# Building a Book Recommendation Pipeline with LangGraph’s MapReduce Pattern > Source: > Published: 2026-07-28 06:33:21+00:00 Member-only story # Building a Book Recommendation Pipeline with LangGraph’s MapReduce Pattern **MapReduce** is a way to process a list of items and combine the results into a single output. The idea has two steps: **Map**: Process each item in the list separately.** Reduce**: Combine all the individual results into one final result. This pattern is commonly used in distributed systems, but it is also useful in LLM applications. In an LLM workflow, the **map** step often means taking a list generated by one model call and running a separate model call (or API call) for each item. The **reduce** step then combines all those results with a final model call. In **LangGraph**, this pattern is implemented using **Send**. In this article, I will build a simple **book recommendation workflow** to show *how **Send** distributes work across multiple parallel tasks and then combines the results into a final output.* ## The Problem with a Fixed Graph A standard LangGraph graph has a **fixed** set of nodes and edges. This works well when the number of steps is known **in advance**. It becomes difficult when the number of steps depends on the data received at** runtime**. For example — suppose a user asks for book recommendations on the **history of the Football World Cup**. The first LLM call may return 2 books, 4 books, or even 10. The graph doesn’t know this until that call finishes.