Key New Features and Changes in Elasticsearch 8.0 Release
Elasticsearch 8.0 introduces major updates including 7.x REST API compatibility headers, default‑enabled security with registration tokens, protected system indices, a preview KNN search API, storage‑efficient keyword/match_only_text/text fields, faster indexing for geo_point and geo_shape, PyTorch model support, and numerous deprecations and enhancements across aggregations, allocation, analysis, authentication, cluster coordination, and engine components.
7.x REST API Compatibility
Elasticsearch 8.0 adds optional headers that allow 7.x‑compatible requests and responses, making the upgrade path smoother while still encouraging developers to move to native 8.0 APIs.
Security Features Enabled by Default
Security (authentication, authorization, TLS) is now enabled and configured automatically on first start. A registration token is generated for connecting Kibana or adding nodes without manual certificate handling.
Known issue on ARM/macOS M1: the elastic user password and Kibana token are not generated automatically; reset them with bin/elasticsearch-reset-password -u elastic and create a token with bin/elasticsearch-create-enrollment-token -s kibana .
Better Protection of System Indices
Direct access to system indices now requires the allow_restricted_indices setting to be set to true . The built‑in superuser role no longer has write privileges on system indices, and the elastic superuser cannot modify them.
New KNN Search API (Technical Preview)
The KNN search API uses the dense_vector field to perform approximate nearest‑neighbor searches, enabling faster, scalable similarity queries for recommendation and NLP use‑cases.
Storage‑Efficient Keyword, match_only_text, and text Fields
Updated inverted index encoding reduces index size for keyword , match_only_text , and text fields; a benchmark on the message field showed a 14.4% reduction for the field and a 3.5% overall disk‑space saving.
Faster Indexing for geo_point, geo_shape, and Range Fields
Multi‑dimensional point indexing speed for geo_point , geo_shape , and range fields is improved by 10‑15% in Lucene‑level benchmarks.
PyTorch Model Support for NLP
Users can now upload PyTorch models trained outside Elasticsearch and run inference, bringing modern NLP capabilities to the Elastic Stack.
Other Notable Changes
Aggregations : removed adjacency matrix, MovingAverage pipeline, deprecated _time/_term sorting, and date‑histogram interval.
Allocation : removed include_relocations setting.
Analysis : cleaned up versioned deprecations, removed delimited_payload_filter .
Authentication : always add file and native realms unless disabled; default NameID format not set in policies; enforced realm order configuration.
Cluster Coordination : removed connection timeout and delayed state recovery support.
Distributed : removed sync refresh and cluster.remote.connect setting.
Engine : disallowed only_expunge_deletes and max_num_segments on force‑merge; removed per‑type index stats and translog retention settings.
Features/CAT APIs : deprecated local parameter for _cat/indices and _cat/shards .
Features/ILM+SLM : defaulted cluster.routing.allocation.enforce_default_tier_preference to true .
Features/Indices APIs : defaulted prefer_v2_templates to true , removed deprecated _upgrade API, removed include_type_name from REST, and removed template field from index templates.
Infra/Core : removed nodes/0 folder prefix, bootstrap.system_call_filter , node.max_local_storage_nodes , Joda dependency, and camel‑case date/time format names.
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