Binary data transfer

Motivation

Often for visualizations in genomics, massive social networks, or sensor data visualizations, it helps to be able to plot millions rather than simply hundreds of thousands of points.

By default, pydeck sends data from Jupyter to the frontend by serializing data to JSON. However, for massive data sets, the costs to serialize and deserialize this JSON can prevent a visualization from rendering.

In order to get around this, pydeck supports binary data transfer, which significantly reduces data size. Binary transfer relies on NumPy and its typed arrays, which are converted to JavaScript typed arrays and passed to deck.gl using precalculated binary attributes.

Usage

Binary transport will only work if the following requirements are met:

  • use_binary_transport must be set to True explictly on your Layer

  • Layer input data must be a pandas.DataFrame object.

  • Data that is not intend to be rendered should not be passed into the layer.

  • Accessor names must be strings representing column names within the data frame, e.g., get_position='position' is correct, not get_position=['x', 'y']. For example,

    This data format, where x & y represent a position and r, g, and b represent color values,

    x

    y

    r

    g

    b

    0

    1

    0

    0

    0

    0

    5

    255

    0

    0

    5

    1

    255

    255

    0

    should be converted to this format

    position

    color

    [0, 1]

    [0, 0, 0]

    [0, 5]

    [255, 0, 0]

    [5, 1]

    [255, 255, 0]

  • Binary transfer only works within Jupyter environments via pydeck.bindings.deck.Deck.show(). It relies on the socket-level communication built into the Jupyter environment.

Example