Map-Reduce is the parallel workhorse of LangGraph. A `fan_out` node inspects the task and emits a `Send` object for each work item — in this case, one per book genre in the database. LangGraph schedules all `Send` targets to run concurrently, and each worker processes its own slice independently.
The key insight is the `Annotated[list, operator.add]` type on the results field in state. This reducer annotation tells LangGraph to *append* each worker's output to the list rather than overwriting it, which is essential for safely merging concurrent writes from many workers.
After all workers complete, a single `reducer` node receives the merged list and synthesizes the final answer. Map-Reduce is ideal for any task that can be decomposed into independent parallel units: per-document analysis, per-region statistics, or multi-source data fetching.