Update Data in Real-Time with Dash Plotly Interval Component

Update On Load using Dash Plotly

In this article, we will explore how to update data in real-time using Dash and Plotly. Specifically, we’ll look at how to use the Interval component to trigger callbacks on page load.

Introduction

Dash is a popular Python framework for building web applications with interactive visualizations. One of its key features is the ability to update data in real-time using callbacks. A callback is a function that runs automatically when a user interacts with an application, or in this case, when the page loads. Plotly is a powerful library for creating interactive plots and charts.

The Problem

The problem our author faced was that if they only set the df variable to the latest data from the CSV file, it would only get the data once when the server starts. This means that if they wanted to keep the data up-to-date every day, they would have to restart the server every day.

Solution Overview

The solution involves using the Interval component in Dash to trigger a callback on page load. We’ll also explore how to handle memory bottlenecks from long-term apps by implementing a time-expiring cache.

Understanding Callbacks

Before we dive into the code, let’s take a brief look at what callbacks are and how they work in Dash.

A callback is a function that runs automatically when a user interacts with an application. In the context of our author’s app, the callback will run every time the page loads (i.e., on interval).

In Dash, callbacks are implemented using the @app.callback decorator. The decorator takes two arguments: the name of the callback and a function that defines what happens when the callback is triggered.

Code

Let’s take a look at the code that our author tried to use:

import dash
import pandas as pd

app = dash.Dash()

df = ''

def get_data():
    global df
    df = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')

app.layout = get_data

if __name__ == '__main__':
    app.run_server(debug=True)

This code tries to assign the df variable on page load, but it won’t work because Dash expects a callback function instead of just setting the df variable.

Implementing an Interval Component

To fix this issue, our author can use the Interval component in Dash. The Interval component takes two arguments: the name of the interval and a function that defines what happens when the interval is triggered.

Here’s how you might implement it:

import dash
from dash import dcc
from dash import html
from dash.dependencies import Input, Output
import pandas as pd

app = dash.Dash()

# Load data from CSV file
def load_data():
    return pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')

# Update interval function
@app.callback(
    Output('df', 'data'),
    [Input('interval-component', 'n_intervals')]
)
def update_interval(n):
    df = load_data()
    return df.to_json()

# Define layout
app.layout = html.Div([
    dcc.Interval(
        id='interval-component',
        interval=1000,  # Update every second (in milliseconds)
        n_intervals=0,
    ),
    html.H1('The time is: ' + str(pd.Timestamp.now())),
])

if __name__ == '__main__':
    app.run_server(debug=True)

In this code:

We load the data from the CSV file using a separate function called load_data.
We use the Interval component to trigger an update every second.
We define what happens when the interval is triggered by defining an output (df) and an input (n_intervals).
We display the current time in the app.

Handling Memory Bottlenecks

One of the potential issues with using Dash is memory bottlenecks. When dealing with large datasets, it can be challenging to manage memory efficiently.

To handle this issue, our author might consider implementing a cache expiration mechanism to prevent long-term data from being stored in memory.

Here’s an example of how you could implement a simple cache expiration mechanism:

import dash
from dash import dcc
from dash import html
from dash.dependencies import Input, Output
import pandas as pd
from datetime import datetime, timedelta

app = dash.Dash()

# Load data from CSV file
def load_data():
    return pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')

# Define cache expiration time
cache_expiration = 3600 * 24 * 7  # 1 week

# Create a variable to store the cached data
df_cache = None

def update_interval(n):
    global df_cache
    now = pd.Timestamp.now()
    
    if (now - datetime.fromtimestamp(df_cache['timestamp'])) < timedelta(seconds=cache_expiration):
        return df_cache['data']
    
    else:
        df = load_data()
        df_cache = {'timestamp': now, 'data': df.to_json()}
        return df.to_json()

# Define layout
app.layout = html.Div([
    dcc.Interval(
        id='interval-component',
        interval=1000,  # Update every second (in milliseconds)
        n_intervals=0,
    ),
    html.H1('The time is: ' + str(now)),
])

if __name__ == '__main__':
    app.run_server(debug=True)

In this updated code:

We define a cache_expiration variable to store the amount of time (in seconds) that we want data to be cached.
We create a dictionary (df_cache) to store the cached data, which includes a timestamp and the JSON representation of the data.
When updating the interval, we check if the current time is within the cache expiration period. If it is, we return the cached data. Otherwise, we load new data from the CSV file, update the cache with the current time and the new data, and return the new data.

This code provides a basic example of how to handle memory bottlenecks by implementing a simple cache expiration mechanism. The actual implementation may vary depending on your specific requirements.

Conclusion

In this tutorial, we explored how you can use Dash’s Interval component to update your app every second. We also discussed the importance of handling memory bottlenecks and provided an example of how you could implement a simple cache expiration mechanism using Dash.

Last modified on 2023-08-16