In full-text search, analyzers, tokenizers, and filters play a crucial role in shaping the result of searches. An inverted index is used to efficiently store and retrieve data, allowing for fast matching of terms against large datasets. Tokenizers split text into words or smaller units, while filters remove irrelevant information and transform tokens into standardized forms. In the context of a Shazam-like app that uses Parsons code to identify music melodies, these components work together to convert user input (whistled notes) into a query term that can be matched against the indexed data. The choice of tokenizer and filter can significantly impact search results, highlighting the importance of selecting the right tools for the job.