Exploring Search Functionality Using OpenSearch Analyzer

OpenSearch is a NoSQL-based search engine derived from Elasticsearch, providing powerful document search and indexing capabilities using Apache Lucene. It stores JSON-based documents and can flexibly respond to changes in data structure through dynamic schema and mapping settings.

Its core feature, Analyzer, splits input data into token units according to specific rules for indexing, improving search accuracy and efficiency. When data is loaded, it goes through the Analyzer to be tokenized before indexing, and when searching, documents are retrieved based on these tokens as well.

Field type configuration is important for efficient search.

• keyword: Indexes the original data as-is, suitable for aggregation, filtering, and sorting.
• text: Split into token units through the Analyzer for indexing, used when sophisticated search is needed.
• date, integer, etc.: Indexed as-is without separate tokenization, used for range searches and similar.

Search quality can be optimized by configuring a custom Analyzer that combines char_filter (removing whitespace/special characters), tokenizer (ngram, etc.), and filter (lowercase conversion) according to the user's search requirements.