Performance issue - add_vlines

Issue: When vlines are added to a figure with subplots, the performance is much worse than without adding them.

Here is the code to reproduce the problem with 30 vlines vs. no vlines:

import numpy as np
import plotly.graph_objects as go
from dash import Dash, dcc, html
from plotly.subplots import make_subplots
import time

def generate_sample_data(n_traces=30, n_points=1000):

    x = np.linspace(0, 100, n_points)
    traces_data = []
    
    for i in range(n_traces):
        y = np.sin(x + i/10) + np.random.normal(0, 0.1, n_points)
        traces_data.append((x, y))

    vline_positions = np.random.choice(x, size=30, replace=False)
    
    return traces_data, vline_positions

def create_figure(traces_data, vline_positions=None, add_vlines=True):

    fig = make_subplots(
        rows=5, 
        cols=1,
        shared_xaxes=True,
        vertical_spacing=0.02
    )
    
    # add all traces to each subplot
    for row in range(1, 6):
        for i, (x, y) in enumerate(traces_data):
            fig.add_trace(
                go.Scatter(
                    x=x,
                    y=y,
                    name=f'Trace {i+1}',
                    mode='lines',
                    line=dict(width=1),
                    showlegend=False
                ),
                row=row,
                col=1
            )

    # add vertical lines
    if add_vlines and vline_positions is not None:
        for x_pos in vline_positions:
            for row in range(1, 6):
                fig.add_vline(
                    x=x_pos,
                    line_width=1,
                    line_dash="dash",
                    line_color="gray",
                    row=row,
                    col=1
                )
    
    fig.update_layout(
        title="Performance Test: vlines vs no vlines",
        showlegend=False,
        width=1200,
        height=1500,
        template="plotly_white",
    )
    
    # y-axis titles
    for i in range(1, 6):
        fig.update_yaxes(title_text=f"Y Axis {i}", row=i, col=1)
    
    return fig

# initialize data
traces_data, vline_positions = generate_sample_data()

# figure creation without vlines
start_time = time.time()
fig_no_vlines = create_figure(traces_data, vline_positions, add_vlines=False)
time_no_vlines = time.time() - start_time

# figure creation with vlines
start_time = time.time()
fig_with_vlines = create_figure(traces_data, vline_positions, add_vlines=True)
time_with_vlines = time.time() - start_time

app = Dash(__name__)

app.layout = html.Div([
    html.H1("Plotly vlines performance test"),
    html.Div([
        html.P(f"Time to create figure without vlines: {time_no_vlines:.4f} seconds"),
        html.P(f"Time to create figure with vlines: {time_with_vlines:.4f} seconds"),
        html.P(f"Difference: {(time_with_vlines - time_no_vlines):.4f} seconds")
    ]),
    html.H2("Figure with vlines"),
    dcc.Graph(figure=fig_with_vlines),
    html.H2("Figure without vlines"),
    dcc.Graph(figure=fig_no_vlines)
])

if __name__ == '__main__':
    app.run(debug=False)

The results of running it on my laptop are:

Plotly vlines performance test

Time to create figure without vlines: 0.5664 seconds
Time to create figure with vlines: 7.1900 seconds
Difference: 6.6235 seconds

I'm working on a simulator of trading strategies where vlines are important in the situation with subplots (to view vales of indicators easily) - there are many more traces than in this demo code with 34 vlines and the time difference is similar.

I discovered the issue using Python 3.12.8, Plotly 5.24.1 and 6.0.0rc0, running on Windows 11 64-bit.

Start by running the supplied Python reproduction with Plotly 5.24.1 or 6.0.0rc0 and compare figure creation with and without add_vline across the five subplots. Trace the add_vline path to locate the repeated work causing the timing gap; done means preserving the vline output while substantially reducing creation time for the reported case.