Parallel Processing
Run independent work at the same time — fan out, then bring the results back together.
By default a workflow follows its connections in order. When steps don't depend on each other, run them in parallel to finish faster.
Fork & Join
- Fork splits the flow into multiple branches that all execute concurrently.
- Join waits for the branches and merges their results before continuing.
Use this when you have several independent tasks — call three APIs at once, process multiple inputs together, or generate several variations side by side. See the Fork node, which also covers Join.
Loop over many items
Hand a list — rows from a Sheet or files from a folder — to a Loop to process each item through the same steps. Combined with multiple servers, items can be spread across machines.
Scale across machines
Pair parallel branches with a compute cluster: each branch can run on a different machine, so heavy data-science workloads finish in a fraction of the time.
Tips
- Only parallelize steps that are truly independent — shared state between branches causes surprises.
- Use Join to synchronize before any step that needs all the results.