I have been playing around with the Apache e-charts implementation provided with PB 2019 R3.   I have found it to be very slow when dealing with large amounts of data.   A key factor in this is the use of getitem to extract data from datawindows to build the e-chart dataset.   

I am developing an alternate solution where I grab a copy of the datawindow that is the data source, make all objects on the datawindow invisible then dynamically build a visible computed field at x=1,y=1 , height =64 that evaluates to a string containing a dataset row for the chart I am making.   I also force all bands to 0 height other than the detail band which I force to height 100.   I then save the datawindow as ascii (the only data that is saved is the value of my computed field)  and I now have a file that contains a formatted dataset.  I am actually developing all this in PB12.5 right now so it would work in any version.  I can create the dataset for 3 columns and 18500 rows in less than a second using this teechnique.

The nastiest aspect of this is dynamically building the computed field expression and handling different data types etc. 

This function will m

of_setup_dw(datawindow adw_ref,as_data_columns[])

string syntax
syntax=adw_ref.object.datawindow.syntax

string ls_tag
string ls_object_names[],ls_object_types[],ls_object_type_filters[],ls_coltypes[]
long i

//Get all the objects and make them invisible
gd.inv_dw_util.of_get_dwobject_array(adw_ref,ls_object_names,ls_object_types,ls_object_type_filters)

string ls_export_dataset_expression
for i=1 to upperbound(ls_object_names)
adw_ref.modify(ls_object_names[i]+'.visible="0"')
next

//Create the computed field for the data columns
string rtns
string ls_compute
ls_compute='create compute(band=detail alignment="0" expression="~~"x~~"" border="0" color="0" x="1" y="1" height="64" width="174" format="[GENERAL]" html.valueishtml="0" name=c_export_dataset visible="1" font.face="Arial" font.height="-10" font.weight="400" font.family="2" font.pitch="2" font.charset="0" background.mode="2" background.color="16777215" background.transparency="0" background.gradient.color="8421504" background.gradient.transparency="0" background.gradient.angle="0" background.brushmode="0" background.gradient.repetition.mode="0" background.gradient.repetition.count="0" background.gradient.repetition.length="100" background.gradient.focus="0" background.gradient.scale="100" background.gradient.spread="100" tooltip.backcolor="134217752" tooltip.delay.initial="0" tooltip.delay.visible="32000" tooltip.enabled="0" tooltip.hasclosebutton="0" tooltip.icon="0" tooltip.isbubble="0" tooltip.maxwidth="0" tooltip.textcolor="134217751" tooltip.transparency="0" transparency="0" )'
adw_ref.modify(ls_compute)

adw_ref.modify("detail.height=100")
adw_ref.modify("header.height=0")
adw_ref.modify("summary.height=0")
adw_ref.modify("footer.height=0")

string ls_expr
ls_expr='~~"[~~"'
//Now build the expression
for i=1 to upperbound(as_data_columns)
ls_expr+='+'

string ls_coltype,ls_object_type
string ls_val_expr

ls_object_type=adw_ref.describe(as_data_columns[i]+".type")
ls_coltype=adw_ref.describe(as_data_columns[i]+".coltype")
ls_val_expr=""

if ls_object_type="compute" then
long ll_coltype
//See if we have a hint on how to handle the data type of the computed field

//We may have a datetime we want to force to date.
ls_tag=lower(adw_ref.describe(as_data_columns[i]+".tag"))

ll_coltype=pos(ls_tag,"coltype(")

if ll_coltype > 0 then
ls_coltype=trim(gd.inv_str.of_parse_string(mid(ls_tag,ll_coltype+8),1,")"))
end if
end if

if ls_coltype='date' then
ls_val_expr=' ~~"~'~~"+string('+as_data_columns[i]+',~'yyyymmdd~')+~~"~'~~" '

elseif ls_coltype='time' then
ls_val_expr=' ~~"~'~~"+string('+as_data_columns[i]+',~'hh:mm:ss~')+~~"~'~~" '

elseif ls_coltype='datetime' or ls_coltype='timestamp' then
ls_val_expr=' ~~"~'~~"+string('+as_data_columns[i]+',~'yyyymmdd hh:mm:ss~')+~~"~'~~" '

elseif gd.inv_dw_util.of_is_number_datatype(ls_coltype) then
ls_val_expr='string('+as_data_columns[i]+')'

else
ls_val_expr=' ~~"~'~~"+gf_fix_html_chars('+as_data_columns[i]+')+~~"~'~~" '
end if

if i=upperbound(as_data_columns) then
else
ls_val_expr+="+', '"
end if
ls_expr+=ls_val_expr
next
ls_expr+='+~~"]~~"+ if(getrow()=rowcount(),~~"~~",~~",~~")'

rtns=adw_ref.modify('c_export_dataset.expression="'+ls_expr+'"')

return 1