Building on our prior knowledge of search functionality, we will now delve into the mechanics of distributed search. The concept of distributed search is equivalent to parallelizing your search operations across multiple data storage systems or servers, which, in an analogy, would be akin to querying multiple databases in parallel to fetch data. This method enhances product performance while maximizing the utilization of resources.
To implement a distributed search, we would need a mechanism to split the search operation amongst multiple nodes and then aggregate the results. Let's illustrate this concept with our own version of a distributed search implementation using Python. Keep in mind how this echoes the principle of distributed computing in areas like AI, where tasks are divided and executed across multiple processing units.
The code example provided showcases a simplified approach, which under the hood in advanced systems usually involves more complex error handling, optimization, and synchronization aspects.
xxxxxxxxxxif __name__ == '__main__': class Distributed_Search: def __init__(self, servers): self.servers = servers def search(self, term): results = [] for server in self.servers: results.extend(server.search(term)) return results # Initiating servers server1 = Server() server2 = Server() # Adding documents to servers server1.add_document('Python is a great language for AI') server2.add_document('Finance applications can also use Python') dist_search = Distributed_Search([server1, server2]) print(dist_search.search('Python'))