Elasticsearch

1. Data Store: Indices, Documents and Mappings

   Documents, indices, JSON storage, near-real-time search and the data store overview.

2. Mapping and Field Types

   Explicit versus dynamic mapping, field types, keyword vs text, multi-fields and mapping explosion.

3. Text Analysis

   Analyzers, tokenizers, token filters, normalizers and index-time vs search-time analysis.

4. Ingest and Pipelines

   Bulk indexing, ingest pipelines, processors and data enrichment.

5. Full-Text Search and Relevance

   Match queries, BM25 relevance scoring, boosting and relevance tuning.

6. Query DSL

   Leaf and compound queries, bool, filter context versus query context, term-level queries.

7. Aggregations

   Bucket, metric and pipeline aggregations, analytics over documents.

8. ES|QL

   Piped query language for filtering, transforming and aggregating data.

9. Vector and Semantic Search

   dense_vector, kNN search, embeddings, semantic_text and hybrid search.

10. Clusters, Nodes and Shards

    Primary and replica shards, node roles, shard allocation, cluster state and resilience.

11. Data Streams

    Time-series append-only data, backing indices, rollover and data stream design.

12. Index Lifecycle Management

    Hot, warm, cold and frozen tiers, rollover, retention and ILM policies.

13. Snapshot and Restore

    Repositories, snapshot lifecycle and cluster recovery.

14. Security

    Authentication, role-based access control, TLS and API keys.

Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.

Source: endoflife.date

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