spring-projects/spring-data-elasticsearch

Features

• Automated Data Repositories - Generates repository implementations from interface definitions to automate common data access operations.
• Enterprise Search - Decouples business logic from search infrastructure by using repository interfaces to manage documents.
• Elasticsearch Integrations - Maps application objects to Elasticsearch indices for CRUD operations and search queries.
• Class-Based Schema Mappings - Maps Java domain objects to Elasticsearch index schemas using class-level and field-level annotations.
• Cluster Connectivity - Establishes network connections to search clusters with support for SSL, timeouts, and authentication.
• CRUD Operations - Implements standard identifier-based CRUD operations for persisting and retrieving domain entities.
• Elasticsearch Connectors - Provides a comprehensive library for mapping Java objects to Elasticsearch and performing data operations.
• Elasticsearch Client Integrations - Provides a non-blocking, reactive client interface for asynchronous search and persistence operations.
• Full Text Search - Implements complex search functionality including highlighting, geospatial queries, and vector similarity.
• Document Retrieval Methods - Retrieves documents using dynamic finder methods and criteria with built-in support for sorting and pagination.
• Object-Document Mappers - Translates documents between JSON representations in Elasticsearch and plain Java objects using annotations.
• Custom Query Definitions - Allows overriding automatic query generation with custom declared queries to fine-tune search logic.
• Query Sorting - Applies dynamic pagination and sorting to search queries using pageable and sort parameters.
• Reactive Data Streams - Executes non-blocking data operations and streams search results as asynchronous sequences.
• Query Domain Specific Languages - Provides a domain-specific language for building complex search queries and aggregations without using raw JSON.
• Search Query Construction - Constructs complex search queries and update operations using a domain-specific language.
• Object Mappings - Maps Java objects to search index schemas using annotations or external configuration files.
• Query Method Derivation - Automatically generates retrieval logic based on method naming conventions to fetch specific documents.
• Object-to-Data Mapping Frameworks - Translates JSON documents stored in search indices into typed Java objects using annotation-based metadata.
• Query Derivation Frameworks - Generates search queries automatically by parsing keywords and property names from repository interface method signatures.
• Repository Proxy Implementations - Creates runtime implementations of data access interfaces using dynamic proxies to handle boilerplate database logic.
• Asynchronous Data Fetching - Returns search results as asynchronous streams or publishers to enable non-blocking application workflows.
• Bulk Document Retrievals - Fetches multiple documents by identifier and provides status for each individual request.
• Document CRUD Operations - Provides asynchronous create, read, update, and delete operations for documents mapped to search indices.
• Document Deletion Operations - Removes records matching a specific search query and returns metadata about the deletion process.
• Geospatial Data Types - Maps location-based types and geometries to Elasticsearch representations using standard geospatial specifications.
• Data Type Mappings - Registers rules to translate between Java domain types and Elasticsearch-specific data formats.
• JSON Query Executions - Enables the execution of specific JSON queries using positional placeholders or expressions for dynamic parameterization.
• Result Streaming - Processes large search datasets incrementally via data streams to minimize memory consumption.
• Result Set Scrolling - Iterates through chunks of a large dataset to avoid loading all data into memory.
• Search Index Management - Automates the creation and configuration of Elasticsearch indices and mappings via annotations.
• Automatic Index Generation - Automatically creates missing search indices at startup based on metadata derived from entity annotations.
• Point-in-Time Search - Creates consistent views of an index at a specific moment to ensure stable search results across requests.
• Index Lifecycle Management - Automates the lifecycle of search indices, including creation, deletion, and the management of mappings and aliases.
• Complex Search Querying - Implements complex search queries and aggregations by leveraging the full capabilities of the native Elasticsearch client.
• Search Templates - Runs pre-defined scripts stored on the Elasticsearch server to perform complex parameterized searches.
• Search Result Sorting - Orders search results using standard sorting, geographical distance, or custom rules for nested objects.
• Search Template Executions - Executes predefined search templates stored in the cluster by mapping method arguments to required parameters.
• Repository Fragment Composition - Adds custom logic to repositories by composing fragment interfaces with dedicated implementations.
• Vector Similarity Search - Implements k-nearest neighbor searches to find documents based on vector similarity.
• Search Result Highlighters - Marks specific fields in returned documents to highlight where the search terms were matched.
• Asynchronous Query Execution - Executes database queries in background tasks to ensure the main execution thread remains non-blocking.
• Exception Hierarchies - Maps provider-specific search engine error codes to a consistent hierarchy of generic exceptions.
• Repository Injection - Supports injecting data access components into application contexts using dependency injection to manage component lifecycles.
• Reactive Data Streams - Integrates non-blocking clients to return search results as asynchronous publishers for event-driven workflows.
• Result Type Mapping - Maps search results to primitives, collections, or custom wrappers to align with the application data model.

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