So far, we've discussed building a basic search functionality that operates on a single document store. However, in a real-world scenario, data is often distributed across multiple servers or databases. To further enhance our search engine, we need to introduce the concept of distributed search.

In the realm of search engines, a distributed search refers to searching across multiple data sources or servers simultaneously. For data-intensive fields like finance and AI, this is particularly crucial. It's akin to searching for a desired book in multiple libraries at once rather than just one, thereby significantly broadening the search scope and optimising the search process. Just like how a diversified investment portfolio can reduce risk and increase returns in finance, a distributed search engine can provide us with wider search coverage and more comprehensive results.

However, this also introduces additional complexities and challenges. Communication between multiple servers, data consistency, query optimization and fault tolerance are all vital considerations when designing a distributed search system. For instance, just like how AI models need to deal with the challenge of coordinating multiple processors for parallel computing, we are to handle communication and synchronization between multiple servers in a distributed search environment. Moving forward, we'll discuss the details of how to implement a distributed search.