QDataObject

This package can be used to access MySQL query results as objects.

It can take a MySQL query result set resource handle as parameter and provides several types of functions to access the data from the result set.

It provides check or retrieve the result set data positions, as well retrieve the type and length of the respective columns.

The package provides implementations that use the regular MySQL and MySQLi extensions to access MySQL query results.

New:
DataObjectFactory

=== QMysqlDataObject ===
Test => SELECT * FROM geodb_textdata
TEST ENV:
Windows Vista Xampp, AMD Turion 64x2 Mobile 1.80 GHz
2 MB RAM (average performance)

1.a) object building runtime 0.176911115646s
on a db table with 337.925 records
(building uncached dataobject from uncached query at page offset 22210 by 10 rows/page)

1.b) object building runtime 0.275130033493s
on a db table with 337.925 records
(building uncached dataobject from uncached query at page offset 3000 by 100 rows/page)

2.a) object building runtime 0.0171821117401s
on a db table with 337.925 records
(building uncached dataobject from uncached query at page offset 10 by 10 rows per page)

2.b) object building runtime 0.0609588623047s
on a db table with 337.925 records
(building uncached dataobject from uncached query at page offset 10 by 100 rows per page)

3)

SELECT * FROM orders
LEFT JOIN customers on customers.CustomerID=orders.CustomerID

(northwind demo database)
object building runtime 0.055801153183s
(building uncached dataobject from uncached query at page offset 1 by 10 rows per page)
Mysql DB times:
Status runtime
(initialization) 0.0000377
Opening tables 0.0002247
System lock 0.0000032
Table lock 0.0000067
init 0.0000107
optimizing 0.0000037
statistics 0.0000107
preparing 0.00001
executing 0.0000467
Sending data 0.0002505
end 0.0000042
query end 0.0000035
freeing items 0.0000077
closing tables 0.000002
removing tmp table 0.0001105
closing tables 0.0000042
logging slow query 0.0000025

Conclusion:
Object building becomes much quicker if you operate with sql conditions.
The test above shows, that a cut on a deep offset is slow. So you have to reduce the data cloud by conditioning and so on.
(On a Linux Web server it will run much faster.)

Regards Tom Schaefer