python bokeh ajaxdatasource to activemq subscriber

PYTHON BOKEH AJAXDATASOURCE TO ACTIVEMQ SUBSCRIBER UPDATE
PYTHON BOKEH AJAXDATASOURCE TO ACTIVEMQ SUBSCRIBER CODE

AjaxDataSource is an abstraction for a data that is continuously streamed from the server every pollinginterval milliseconds.

PYTHON BOKEH AJAXDATASOURCE TO ACTIVEMQ SUBSCRIBER CODE

The sent JavaScript code contains code for requesting new data from the server via Ajax method. When a subscriber registers as a durable topic subscriber and then becomes unavailable, ActiveMQ will store the received messages for delivering them when the durable subscriber comes back up again. With a wide array of widgets, plot tools, and UI events that can trigger real Python callbacks, the Bokeh server is the bridge that lets you connect these tools to rich, interactive visualizations in the browser. The server is Flasks built-in server, which sends Bokeh plots HTML & JavaScript code to users browser. To avoid such a scenario, you would need to register ‘Order Management Service’ as a ‘Durable Topic Subscriber’ on ActiveMQ.

PYTHON BOKEH AJAXDATASOURCE TO ACTIVEMQ SUBSCRIBER UPDATE

The only way I’ve found to do this is to use “replace” mode for AjaxDataSource, and this doesn’t work well given the size and update rate of the data. Python has an incredible ecosystem of powerful analytics tools: NumPy, Scipy, Pandas, Dask, Scikit-Learn, OpenCV, and more. Ideally, I’d like the x-axis to show the age of a sample (-50 to 0 seconds), not an absolute time that it was collected or an arbitrary index number. Ajax allows a regular DHTML client (with JavaScript and a modern version 5 or later web browser) to send and receive messages over the web. The issue I’m having is finding a way to not update the x-axis data.

In a nutshell: we have our base html skeleton (layout.html) and ourdashboard.html which extends the base.In dashboard.html we output all parts (we see that next) of all plots that are contained in the plots variable. This all works great based on an example in flask_bokeh_ajax. The template files should look familiar to you if you have worked with Flask/Jinja before. The app will update data every 100ms, always showing the most recent 500 data samples and appending new data to the right side of the plot (AjaxDataSource stream in append mode).

I’ve worked through and fiddled with the Bokeh tutorials for streaming data from Flask and am really encouraged by the possibility. I’m looking at using Bokeh for an application with live streaming plots I’m currently working directly with the flot javascript library. Recommended Video Course: Interactive Data Visualization in Python With Bokeh. Tutorial on creating Bokeh plots with an AJAX data source, served from an existing Flask app.

Python bokeh ajaxdatasource to activemq subscriber