Hi, OK as a quick test I created a default spark data grid with 100,000 rows and ten columns with simple numbers and tried sorting a column, it’s slow about 10 seconds on my machine.
Looking in Scout 30+% of the time is garbage collection in the sort routines and 20+% is the sorting itself, so thats’ where most of the time is going. So as I suspected a lot of it is the temporary objects used by the default sorting algorithms, in particular the code to handle complex fields. There is however a work around: - Changing the data grid to use simple custom sort functions changes the sort time to around 1 second and there’s very little garbage collection. - Changing to use a named class rather than objects improved the performance a little more. OR alternatively removing the custom sort and using the named data class reduced the column sort time to about 2 seconds (again down for 10 seconds). Double checking with a mx datagrid by default the performance was significantly faster and usable without any custom sort routines or named classes so I can see there may be an expectation that the spark data grid performs just as well. The mx datagrid is calling Sort.sort method directly, it may be possible to optimise the spark Datagrid to do this as well. However for large amounts of data the defaults may not going to be the most performant, and given they are coding for all cases rather than a known simple case it's probably not that surprising. Thanks, Justin
