Amazon Neptune vs OpenSearch for Graph Queries

Graph queries - traversing relationships between entities - can be handled by both Neptune (a purpose-built graph database) and OpenSearch (which has graph-adjacent capabilities through nested documents and aggregations). The right choice depends on how central graph traversal is to your workload.

Overview

Graph Data Modeling

Neptune supports two graph models. Property graphs store vertices and edges with properties, queried via Gremlin or openCypher. RDF graphs store triples (subject-predicate-object), queried via SPARQL. Both models excel at representing and traversing complex relationships.

OpenSearch uses JSON documents. You can model relationships through nested objects, parent-child relationships, or denormalized documents. However, these are not true graph relationships. Multi-hop traversals (find all users connected to user A through three degrees of connection) are not feasible in OpenSearch.

Query Capabilities

Neptune handles complex graph queries efficiently: shortest path, connected components, centrality measures, pattern matching, and recursive traversals. A query like “find all entities within 3 hops of entity X that match criteria Y” is straightforward and performant.

OpenSearch excels at search, filtering, and aggregation across documents. It can answer “find all documents related to entity X” through term queries and aggregations, but cannot natively traverse relationship chains. Graph-like queries require multiple round-trips or denormalized data structures.

AI and Knowledge Graph Use Cases

Neptune is the natural choice for knowledge graphs that power AI applications. Entity resolution, recommendation engines based on relationship traversal, fraud detection through network analysis, and ontology-based reasoning all leverage Neptune’s graph capabilities. Neptune ML integrates with graph neural networks through SageMaker.

OpenSearch serves AI use cases centered on search and retrieval: RAG pipelines, semantic search, log analysis, and anomaly detection. Its vector search capabilities support embedding-based similarity queries that Neptune does not offer.

Combining Both

Some architectures use Neptune for relationship data and OpenSearch for search and vector capabilities. A knowledge graph in Neptune stores entity relationships. OpenSearch indexes the same entities for full-text search and vector similarity. A query layer combines results from both services.

When to Choose Neptune

Choose Neptune when your core use case is relationship traversal - knowledge graphs, social network analysis, fraud detection networks, recommendation engines based on graph patterns, or identity resolution. Neptune is essential when you need multi-hop queries, path analysis, or graph algorithms.

When to Choose OpenSearch

Choose OpenSearch when your primary needs are search, filtering, and aggregation rather than relationship traversal. If your “graph” requirements are limited to finding related documents or entities without deep traversal, OpenSearch’s document model is sufficient and avoids the operational overhead of a separate graph database.

Practical Recommendation

If you need to answer questions about relationships between entities (who is connected to whom, what is the shortest path, which entities form clusters), Neptune is the right tool. If you need to find entities by attributes, content, or vector similarity, OpenSearch is the right tool. For AI applications that need both relationship reasoning and semantic search, consider using both with a shared entity identifier scheme.