Elasticsearch Query DSL Overview: Queries, Filters, and Advanced Types

After understanding mapping and analysis in Elasticsearch, users often focus on constructing query statements to retrieve the desired data; this article introduces the Query DSL that serves this purpose.

Query DSL (Query Domain Specific Language) is a language dedicated to querying Elasticsearch, with two main categories: leaf query clauses that match specific field values, and compound query clauses that combine multiple simple or compound clauses logically.

The behavior of a query depends on its context: in filter context, a document either matches or not, and results are cached for high efficiency; in query context, relevance scores (_score) are calculated based on the proportion of matched terms.

Basic match‑all queries include match_all (matches all documents) and match_none (matches none).

Full‑text queries analyze the query string before execution and include match , match_phrase , match_phrase_prefix , multi_match , common_terms , query_string , and simple_query_string .

Term‑level queries operate directly on exact terms stored in the inverted index and cover term , terms , terms_set , range , exists , prefix , wildcard , regexp , fuzzy , type , and ids .

Compound queries combine other queries and can modify scoring or switch contexts; examples are constant_score , bool , dis_max , function_score , and boosting .

Joining queries, which avoid costly SQL‑style joins in a distributed system, include nested , has_child / has_parent , and parent_id .

Geo queries support two data types— geo_point and geo_shape —and provide geo_shape , geo_bounding_box , geo_distance , and geo_polygon queries.

Specialized queries not covered elsewhere are more_like_this , script , percolate , and wrapper .

Span queries offer low‑level control over term order and proximity, useful for legal or patent documents; they include span_term , span_multi , span_first , span_near , span_or , span_not , span_containing , span_within , and field_masking_span .

The article concludes that this overview gives a general impression of Elasticsearch's query capabilities, and readers should refer to the official documentation for detailed specifics.