Performance Best Practices

This page explains how you can graph large amount of data quickly from SharePoint, and general tips to improve visualization performances.

What makes up the loading time?

The visualizer need to do two things in order to graph a visualization.

  1. Load data from SharePoint list/document library
  2. Render the loaded data on the selected graph

So, to speed up performance, let look at each step in turn

Improving data load time – use SharePoint list view

Manage large lists (more than 5000 items) in SharePoint is a complex topic, you can have a look at the Microsoft page. However, for visualization purposes, consider creating a dedicated SharePoint list view. A SharePoint list view can:

  • Only show columns that’s necessary for visualization
  • Apply filters to reduce the number of rows needed for visualization

To create a SharePoint list view that will help performance, follow these step:

create_list_view

create_list_view_1

create_list_view_2

create_list_view_3

create_list_view_4

create_list_view_5

 

Improve render time – use data grouping and use the right graphs

Once the data has been loaded by the visualizer, the rendering speed comes down to how many shapes (data points) must be rendered on the graph. To improve the rendering speed, consider

  1. Choose the right graph for your purpose
  2. Apply data grouping

Choose the right graph

Each graph has a different purpose, and with different rendering characteristics. For example, the line graph generally can handle a lot more data points than the pie graph. This is because the line is actually one shape (called “path”), so as the result, the line graph can handle a few thousands data points without any issues. However, if you try to render a few thousands pie slices on the one pie graph, it is not going to render well.

So choose the right graph is important, get familiar with the purpose and limitation of each graph and use the most appropriate one for your purpose.

Apply data grouping

Many graphs has the ability to group data and reduce the number of data points to render. Consider the following example.

large_list_grouping_1

large_list_grouping_2

large_list_grouping_3large_list_grouping_4

 

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