Performance #

General #

The table is easily able to render and page ten of thousands of rows, however there is a cost to rendering data types dynamically. The cost of reflection increases proportianally with the size of your dataset.

In practice the size of your dataset introduces limitations ie:- filtering larger datasets just takes longer.

Specifying the column types such as booleans and especially identity columns, are likely to help with performance.

_{sever side paging will be implented in a future release}

Search filtering #

on large volume data sets a debounce should be added to the search as the ui may feel sticky while the dataset is filtered

Todo add debounce example

Dates #

The table does not attempt to infer dates, as JSON dates are strings and not date types. Although parsing a date type is perfectly valid, the performance loss is not inconsequental. As such date columns should be specified with the isDateCols option, or they will be treated as strings.

The example below contains 5 columns with 10,000 rows the id column is hidden, nb: this is running on a low powered sandbox, is there is any noticeable performance degradation ?

https://codesandbox.io/s/10000-row-example-npxvq

Todo add 100,000k row example