- Split the data processed into smaller chunks. For example, instead of fetching 10,000 rows with each execution, process 200 rows with each execution.
- Avoid using the Code node where possible.
- Avoid manual executions when processing larger amounts of data.
- Split the workflow up into sub-workflows and ensure each sub-workflow returns a limited amount of data to its parent workflow.
Splitting the workflow might seem counter-intuitive at first as it usually requires adding at least two more nodes: the Loop Over Items node to split up the items into smaller batches and the Execute Workflow node to start the sub-workflow.
However, as long as your sub-workflow does the heavy lifting for each batch and then returns only a small result set to the main workflow, this reduces memory consumption. This is because the sub-workflow only holds the data for the current batch in memory, after which the memory is free again.