Parallel execution in SSIS improves performance on computers that have multiple physical or logical processors. To support parallel execution of different tasks in a package, SSIS uses two properties: MaxConcurrentExecutables and EngineThreads. If you are like me, you probably did not even know about these two properties, and therefore were unaware of the opportunity to make your SSIS packages execute faster. A description of each property:
The MaxConcurrentExecutables property is a property of the package. This property defines how many tasks can run simultaneously by specifying the maximum number of executables that can execute in parallel per package. The default value is -1, which equates to the number of physical or logical processors plus 2.
The EngineThreads property is a property of each Data Flow task. This property defines how many threads the data flow engine can create and run in parallel. The EngineThreads property applies equally to both the source threads that the data flow engine creates for sources and the worker threads that the engine creates for transformations and destinations. Therefore, setting EngineThreads to 10 means that the engine can create up to ten source threads and up to ten worker threads. The default is 5 in SQL Server 2005 and 10 in SQL Server 2008, with a minimum value of 2.
One other thing to consider: If you are using the Execute Package Task, the child package to be executed can be run in-process or out-of-process by use of the ExecuteOutOfProcess property. If a child package is executed out-of-process, you will see another dtshost.exe process start. These processes will remain “live”, using up resources, for quite a while after execution is complete.
If executing in-process, a bug in a task of the child package will cause the master package to fail. Not so if executing out-of-process. On 32-bit systems a process is able to consume up to 2GB of virtual memory. Executing out-of-process means each process can claim its own 2GB portion of virtual memory. Therefore if you are simply using many packages to structure your solution in a more modular fashion, executing in-process is probably the way to go because you don’t have the overhead of launching more processes.
More info:
Improving the Performance of the Data Flow
