Building search experiences for JSON data with Amazon OpenSearch Service

This article demonstrates how to use Amazon OpenSearch Service as a discovery layer for JSON data, combining full-text, vector, and geospatial search in a single query using a restaurant discovery app example.

• OpenSearch Service indexes each JSON field independently with per-field strategies: text, vectors, keywords, or geospatial points.
• Nested objects and arrays preserve structure, enabling accurate queries across distinct items without collapsing boundaries.
• Schema is flexible yet controllable, supporting auto-mapping or strict validation without requiring migrations.
• Query language is JSON, mirroring document structure for readable and programmatically constructible complex queries.
• Functions as a discovery layer on top of DynamoDB, DocumentDB, Aurora, and S3 via Change Data Capture pipelines.
• Hybrid queries combine multi_match text search, neural vector search, and geo_distance filters with efficient inline filtering.
• Best practices include routing non-relevance constraints through cached filters, using multi-field mappings, and setting index:false for display-only fields.

OpenSearch Service provides transactional database guarantees alongside search intelligence, enabling context-aware results from richly structured data without specialized system integration.