One field, one copy: How Elasticsearch columnar storage drops the inverted index
Storing each field once means no inverted index, so doc values now read in bulk and skippers let queries skip whole ranges of documents, while new mapping attributes control what each field is allowed to contain.
Taming PUNKs: How ES|QL queries Elasticsearch fields it was never told about
In Elasticsearch 9.5, ES|QL can query unmapped fields. It reads them from _source or returns nulls, so a query keeps working when a field drops out of the mapping and you avoid a reindex that takes hours.
Skip the mapping explosion: ES|QL queries schemaless JSON keys without dynamic mapping
Flattened fields turn Elasticsearch into a schema-on-read store where you index schemaless data under one mapping, then use ES|QL's FIELD_EXTRACT to pull out any JSON key you need to filter, group or join on, with predicates pushed into the columnar store.
Elasticsearch ES|QL brings full-text search to data you never indexed
MATCH and TO_TEXT bring full-text search to data you never indexed. Search computed columns, unmapped fields and federated sources in ES|QL.
One field, every modality: how Elasticsearch's semantic field indexes and searches images, audio, video and PDFs automatically
The semantic field turns images, audio, video, PDFs and text into multimodal embeddings at ingest time. Describe a scene and find the matching image or use a video frame to surface related clips, all from one Elasticsearch field.
How to display fields of an Elasticsearch index
Learn how to display fields of an Elasticsearch index using the _mapping and _search APIs, sub-fields, synthetic _source, and runtime fields.
Mapping embeddings to Elasticsearch field types: semantic_text, dense_vector, sparse_vector
Discussing how and when to use semantic_text, dense_vector, or sparse_vector, and how they relate to embedding generation.