How does too many nodes in a process model increase memory footprint?

hi Yogi Patel  too many nodes need to handle many process variables that need to be stored and managed throughout the lifecycle of the process. If each node interacts with a large number of variables, or if there are many intermediate variables, this increases the memory footprint.

Appian allocates threads to handle node execution. More nodes can lead to higher thread usage, which consumes memory.

Managing the dependencies and interactions between the nodes may require additional memory.

each node generates log more number of nodes may require more logging data to be stored

Have you got a chance to look at this article?

How to Create Memory Efficient Models

Also as a simple answer I would say, More the number of nodes more the memory allocation to run the models efficiently. Your instances will consume more memory to keep up with the audit details such as your variable history or any other data logs. So smaller the model (Lesser nodes) better the performance. Converting your long models into smaller chunks and running them based on their priority as synchronous/asynchronous.

Abhishek Karumuru Konduru Chaitanya Thanks for your inputs.

Yes, the number of process variables and the data size will impact the memory.

While doing sequential processing, the new thread will be span for each node one by one i.e. once the execution of one node is completed, create a thread for another node. Hence, the number of nodes should not have a major impact on memory.

As mentioned by Konduru Chaitanya  and Abhishek Karumuru, a greater number of nodes will increase process history and other logistic data(log, etc.). However, I feel that it should not have much impact on memory since this data should not take much memory/space.