• 23-Jan-2023
Lightrun Team
Author Lightrun Team
Share
This article is about fixing ConnectionError: HTTPConnectionPool: Max retries exceeded in Plotly Jupyter Dash

ConnectionError: HTTPConnectionPool: Max retries exceeded in Plotly Jupyter Dash

Lightrun Team
Lightrun Team
23-Jan-2023

Explanation of the problem

The error message ConnectionError: HTTPConnectionPool(host='x86_64-conda_cos6-linux-gnu', port=8050): Max retries exceeded with url: /_alive_a3a79a8c-7956-4602-96cc-f8f88ec33619 (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f4dd6f53a58>: Failed to establish a new connection: [Errno -2] Name or service not known')) is encountered when attempting to run demo code. This issue is likely caused by a failure to establish a new connection to the specified host and port due to a failure to resolve the hostname.

Code Example:

from urllib3.exceptions import NewConnectionError
from urllib3.connectionpool import HTTPConnectionPool

try:
    http = HTTPConnectionPool(host='x86_64-conda_cos6-linux-gnu', port=8050)
    http.request('GET', '/_alive_a3a79a8c-7956-4602-96cc-f8f88ec33619')
except NewConnectionError as e:
    print(e)

Expected Behavior: The code should establish a connection to the specified host and port and return a response without error.

Troubleshooting with the Lightrun Developer Observability Platform

Getting a sense of what’s actually happening inside a live application is a frustrating experience, one that relies mostly on querying and observing whatever logs were written during development.
Lightrun is a Developer Observability Platform, allowing developers to add telemetry to live applications in real-time, on-demand, and right from the IDE.

  • Instantly add logs to, set metrics in, and take snapshots of live applications
  • Insights delivered straight to your IDE or CLI
  • Works where you do: dev, QA, staging, CI/CD, and production

Start for free today

Problem solution for ConnectionError: HTTPConnectionPool: Max retries exceeded in Plotly Jupyter Dash

This error occurs when the HTTPConnectionPool is unable to establish a new connection to the specified host and port. This can happen due to a number of reasons such as network connectivity issues, incorrect host or port being specified or the host being down.

The user can try the following solutions to resolve this issue:

  • Checking network connectivity and ensuring that the machine is connected to a stable network.
  • Ensuring that the specified host and port are correct and reachable.
  • Checking if the host is up and running and if not, starting it.
  • Increasing the number of retries for the HTTPConnectionPool.
  • Updating the Plotly Jupyter Dash package to the latest version to see if any bugs have been fixed.

Example Code:

from urllib3 import Retry
from urllib3.util import Retry
from urllib3.util.retry import Retry

retry_strategy = Retry(
    total=5,
    status=5,
    backoff_factor=0.1,
    status_forcelist=[ 500, 502, 503, 504 ],
)

http = urllib3.PoolManager(
    retries=retry_strategy
)

In this example code, the user is increasing the number of retries to 5 and also setting a backoff factor of 0.1. Additionally, the user is also specifying a list of status codes that should trigger a retry. This can help resolve the “ConnectionError: HTTPConnectionPool: Max retries exceeded” error.

Other popular problems with Plotly Jupyter Dash

Problem: ConnectionError: HTTPConnectionPool: Max retries exceeded

When attempting to run the demo code for Plotly Jupyter Dash, users may encounter a ConnectionError with the message “HTTPConnectionPool(host=’x86_64-conda_cos6-linux-gnu’, port=8050): Max retries exceeded with url: /_alive_a3a79a8c-7956-4602-96cc-f8f88ec33619 (Caused by NewConnectionError(‘<urllib3.connection.HTTPConnection object at 0x7f4dd6f53a58>: Failed to establish a new connection: [Errno -2] Name or service not known’))”. This error occurs when the connection to the Jupyter Dash server fails due to a failure to establish a new connection.

Solution:

One possible solution to this problem is to check the network connection and ensure that the hostname and port specified in the error message are correct. If the hostname and port are correct, it may be necessary to check the firewall settings to ensure that the connection is not being blocked. If the problem persists, it may be necessary to check the Jupyter Dash server logs for additional information on the cause of the connection failure.

Problem: Incorrectly configured Jupyter Dash server

Some users may encounter issues when attempting to run Plotly Jupyter Dash if the Jupyter Dash server is not configured correctly. This can result in errors such as “Attribute not found” or “module not found” when trying to import Plotly Jupyter Dash modules or when trying to use certain functions within the library.

Solution:

To resolve this issue, it is necessary to ensure that the Jupyter Dash server is configured correctly and that all necessary dependencies are installed. This may involve installing additional libraries or modifying the server’s configuration files. It may also be necessary to update the version of Plotly Jupyter Dash being used to ensure compatibility with the server.

Problem: Incompatibility with other libraries

When using Plotly Jupyter Dash, users may encounter issues if there are conflicts with other libraries being used in the same project. This can result in unexpected behavior or errors when attempting to use certain functions within Plotly Jupyter Dash.

Solution:

To resolve this issue, it is necessary to identify any conflicting libraries and either update them to a compatible version or remove them from the project. It may also be necessary to update the version of Plotly Jupyter Dash being used to ensure compatibility with the other libraries.

A brief introduction to Plotly Jupyter Dash

Plotly Jupyter Dash is a library for building web-based interactive analytical applications. It is built on top of Plotly.js, a JavaScript library for creating interactive plots, and the Jupyter Notebook, a web-based interactive computational environment. Dash applications are written in Python and rendered in the browser using JavaScript and HTML. The library provides a high-level, easy-to-use interface for creating complex dashboards and interactive visualizations. The library is useful for data scientists, engineers, and analysts who want to create interactive visualizations without having to write JavaScript code.

Dash allows users to create a wide range of interactive visualizations, including bar charts, line charts, scatter plots, heat maps, and more. The library also supports the integration of interactive controls, such as dropdown menus and sliders, which can be used to filter or modify the data displayed in the visualization. Dash also supports the integration of other Jupyter widgets, such as text boxes and buttons, which can be used to build more complex applications. The library also includes a built-in server and a built-in development interface, which makes it easy to deploy and test Dash applications in a local or remote environment.

Most popular use cases for Plotly Jupyter Dash

  1. Plotly Jupyter Dash can be used for creating interactive data visualizations within Jupyter notebooks. Dash provides a simple and powerful library for building web-based visualizations, which can be easily integrated with Jupyter notebooks to create interactive and shareable data visualizations.
  2. Plotly Jupyter Dash can be used to create a wide variety of data visualizations such as line plots, scatter plots, bar charts, histograms, and more. In addition, Dash also provides interactive components such as dropdown menus and sliders, which can be used to dynamically update the visualizations based on user input.
  3. Plotly Jupyter Dash can be used to create custom dashboards, where multiple visualizations can be combined and organized to provide a holistic view of the data. Additionally, Dash allows for easy deployment of the dashboards and visualizations on the web, which can be shared with others.
import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()

app.layout = html.Div([
    dcc.Graph(
        id='example-graph',
        figure={
            'data': [
                {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},
                {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},
            ],
            'layout': {
                'title': 'Dash Data Visualization'
            }
        }
    )
])

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

This is a sample code to create a simple bar chart using Plotly Jupyter Dash. The sample creates a Dash app and sets its layout as a Div containing a Graph component. The figure attribute of the Graph component is set to a dictionary containing the data and layout properties of the chart. The data property is a list of dictionaries, where each dictionary represents a dataset and has properties such as x, y, type and name. The layout property is a dictionary containing the title of the chart. The last line runs the app in debug mode.

Share

It’s Really not that Complicated.

You can actually understand what’s going on inside your live applications. It’s a registration form away.

Get Lightrun

Lets Talk!

Looking for more information about Lightrun and debugging?
We’d love to hear from you!
Drop us a line and we’ll get back to you shortly.

By submitting this form, I agree to Lightrun’s Privacy Policy and Terms of Use.