# Mappings Published articles for Mappings. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## One field, one copy: How Elasticsearch columnar storage drops the inverted index DevFeed: [One field, one copy: How Elasticsearch columnar storage drops the inverted index]() Original publisher: [Read original article]() Author: Martijn van Groningen Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Elasticsearch Labs]() Topics: [elasticsearch](), [Data science and engineering]() Tags: [bits](), [cardinality](), [columnar](), [compress](), [compression](), [configuration](), [data-structure](), [delta-encoding](), [elasticsearch](), [index-data](), [inverted-index](), [lucene](), [mappings]() ### AI overview Elasticsearch 9.5.0 introduced columnar and logsdb_columnar index modes in technical preview. The article explains how these modes store fields as doc values, use bulk scanning and doc value skippers to support queries without indexing most fields by default, and add mapping controls for multi-valued and missing fields. It also covers tradeoffs in storage and query performance, with text fields remaining indexed by default. ### Source excerpt 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 DevFeed: [Taming PUNKs: How ES|QL queries Elasticsearch fields it was never told about]() Original publisher: [Read original article]() Author: Alexander Spies Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Elasticsearch Labs]() Topics: [elasticsearch](), [Query (disambiguation)]() Tags: [ai-agent](), [bugs](), [coverage](), [data](), [elasticsearch](), [es-ql](), [inside-elastic](), [mappings](), [query]() ### AI overview Elasticsearch 9.5 adds an ES|QL setting that lets queries load unmapped fields from `_source` or treat them as null. The article explains how the analyzer adds these fields to query plans, how the system handles type conflicts and preserves optimizations, and how spec and generative tests were used to check the feature. ### Source excerpt 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 DevFeed: [Skip the mapping explosion: ES|QL queries schemaless JSON keys without dynamic mapping]() Original publisher: [Read original article]() Author: Jordan Powers,Dima Leontyev Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Elasticsearch Labs]() Topics: [elasticsearch](), [JSON](), [json lines format]() Tags: [columnar](), [data](), [elasticsearch](), [es-ql](), [join](), [lucene](), [mappings](), [schema]() ### AI overview The article explains how Elasticsearch flattened fields store schemaless JSON under one mapping and how ES|QL's FIELD_EXTRACT can query dynamic keys. It describes query limitations and how extraction and predicates can use doc values and the inverted index, while noting that dynamic values remain keyword-typed unless mapped explicitly. ### Source excerpt 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 DevFeed: [Elasticsearch ES|QL brings full-text search to data you never indexed]() Original publisher: [Read original article]() Author: Kevin Corcoran,Ioana Tagirta Published: 2026-08-05T00:00:00Z Content type: article Language: en Sources: [Elasticsearch Labs]() Topics: [elasticsearch](), [search-engines](), [hybrid-search]() Tags: [elastic](), [elastic-cloud](), [elasticsearch](), [es-ql](), [mappings](), [search]() ### AI overview Elasticsearch 9.5 introduces MATCH and TO_TEXT for full-text searching of ES|QL expressions, including computed values, unmapped fields, keyword fields, and federated data without reindexing. Runtime matching analyzes data row by row and is available in Elastic Cloud Serverless, with a technical preview in Elasticsearch 9.5. Current limitations include filtering only, standard-analyzer use, and unsupported query options such as fuzziness. ### Source excerpt 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 DevFeed: [One field, every modality: how Elasticsearch's semantic field indexes and searches images, audio, video and PDFs automatically]() Original publisher: [Read original article]() Author: Mike Pellegrini Published: 2026-08-03T00:00:00Z Content type: tutorial Language: en Sources: [Elasticsearch Labs]() Topics: [elasticsearch](), [multimodal-ai](), [Embeddings](), [hybrid-search](), [Image](), [asr](), [FFmpeg (Fast Forward Moving Picture Experts Group)]() Tags: [elasticsearch](), [embeddings](), [images](), [index-data](), [jina-ai](), [mappings](), [multimodal](), [pdfs](), [semantic](), [vector-database](), [video]() ### AI overview Elasticsearch's semantic field automatically embeds text, images, audio, video, and PDFs at ingest time, enabling cross-modal search through one field. The tutorial explains index setup, querying with text or media, related search features, and ways to reduce input size and response overhead. The feature is available in Elasticsearch 9.5 and Serverless as a tech preview. ### Source excerpt 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.