Size mismatch in AjaxDataSource in append mode

Hello all - I’m having some trouble working with an AjaxDataSource displaying continuously updating data in a browser. If it is at all relevant, I am using Flask to serve the data.

What I am trying to do:
I am attempting to continuously update a plot with an AjaxDataSource that has two line glyphs, each with it’s own independent x and y lists of data (x is time and y is values). This means that the columns in the AjaxDataSource will have different sizes, but that does not affect the result as long as each line’s x and y are the same.

The problem I am having is when the AjaxDataSource is in “append” mode with a “max_size” parameter set. Right when the AjaxDataSource is filled to the point where old data would usually start to be removed because of the max_size parameter, it fails with the JS error “Uncaught error: size mismatch.”

Here is how I am defining my layout in python:

source = AjaxDataSource(

fig = figure(title='Test')
fig.line(x='time_1', y='data_1', legend_label='Data 1', color='blue', source=source)
fig.line(x='time_2', y='data_2', legend_label='Data 2', color='green', source=source)

So with this example, as soon as one of the data columns reaches 1000 data points, the error occurs.

What I have tried:
When using a regular ColumnDataSource, the way that I would usually fix the problem of different-sized data columns is to pad the smaller ones with numpy.nan values. I have tried same thing with the AjaxDataSource, but I cannot find a way that works. From other posts here (like this one) I have learned that this is because JSON does not support NaN values.

Is there a way to avoid the first problem, or if not, is there a way to pad the lengths of the columns in an AjaxDataSource?

Please let me know what screenshots or code would be helpful.
Thank you for any help!

You will have to use separate data sources for each glyph, full stop. It is an absolute assumption that all columns in a CDS (which an AjaxDataSource is a specific subtype of) are always all the same length, at all times. You should think of a CDS like a “lightweight DataFrame”—you can’t ever have different column lengths in DataFrames.

1 Like

Undertstood - thank you!