retailers
Edge AI is unlocking new efficiencies for retailers by enabling real-time, offline-capable intelligence across customer engagement, marketing, in-store operations, and supply chains.
Key applications of Edge AI in retail, driving personalization, operational efficiency, and smarter decision-making.
Enhancing Customer Experiences in Retail Stores with Edge AI – Examples
Edge AI transforms the shopping experience, enabling retailers to offer more streamlined and more personalized services based on real-time data, thereby boosting customer satisfaction and sales. Key benefits include:
Contactless Checkout: AI-driven self-checkouts allow customers to select products captured by cameras. Thus, bypassing the need for scanning product codes, which streamlines both standard and automated checkout processes. For example, Amazon’s Just Walk Out technology allows customers to pick up items and leave the store without traditional checkout, enhancing convenience and reducing wait times.
Retail operational excellence and cost optimization with Edge AI – Examples
Edge AI also significantly enhances operational efficiency, especially operational in-store efficiency, reduces losses, and helps lower costs (while at the same time enhancing sustainability):
Loss Prevention: Retail shrink, exacerbated by inflation-driven shoplifting and self-checkout vulnerabilities, costs the industry over $100 billion annually. Advanced sensors and intelligent cameras combined with Edge AI help detect early signs of theft or fraud. Thus, allowing security measures to intervene promptly, and independently from an internet connection.
Energy Savings: Smart sensors and Edge AI can be used to optimize the use of energy for lighting, heating, ventilation, water use, etc. For example, 45 Broadway, a 32-story office building in Manhattan, implemented an AI system that analyzes real-time data. That included temperature, humidity, sun angle, and occupancy patterns – to proactively adjust HVAC settings. This integration led to a 15.8% reduction in HVAC-related energy consumption. Plus, saving over $42,000 annually and reducing carbon emissions by 37 metric tons in just 11 months.
Conclusion: Edge AI as Retail’s Strategic Imperative
Yet, Edge AI isn’t just about running AI models locally. It’s about creating an autonomous, resilient system where on-device vector databases, local processing, and hybrid data sync work together. This combination enables real-time retail intelligence while keeping costs low, data private, and operations uninterrupted. To stay ahead, businesses should invest in edge-ready infrastructure with on-device vector databases and data sync that works on-premise at their core. Those who hesitate risk losing ground to nimble competitors who have already tapped into real-time, in-store intelligence.
Hybrid systems, combining lightning-fast offline-first edge response times with the power of the cloud, are becoming the norm. IDC projects that 78% of retailers will adopt these setups by 2026, saving an average of $3.6 million per store annually. In an inflation-driven market, Edge AI isn’t just a perk – it’s a critical strategy for thriving in the future of retail. By leveraging Edge AI-powered on-device databases, retailers gain the speed, efficiency, and reliability needed to stay competitive in an AI-driven retail landscape.
Ever waited to order or pay with a waiter holding their ordering device in the air for a signal? These moments show why offline-first Data Sync is essential. With more and more services relying on the availability of on-device apps and the IoT market projected to hit $1.1 trillion by 2026, choosing the right solution – particularly online-only or offline-first data sync – is more crucial than ever. In this blog, we discuss their differences and highlight common Data Sync alternatives.
What is Data Sync?
Data synchronization (Sync) aligns data between two or more devices to maintain consistency over time. It is an essential component in applications ranging from IoT and mobile apps to cloud computing. Challenges in data synchronization include asynchrony, conflicts, and managing data across flaky networks.
Data Sync vs. Data Replication
Data Synchronization is often confused with Data Replication. Nevertheless, they serve different purposes:
Data Replication: A unidirectional process (works in one direction only) that duplicates data across storage locations to ensure availability and prevent loss. It is simple but limited in its application, and efficiency, and lacks conflict management.
Data Synchronization: A bidirectional process that harmonizes all or a subset of data between two or more devices. It ensures consistency across devices and entails conflict resolution. It is inherently more complex but also more flexible.
Online vs Offline Solutions: Why Offline Sync Matters
Online-only synchronization solutions rely entirely on cloud infrastructure, requiring a stable internet connection to function. While these tools offer simplicity and scalability, their dependency on constant cloud connectivity brings limitations: Online Data Sync solutions cannot guarantee response rates and their speed varies depending on the network. They do not work when offline or in on-premise settings. Using an Online Sync solution often entails sharing the data and might not comply with data privacy requirements. So, do read the terms and conditions.
Offline-first solutions (offline Sync) focus on local data storage and processing, ensuring the app remains fully functional even without an internet connection. When a network is available, the app synchronizes seamlessly with a server, the cloud, or other devices as needed. These solutions are ideal for on-premise scenarios with unreliable or no internet access, mission-critical applications that must always operate, real-time and high-performance use cases, as well as situations requiring high data privacy and data security compliance.
A less discussed, but in our view also relevant point, is sustainability. While there might be exceptions depending on the use case, for most applications offline-first solutions are more resourceful and therefore more sustainable. If CO2 footprint or battery usage is of concern to you, you might want to look into offline-first Data Sync alternatives.
The CE Sync is a bare minimum and typically not usable;
Self-hosted Sync with Couchbase Servers is available as part of their
Enterprise offering
✅ as part of the Enterprise offering; gets expensive
quickly
Edge: Couchbase Lite; Server: Couchbase
Multi-model NoSQL document-oriented database
Couchbase Lite: iOS, Android, macOS, Linux, Windows, Raspbian
and Raspberry Pi OS
Couchbase Sync Gateway: Red Hat Enterprise Linux (RHEL) 9.x, Alma Linux
9.x, Rocky Linux 9.x, Ubuntu, Debian (11.x, 12.x), Windows Server 2022
.Net
C
Go
Java
JavaScript info
Kotlin
PHP
Python
Ruby
Scala
✅
Couchbase Lite is available under different licenses; the open
source Community Edition does not get regular updates and misses many
features especially around Sync (e.g. it does not include Delta Sync making
it slow and expensive)
Typically requires Couchbase servers, quickly gets expensive
MongoDB: NoSQL document store; RealmDB: Embedded NoSQL DB
MongoDB: Linux
OS X
Solaris
Windows
Mongo Realm DB:
Android, iOS
more than 20 languages, e.g. Java, C, C#, C++
✅
MongoDB changed its license from open source (AGPL) to MongoDB
Inc.’s Server Side Public License (SSPL) in 2018. RealmDB is open source
under the Apache 2.0 License. The Data Sync was proprietary.