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SQL

superduperdb supports SQL databases via the ibis project. With superduperdb, queries may be built which conform to the ibis API, with additional support for complex data-types and vector-searches.

Inserting data​

Table data must correspond to the Schema for that table. Either create a Schema and Table or use an auto-detected Schema. Once you've got a Schema, all data inserted must conform to that Schema:

import pandas

pandas.DataFrame([
    PIL.Image.open('image.jpg'), 'some text', 4,
    PIL.Image.open('other_image.jpg'), 'some other text', 3,
])

t.insert(dataframe.to_dict(orient='records'))

Selecting data​

superduperdb supports selecting data via the ibis query API. For example:

db['my_table'].filter(t.rating > 3).limit(5).select(t.image).execute()

Vector-searches are supported via the like operator:

(
    db['my_table']
    .like({'text': 'something like this'}, vector_index='my-index')
    .filter(t.rating > 3)
    .limit(5)
    .select(t.image, t.id)
).execute()

Vector-searches are either first or last in a chain of operations:

(
    db['my_table']
    t.filter(t.rating > 3)
    .limit(5)
    .select(t.image, t.id)
    .like({'text': 'something like this'}, vector_index='my-index')
).execute()

Updating data​

Updates are not covered for superduperdb SQL integrations.

Deleting data​

db.databackend.drop_table('my-table')