As your project grows, Talend Studio performance can slow down. You are usually required to allocate more memory to Talend Studio to improve its performance.
You can modify the memory allocated to Talend Studio by modifying the relevant Studio .ini configuration file for your system, such as TOS_DI-win32-x86.ini for 32-bit Windows systems. For Linux/Solaris/Windows systems, the relevant .ini configuration file is located in your Studio installation folder.
By default, the .ini file includes the following JVM parameters:
-vmargs -Xms64m -Xmx768m -XX:MaxPermSize=512m -Dfile.encoding=UTF-8
The memory that you can allocate to your Talend Studio depends mostly on your system memory availability. However, Talend recommends the following settings based on the most usual system memory values.
With 2 GB of memory available on a 32-bit system, bounds can be changed as follows:
-vmargs -Xms256m -Xmx1024m -XX:MaxPermSize=256m -Dfile.encoding=UTF-8
With 8 GB of memory available on 64-bit system, the optimal settings can be:
-vmargs -Xms1024m -Xmx4096m -XX:MaxPermSize=512m -Dfile.encoding=UTF-8
For Mac systems, the studio .ini configuration file named TOS_DI-macosx-cocoa.ini is located in the Talend Studio install dir\TOS_DI-macosx-cocoa.app\Contents\MacOS directory. The default settings are shown below:
--launcher.XXMaxPermSize512m -vmargs -Xms64m -Xmx768m -Xdock:icon=../Resources/talend.icns -XstartOnFirstThread -Dorg.eclipse.swt.internal.carbon.smallFonts -Dosgi.instance.area.default=../../../workspace -Dfile.encoding=UTF-8
Modify the Java parameters to allocate more memory to Talend Studio. For example, with 8 GB of memory available on 64-bit system, the optimal settings can be:
--launcher.XXMaxPermSize512m -vmargs -Xms2014m -Xmx4096m -Xdock:icon=../Resources/talend.icns -XstartOnFirstThread -Dorg.eclipse.swt.internal.carbon.smallFonts -Dosgi.instance.area.default=../../../workspace -Dfile.encoding=UTF-8
Note: The setting of this .ini configuration file will only impact the performance of Talend Studio. It will not impact the job execution itself. If you want to allocate more memory to the job execution, please refer to the Allocating more memory to one or several Jobs section in the article: OutOfMemory Exception.