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(Optional) Setting up tables and encodings

Superduper has flexible support for data-types. In both MongoDB and SQL databases, one can uses superduper.DataType to define one's own data-types.

If no-datatypes are provided, superduper uses fallbacks to encode and decode data. To gain more-control, developers may use the DataType and Schema components.

DataType abstraction​

The DataType class requires two functions which allow the user to go to-and-from bytes. Here is an DataType which encodes numpy.ndarray instances to bytes:

import numpy
from superduper import DataType

my_array = DataType(
    'my-array',
    encoder=lambda x: memoryview(x).tobytes(),
    decode=lambda x: numpy.frombuffer(x),
)

Here's a more interesting DataType which encoders audio from numpy.array format to .wav file bytes:

import librosa
import io
import soundfile

def decoder(x):
    buffer = io.BytesIO(x)
    return librosa.load(buffer)

def encoder(x):
    buffer = io.BytesIO()
    soundfile.write(buffer)
    return buffer.getvalue()

audio = DataType('audio', encoder=encoder, decoder=decoder)

It's completely open to the user how exactly the encoder and decoder arguments are set.

You may include these DataType instances in models, data-inserts and more. You can also directly register the DataType instances in the system, using:

db.apply(my_array)
db.apply(audio)

To reload (for instance in another session) do:

my_array_reloaded = db.load('datatype', 'my_array')
audio_reloaded = db.load('datatype', 'audio')
tip

Many of the superduper extensions come with their own pre-built DataType instances. For example:

  • superduper.ext.pillow.pil_image
  • superduper.ext.numpy.array
  • superduper.ext.torch.tensor

Read more about DataType here.

Create a Schema​

The Schema component wraps several columns of standard data or DataType encoded data; it may be used with MongoDB and SQL databases, but is only necessary for SQL.

Here is a Schema with three columns, one of the columns is a standard data-type "str". The other 2 are given by the DataType instances defined above.

from superduper import Schema
from superduper.ext.pillow import pil_image

my_schema = Schema(
    'my-schema',
    fields={'txt': 'str', 'audio': audio, 'img': pil_image}
)

# save this for later use
db.apply(my_schema)

Create a table with a Schema​

If a Table is created with a Schema, all data inserted to this table will use that Schema.

from superduper import Table

db.apply(Table('my-table', schema=my_schema))

In MongoDB this Table refers to a MongoDB collection, otherwise to an SQL table.

Then when data is inserted, it will use this my_schema object:

db['my-table'].insert[_many](data).execute()