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Here is a schematic of the superduperdb design.


  1. superduperdb expects data and components to be added/ updated from a range of client-side mechanisms: scripts, apps, notebooks or third-party database clients (possibly non-python).

  2. Users and programs can add components (models, data encoders, vector-indexes and more) from the client-side. These large items are stored in the artifact-store and are tracked via the meta-data store.

  3. If data is inserted to the databackend the change-data-capture (CDC) component captures these changes as they stream in.

  4. (CDC) triggers work to be performed in response to these changes, depending on which components are present in the system.

  5. The work is submitted to the workers via the scheduler. Together the scheduler and workers make up the compute layer.

  6. workers write their outputs back to the databackend and trained models to the artifact-store

  7. The compute, databackend, metadata-store, artifact-store collectively make up the datalayer

  8. The datalayer may be queried from client-side, including hybrid-queries or compound-select queries, which synthesizes classical selects with vector-searches