Dagger

The Dagger database is the high-performance spatial database and streaming engine at the heart of the Foresight SDK platform. It is specifically designed to manage and stream massive geometric models over the network with minimal latency, ensuring that users can interact with 3D data as if it were stored locally.

Progressive Fidelity

The core philosophy of Dagger is to maximize the rate of increasing render fidelity. Instead of making the user wait for a massive file to download, Dagger prioritizes the delivery of data that contributes most to the visual quality of the scene. This allows clients to show a low-resolution preview almost instantly and progressively refine it into a high-fidelity representation as more data arrives.

Scaling to the Massive

Handling a variety of 3D and volumetric data formats—including point clouds, block models, triangle meshes, and time-varying models—Dagger is built to support datasets that are far too large to fit in local memory. It achieves this by leveraging advanced Level of Detail (LOD) and spatial acceleration structures that only stream what the user is currently looking at.

Architecture and Scale

Dagger is built as a cloud-native application that separates model metadata from bulk geometric data. While model structure and relationships are managed in a high-performance metadata database, the bulk data is stored in cost-effective object storage like Amazon S3 or Azure Blob Storage. This separation allows Dagger to scale horizontally and handle petabytes of spatial data without a corresponding increase in infrastructure complexity.

Seamless Integration

Dagger is designed to be a transparent part of the Foresight SDK ecosystem. It provides a robust API for interacting with the database and coordinating data access, allowing developers to focus on building spatial tools rather than managing network protocols or byte-level streaming.

To ensure data security, Dagger relies on modular middleware for authorization. Rather than implementing a proprietary authentication system, it leverages standardized tools like fsl_axum to verify user identity and enforce project-level access rules. This ensures that spatial data is only streamed to authorized users, maintaining a secure and reliable audit trail for all project interactions.