Appeon Performance Tuning Guide

Appeon® for PowerBuilder® 2016

FOR WINDOWS & UNIX & LINUX

DOCUMENT ID: ADC10089-01-06780-01

LAST REVISED: September 07, 2016

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Table of Contents

Appeon Performance
Impacts to Appeon performance
Impact of the Internet and slow networks
Impact of “heavy” client-side logic
Impact of large data transmission
Expected performance level
Automatic performance boosting
Performance-Related Settings
Overview
Appeon Developer performance settings
Appeon Enterprise Manager performance settings
Timeout settings
DataWindow data caching
Multi-thread download settings
Custom Libraries download settings
Log file settings
Internet Explorer performance settings
Web and application server performance settings
SAP EAServer
Microsoft IIS server
Database performance settings
Recommended database driver
Recommended database setting
Identifying Performance Bottlenecks
Overview
Appeon Performance Analyzer
Getting Started
Working with Appeon Performance Analyzer
Analyzing log files
Analyzing Windows application log files
Analyzing Appeon Server log files
Analyzing active transaction log
Identifying Performance Bottlenecks of Web Server and Application Server
Identifying Performance Bottlenecks of DB Server
Deadlock analysis
Identifying Performance Bottlenecks of PB application
Analyzing performance bottlenecks of PB application
Tuning: DB Server
Database
Tuning: Excessive Server Calls
Overview
Technique #1: partitioning transactions via stored procedures
Technique #2: partitioning non-visual logic via NVOs
Technique #3: eliminating recursive Embedded SQL
Technique #4: grouping multiple server calls with Appeon Labels
Tuning: Heavy Client
Overview
Technique #1: thin-out “heavy” Windows
Technique #2: thin-out “heavy” UI logic
Manipulating the UI in loops
Triggering events repeatedly
Performing single repetitive tasks
Initializing “heavy” tabs
Using ShareData or RowsCopy/RowsMove for data synchronization
Using computed fields
Using DataWindow expressions
Using complex filters
Using RowsFocusChanging/RowsFocusChanged events
Technique #3: offload “heavy” non-visual logic
Tuning: Large Data Transmissions
Overview
Technique #1: retrieving data incrementally
For Oracle database server
For all other database servers
Technique #2: minimizing excessive number of columns
Conclusion
Index