When a match query is executed in Elasticsearch, it analyzes the search term to generate a list of terms that are used to search the index. The query then returns any documents that contain at least one of the generated terms.
The matching process involves several steps:
1. Analysis: The search term is analyzed to generate a list of terms that will be used to search the index. This involves breaking the term into individual words, removing stop words, and performing other text processing tasks based on the analyzer configured for the field.
2. Term matching: The generated terms are used to search the inverted index, which maps each term to the documents that contain it. The query returns any documents that contain at least one of the generated terms.
3. Scoring: Each document that matches the query is assigned a relevance score based on how well it matches the search term. The relevance score takes into account factors such as the frequency of the term in the document and the field, and the length of the field.
4. Sorting: The search results are sorted based on their relevance score, with the most relevant results appearing at the top of the list.
By default, the match query is a “fuzzy” query, meaning that it can handle small spelling mistakes and variations in the search term. This is achieved through the use of techniques such as stemming and fuzzy matching, which allow the query to match similar but not identical terms.
Overall, the match query provides a powerful and flexible way to search for specific terms or phrases in Elasticsearch, while taking into account the complexities of natural language text.