Dash Python App Uploading for Long Time
Operation
This chapter contains several recommendations for improving the functioning
of your dash apps.
The master performance limitation of dash apps is probable the callbacks in
the application lawmaking itself. If you tin speed up your callbacks, your app
will experience snappier.
Memoization
Since Dash's callbacks are functional in nature (they don't contain any state),
it's easy to add together memoization caching. Memoization stores the results of a
function after it is called and re-uses the result if the function is called
with the aforementioned arguments.
For a simple example of using memoization in a Dash app to improve performance, run into the "Improving operation with memoization" section
in the advanced callbacks chapter.
Nuance apps are oft deployed across multiple processes or threads.
In these cases, each procedure or thread contains its ain retentiveness, it doesn't
share memory beyond instances. This means that if we were to use lru_cache,
our cached results might not be shared across sessions.
Y'all tin add together memoization caching with Long Callbacks.
Long callbacks are a great choice for caching callbacks.
In some cases, however, it may exist more memory efficient to cache within
your own functions that the callback calls, and non on the callback itself.
Caching a office tin can give you lot more control over the specific functionality y'all want to cache.
Some other selection for caching is the Flask-Caching library, which saves the results in a shared memory database like Redis or equally
a file on your filesystem. Flask-Caching also has other nice features like
time-based decease. Time-based expiry is helpful if you want to update your
information (clear your enshroud) every hour or every day.
Hither is an example of Flask-Caching with Redis:
from dash import Dash, dcc, html, Input, Output import datetime import os from flask_caching import Cache external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = Nuance(__name__, external_stylesheets=external_stylesheets) cache = Cache(app.server, config={ # try 'filesystem' if you don't want to setup redis 'CACHE_TYPE': 'redis', 'CACHE_REDIS_URL': bone.environ.get('REDIS_URL', '') }) app.config.suppress_callback_exceptions = Truthful timeout = 20 app.layout = html.Div([ html.Div(id='flask-cache-memoized-children'), dcc.RadioItems( [f"Choice {i}" for i in range(1, 4)], 'Option 1', id='flask-enshroud-memoized-dropdown' ), html.Div(f'Results are buried for {timeout} seconds') ]) @app.callback( Output('flask-cache-memoized-children', 'children'), Input('flask-cache-memoized-dropdown', 'value')) @cache.memoize(timeout=timeout) # in seconds def render(value): current_time = datetime.datetime.now().strftime('%H:%M:%Due south') render f'Selected "{value}" at "{current_time}"' if __name__ == '__main__': app.run_server(debug=True) Here is an case that caches a dataset instead of a callback.
It uses the FileSystem cache, saving the cached results to the filesystem.
This approach works well if there is 1 dataset that is used to update
several callbacks.
from dash import Dash, html, dcc, Input, Output import datetime as dt import numpy as np import pandas as pd from dash.dependencies import Input, Output from flask_caching import Cache external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = Nuance(__name__, external_stylesheets=external_stylesheets) cache = Enshroud(app.server, config={ 'CACHE_TYPE': 'filesystem', 'CACHE_DIR': 'cache-directory' }) TIMEOUT = 60 @cache.memoize(timeout=TIMEOUT) def query_data(): # This could be an expensive information querying step np.random.seed(0) # no-display df = pd.DataFrame( np.random.randint(0, 100, size=(100, iv)), columns=list('ABCD') ) now = dt.datetime.now() df['fourth dimension'] = [now - dt.timedelta(seconds=5*i) for i in range(100)] return df.to_json(date_format='iso', orient='split') def dataframe(): return pd.read_json(query_data(), orient='split up') app.layout = html.Div([ html.Div('Data was updated within the last {} seconds'.format(TIMEOUT)), dcc.Dropdown(dataframe().columns, 'A', id='live-dropdown'), dcc.Graph(id='alive-graph') ]) @app.callback(Output('live-graph', 'figure'), Input('live-dropdown', 'value')) def update_live_graph(value): df = dataframe() at present = dt.datetime.at present() render { 'information': [{ 'x': df['fourth dimension'], 'y': df[value], 'line': { 'width': 1, 'color': '#0074D9', 'shape': 'spline' } }], 'layout': { # display the current position of now # this line will be between 0 and 60 seconds # abroad from the last datapoint 'shapes': [{ 'blazon': 'line', 'xref': 'ten', 'x0': at present, 'x1': now, 'yref': 'paper', 'y0': 0, 'y1': 1, 'line': {'colour': 'darkgrey', 'width': 1} }], 'annotations': [{ 'showarrow': Imitation, 'xref': '10', 'ten': now, 'xanchor': 'right', 'yref': 'paper', 'y': 0.95, 'yanchor': 'peak', 'text': 'Current time ({}:{}:{})'.format( now.hour, at present.minute, now.second), 'bgcolor': 'rgba(255, 255, 255, 0.8)' }], # aesthetic options 'margin': {'l': xl, 'b': xl, 'r': 20, 't': 10}, 'xaxis': {'showgrid': False, 'zeroline': False}, 'yaxis': {'showgrid': Simulated, 'zeroline': Imitation} } } if __name__ == '__main__': app.run_server(debug=True) Graphs
Plotly.js is pretty fast out of the box.
Most plotly charts are rendered with SVG. This provides well-baked rendering,
publication-quality image consign, and wide browser support.
Unfortunately, rendering graphics in SVG can exist dull for big datasets
(like those with more than than 15k points).
To overcome this limitation, plotly.js has WebGL alternatives to
some chart types. WebGL uses the GPU to render graphics.
The high operation, WebGL alternatives include:
- scattergl: A webgl implementation of the scatter chart type. Examples, reference
- pointcloud: A lightweight version of scattergl with express customizability simply fifty-fifty faster rendering. Reference
- heatmapgl: A webgl implementation of the heatmap chart type. Reference
Currently, dash redraws the entire graph on update using the plotly.js
newPlot phone call. The operation of updating a chart could be improved
considerably past introducing restyle calls into this logic. If you or
your visitor would like to sponsor this work,
arrive touch.
Clientside Callbacks
Clientside callbacks execute your lawmaking in the customer in JavaScript rather than
on the server in Python.
Read more than nearly clientside callbacks in the clientside callbacks
chapter.
Long Callbacks
In improver to providing memoization caching, long callbacks can help you meliorate your app scalability by moving computations from the Dash app server to a groundwork task queue using Long Callbacks.
See the Long Callbacks chapter for more than information on how to implement these.
Data Serialization
New with Dash 2.0, y'all can use orjson to speed upward serialization to JSON and in plow improve your callback functioning. For an app that runs complex callbacks involving a large amount of data points, y'all could see a performance increase of up to 750 ms.
Learn more almost orjson here or install it with pip install orjson.
orjson is completely optional: If information technology exists in the surroundings, so Dash volition use it; if not, Dash will use the default json library.
In that location are many other ways that we tin can amend the operation of dash apps,
similar caching front-terminate requests, pre-filling the cache, improving plotly.js'south
webgl capabilities, reducing JavaScript bundle sizes, and more.
Historically, many of these performance related features have been funded
through company sponsorship. If you or your company would like to sponsor
these types of enhancements, delight go in touch,
we'd love to help.
Source: https://dash.plotly.com/performance
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