🔍 Elasticsearch + Spring Boot Integration

Вместо простого full-text search, вы получаете мощную аналитическую платформу с real-time индексацией.

Spring Data Elasticsearch даёт полный набор возможностей: собственные анализаторы текста, агрегаты для аналитики и сводных выборок, подсветку найденных терминов, поиск с учётом опечаток, геозапросы по координатам, декларативный конструктор запросов, управление жизненным циклом индекса, поиск между несколькими кластерами, создание снимков и восстановление данных для резервного копирования, а также наблюдение и метрики через панели мониторинга в Kibana.

📝 Промпт:

Implement advanced Elasticsearch integration for Spring Boot 3 application:
— Configure Spring Data Elasticsearch: connection settings (cluster nodes, port 9200), RestHighLevelClient configuration, authentication (username/password or API key), SSL/TLS for secure connection, connection pool tuning (max connections, timeout).
— Create document mappings: @Document with index name and settings, @Field with type (text, keyword, date, nested), custom analyzers (standard, whitespace, ngram for autocomplete), tokenizers and filters, multi-field mappings for text/keyword.
— Implement repository layer: extend ElasticsearchRepository, custom query methods with naming conventions (findByTitleContaining), @Query annotation with JSON DSL, native search queries with NativeSearchQuery, pagination with Pageable.
— Add full-text search: multi-match queries across fields, boosting for relevance tuning (title^3, description^1), phrase matching, fuzzy search for typos (fuzziness=AUTO), highlighting with <em> tags, minimum_should_match parameter.
— Configure aggregations: terms aggregation for faceting, date histogram for time-based analytics, metrics (avg, sum, min, max), nested aggregations, bucket sorting, pipeline aggregations for calculations.
— Implement geo-spatial search: geo_point field type, geo_distance queries for radius search, geo_bounding_box for area search, geo_shape for complex polygons, distance sorting.
— Set up index management: index templates for consistent settings, index aliases for zero-downtime reindexing, ILM policies (hot/warm/cold/delete phases), rollover based on size/age, shrink for optimization.
— Add bulk operations: bulk indexing with BulkRequest for performance, batch size tuning (1000-5000 docs), refresh strategy (wait_for or async), error handling for partial failures, bulk processor with backoff.
— Configure search optimization: query cache for repeated queries, field data cache for aggregations/sorting, request cache for size=0 aggregation queries, index refresh interval (1s default, increase for write-heavy), force merge for read-only indices.
— Implement monitoring: cluster health API (green/yellow/red), node stats (JVM heap, disk usage), index stats (doc count, size), slow log for query/indexing analysis, Kibana dashboards with visualizations, alerting with Watcher.
Deliverables: ElasticsearchConfig.java, document entity classes with @Document, repository interfaces, search service with query builders, index templates JSON, ILM policies, Kibana dashboard exports, integration tests with Testcontainers

💡 Расширения:

— добавить learning to rank with LTR plugin;
— реализовать semantic search with dense vectors;
— настроить cross-cluster replication

🤌 Бонусы для подписчиков:
— Скидка 40% на все курсы Академии
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— Бесплатный тест на знание математики

🐸 Библиотека джависта

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🐸 Библиотека джависта

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