1. PBDJ APRIL 2012 | POWERBUILDER.SYS-CON.COM
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Tips & tricks
I
n data entry type applications there can be a need
for users to customize the data entry windows
in some way. This is especially true for packaged
software that goes to a variety of companies and in-
dustries where rules and requirements for the same
type of data can be very different. Properties such
as what is a required field, initial values, edit masks
can vary for each installation. This article describes
some of the methods that can be used to create uni-
fied customizable data entry DataWindows, specifi-
cally for the input of new records.
Usually entering new data for an account, contact
or part requires a certain minimum set of informa-
tion consisting of either application required fields
and user required and recommended fields. This
could be a small set out of a large number of fields.
Creating a runtime generated custom data input Da-
taWindow makes entering new data much easier for
the user as they are presented with only the data that
needs to be input for a new entity of some sort rather
than sifting through a large number of optional fields
that could exist on multiple windows. The DataWin-
dow control allows for this type of flexibility.
Issues
There are some considerations when merging
input fields from different DataWindows: (for this ar-
ticle it is assumed the data is sourced from database
tables):
1. Datatype.
2. Input type: simple free input, dropdown list,
calendar or child DataWindow, checkbox.
3. Initial values associated with the field.
4. Editmask associated with the field.
Example
In the example used for this article is a contact
application with five different edit windows avail-
able for different aspects of contact information as
seen in Figure 1.
Generating Dynamic Input DataWindows
By buck wooLley
DataWindow Consolidation

2. Tips & tricks
PBDJ april 2012 | POWERBUILDER.SYS-CON.COM
PAGE 26
Each DataWindow contains input fields that are re-
quired by the application or can be set to either required
or recommended by the user (see Figure 2).
Each of these windows contais one or two DataWindows
for data entry and editing.The new record input screen
is created be deconstructing each of these individual Da-
taWindows, removing each field that has been marked as
required or recommended and at runtime creating a new
DataWindow containing only these fields with their associ-
ated properties (see Figure 3).
Creating the Input DataWindow
Some of the properties such as column name, label text
and edit style are part of the DataWindow, others such as
initial values, edit mask and required flags are user definable
and are stored in tables for each DataWindow and are based
on the DataWindow and column name.
The new input DataWindow to be created as an external
DataWindow.The base DataWindow syntax is stored as an
instance variable with position holders for the tables and
column section of the DataWindow.These are marked by
the strings‘*tables*’ and‘*columns*’.These strings can be
replaced later by syntax that will be generated in two vari-
ables called ls_dw_tables and ls_dw_columns.
string idw_syntax = ‘release 10.5; datawindow(units=0 timer_inter-
val=0’....
....’detail(height=1000 color=”536870912” height.autosize=yes) ‘+&
‘table(*tables*) *columns* ‘+&
‘htmltable(border=”1” ) ‘....
A datastore called lds_column_list is used to select all
columns defined by the user as required or recommend. An
array containing the names of all relevant DataWindows is
the input parameter to this select.The req_flag determines
if the column is required to recommended.The SQL for this
would look something like:
select column_name, column_label, dw_object, req_flag
from recommended_fields
where dw_object in (‘d_input1’ ,‘d_input2’, ‘d_input3’ ....)
Figure 4 shows some of the data in lds_column_list,
specifically the column label, column name and source
DataWindow name.
The resulting list is sorted alphabetically by the column
label text so that the generated DataWindow displays labels
in alphabetic order.The list is looped through to gener-
ate strings that will contain the definition syntax for each
column and the create syntax for each column and label.
For each row in lds_column_list the DataWindow name
is loaded into a datastore object to extract its table and
display properties.
ls_dataobject = lds_column_list.object.dw_object[ll_row]
The DataWindow name is assigned to a datastore
dataobject called lds_work.
lds_work.dataobject = ls_dataobject
Now the code has reference to the syntax of the input
DataWindow. The column_name is moved from lds_col-
umn_list to the variable ls_column_name.
ls_column_name = lds_column_list.object.column_name[ll_row]
From this variable the information regarding proper-
ties the column is extracted from the datastore lds_work
using the describe function.
Figure 1: Five input windows for different aspects of a contact
Figure 2: Window allows user to mark an input field as mandatory or recommended

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ls_syntax = lds_work.describe(‘datawindow.syntax’)
The ls_syntax variable contains the entire syntax of lds_work
and will be used to extract the definition of the column ls_column_
name. Other properties are extracted.
ls_column = lds_work.describe(ls_column_name+’.dbname’)
ls_label = lds_work.describe(ls_column_name+’_t.text’)
ls_coltype = lds_work.describe(ls_column_name+’.coltype’)
ls_values = lds_work.describe(ls_column_name+’.values’)
ls_dddw_name = lds_work.describe(ls_column_name+’.dddw.name’)
Once these variables are loaded, the syntax for table section can
be generated for the column:
ls_dw_tables = ls_dw_tables+’ column=(type=’+ls_coltype+’ updatewhereclause=yes
name=’+ls_column_name+’ dbname=”’+ls_column+’” values=”’+ls_values+’”) ‘
The next step is to extract the Create() syntax for the column
defined in ls_column_name. There are several ways to do this. This
method first locates the name of the column in the ls_syntax string.
ll_pos = LASTPOS(ls_syntax,’name=’+ls_column_name+’ ‘)
If the column is found, find the start and end of the create syntax
for that column. First find the start position and store it in ll_pos1.
ll_pos1 = LASTPOS(LEFT(ls_syntax,ll_pos),’column(‘)
Next, find the end of the objects definition by finding the start of the
next object.To be most generic this involves finding the start of all pos-
sible objects and find the one closest to and greater than the amount in
variable ll_pos.
Figure 3: The dynamically created input DataWindow with fields from each of the five
data edit windows

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Tips & tricks
ll_pos_a[1] = POS(ls_syntax,’htmltable(‘, ll_pos)
ll_pos_a[2] = POS(ls_syntax,’column(‘, ll_pos)
ll_pos_a[3] = POS(ls_syntax,’text(‘, ll_pos)
ll_pos_a[4] = POS(ls_syntax,’compute(‘, ll_pos)
ll_pos_a[5] = POS(ls_syntax,’rectangle(‘, ll_pos)
ll_pos_a[6] = POS(ls_syntax,’line(‘, ll_pos)
ll_pos_a[7] = POS(ls_syntax,’button(‘, ll_pos)
Loop through array ll_pos_a to find the end of the columns
syntax and store that value in ll_pos2. Using the values in ll_
pos1 and ll_pos2 extract the column definition using the MID()
function, then set the id and tabsequence for the column
which will be a sequence number variable ll_detail_id.
ll_detail_id++
Extract the column definition for ls_column_name.
ls_column = MID(ls_syntax,ll_pos1,ll_pos2 - ll_pos1)
Find the id for the column and replace it with ll_detail_id.
ll_pos = POS(ls_column,’id=’)
IF ll_pos > 0 THEN
ll_pos = ll_pos+3
ll_pos1 = POS(ls_column,’ ‘,ll_pos)
ls_column = REPLACE(ls_column, ll_pos, ll_pos1 - ll_pos, STRING
(ll_detail_id)+’ ‘)
END IF
Find the tabsequence for the column and replace it with
ll_detail_id.
ll_pos = POS(ls_column,’tabsequence=’)
IF ll_pos > 0 THEN
ll_pos = ll_pos+12
ll_pos1 = POS(ls_column,’ ‘,ll_pos)
ls_column = REPLACE(ls_column, ll_pos, ll_pos1 - ll_pos, STRING
(ll_detail_id)+’ ‘)
END IF
A definition of the column label must be created after
creating the definition for the column. The label definition
is static except for the label text and name. The label text
was extracted earlier from lds_work and the label name is
ls_column_name +’_t’.
ls_label_name = ls_column_name+’_t’
ls_dw_columns = ls_dw_columns+’ text(band=detail alignment=”0”
text=”’+ls_label+’” name=’+ls_label_name....
After looping through the list of required columns the con-
tents of the variables the complete DataWindow syntax can
be made by taking the template DataWindow syntax defined
earlier in idw_syntax and doing the appropriate substitutions.
Once the syntax is made then the new data input DataWin-
dow can be created.
ll_pos = POS(ls_dwsyntax,’*tables*’)
ls_dwsyntax = REPLACE(ls_dwsyntax,ll_pos,8,ls_dw_tables)
ll_pos = POS(ls_dwsyntax,’*columns*’)
ls_dwsyntax = REPLACE(ls_dwsyntax,ll_pos,9,ls_dw_columns)
dw_step.create(ls_dwsyntax,ls_err)
The DataWindow has now been created but it still needs to
be formatted. This involves looping through the column list
again and adjusting the y values for each input field based
on the sort order. It is also at this point child DataWindows
are retrieved and initial values and edit masks are set.
ls_dwsyntax=ls_dwsyntax+’ ‘+ls_column_name+”.Initial=’”+ls_default_
Figure 4: Table containing information regarding recommended column
Figure 5: Shows the various sources of properties for the generated DataWindow

5. PBDJ APRIL 2012 | POWERBUILDER.SYS-CON.COM
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TIPS & TECHNIQUES
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string+”’”
ls_dwsyntax=ls_dwsyntax+’ ‘+ls_column_name+”.editmask.mask=’”+ls_mask+”’”
To determine if the field contains a child DataWindow, then the
following test is made:
IF ls_dddw_name <> ‘?’ THEN
If a child DataWindow exists, then determine if arguments ex-
ists and retrieve the DataWindow. Most of the child DataWindows
either have no argument or require a language id.
lds_work.getchild(ls_column_name,ldwc)
ldwc.settransobject(gtr_main)
ls_arguments = ldwc.describe(‘DataWindow.Table.Arguments’)
IF ls_arguments = ‘?’ THEN
ldwc.retrieve()
ELSE
ldwc.retrieve(ll_language_id)
END IF
Columns with child DataWindows with more complex arguments
are removed prior to presentation.The user must go to the appropriate
edit window to edit these columns.
The DataWindow dw_input is now presented to the user correctly
formatted with all recommended and required fields. Once the user has
filled in the fields then the data can be saved.
Save Process
The saving of a new contact is a two-step process.The first step is
inserting new records in the various tables.These new records contain
the data that is considered as required by the application. For instance
a name, country and language for a contact are considered applica-
tion requirements.This data is saved directly from a static contact
DataWindow.
This process establishes the new contact record and creates a key val-
ue for the new contact. Once this is done, then the data in dw_input is
applied to tables using dynamic SQL updates.This is achieved by loop-
ing through lds_column_list to get column information and extracting
the entered value for each column from the dw_input DataWindow.
In the save loop the first tasks is to get the DataWindow column
name for the field.
ls_column_name = lds_column_list.object.column_name[ll_row]
Next get the database name for the column from the original
DataWindow object which has temporarily been created in datastore
lds_work. Extract the table name from the column name.
ls_column = lds_work.describe(ls_column_name+’.dbname’)
ll_pos = POS(ls_column,’.’)
ls_table = LEFT(ls_column,ll_pos - 1)
Next, get the user entered data for the column from dw_input.
ls_data = STRING(dw_input.getitemstring(1, ls_column_name))
Use these variables to build the SQL update statement for the
column and execute it.
ls_sql = ‘Update ‘+ls_table+’ set ‘+ls_column+’=’+ls_data+’ where id=’+ls_id
EXECUTE IMMEDIATE :ls_sql USING sqlca;
Repeat this process for all columns in dw_input. Figure 5 shows
the relationship between the original data edit DataWindow, the user
selected recommended and required fields in lds_column_list, and the
generated new record input DataWindow dw_input. It summarizes the
process for a single column AssociateType which is defined as a recom-
mended field. It has a format of dropdown DataWindow and an initial
value of Client/Applicant.
Conclusion
In complex applications where there are several data edit windows,
it’s possible to dynamically create new data input windows that con-
tain only the columns that are marked as recommended or required
by the user.This process aggregates multiple input windows into one
dynamically created DataWindow eliminating the need for the user to
open and edit multiple windows while insuring that all necessary data
has been entered.
About the Author
Buck Woolley has been involved in programming and software develop- ment since
1987 and has been a Powerbuilder consultant since 1994, starting with version 3.0.
Currently he is the main client server developer at Patrix AB in Gothenberg, Sweden.
He has made numerous Techwave presentations covering advanced datawindow tech-
niques. Buck and his wife reside in San Diego, California.
bwoolley@dw-extreme.com