Data in: documents and indices

Elasticsearch Overview:

• Elasticsearch serves as a distributed document store.
• Stores complex data structures serialized as JSON documents.
• Documents are indexed and fully searchable in near real-time (within 1 second).
• Utilizes an inverted index for fast full-text searches.
• Each unique word in any document is listed in the inverted index, linking to the relevant documents.

Index and Document Structure:

• An index is an optimized collection of documents.
• Each document contains fields (key-value pairs) with data.
• Elasticsearch indexes all data in every field by default.
• Indexed fields have dedicated, optimized data structures (e.g., inverted indices, BKD trees).

Schema Flexibility:

• Elasticsearch is schema-less; documents can be indexed without explicit field definitions.
• Dynamic mapping automatically detects and adds new fields to the index.
• Easy exploration: Start indexing documents, and Elasticsearch maps data types (booleans, floats, dates, etc.) automatically.

Custom Mappings:

• Define your own mappings to take control:
• Distinguish between full-text and exact value string fields.
• Perform language-specific text analysis.
• Optimize fields for partial matching.
• Use custom date formats and non-detected data types (e.g., geo_point, geo_shape).

Flexible Indexing:

• Index the same field differently for various purposes (e.g., full-text search vs. sorting).
• Apply multiple language analyzers to process user input in string fields.

Analysis Chain:

• Full-text field analysis during indexing is also used at search time.
• Query text undergoes the same analysis before term lookup in the index.