Implementing Japanese full-text search in Elasticsearch poses unique challenges due to the language's lack of whitespace word breaks, necessitating specialized analysis techniques. To address this, the blog explores using both n-gram and morphological analysis methods in tandem, with each compensating for the other's weaknesses. N-gram analysis, which separates text into fixed-length character sequences, can lead to bloated indices and increased noise, while morphological analysis relies on dictionaries to divide text into meaningful words, struggling with new or unknown words. By utilizing both techniques, a balance between precision and recall can be achieved, ensuring relevant search results. The blog details the configuration of Elasticsearch analyzers and mappings, showcasing the integration of the analysis-icu and analysis-kuromoji plugins to handle character normalization and synonyms. An example demonstrates how searches for terms like "東京大学" (University of Tokyo) can return comprehensive results, including synonyms like "東大" (a shorthand for Tokyo University). Overall, the blog provides a comprehensive guide to setting up Japanese full-text search with Elasticsearch, emphasizing the importance of tuning precision and recall based on specific use cases.