What is an example of a distributed graph database?

An example of a distributed graph database is JanusGraph, an open-source system designed to handle large-scale graph data across multiple machines. JanusGraph separates storage and query processing, allowing it to scale horizontally by distributing data and computation. It relies on distributed storage backends like Apache Cassandra, Google Cloud Bigtable, or Apache HBase to manage data persistence, while its query engine uses Apache TinkerPop’s Gremlin traversal language for graph operations. This architecture enables JanusGraph to support high availability, fault tolerance, and the ability to process complex graph queries efficiently across clusters.

JanusGraph’s distributed nature is evident in its storage layer. For instance, when using Apache Cassandra as the backend, data is partitioned and replicated across nodes in a cluster, ensuring resilience and scalability. Each node in the graph (e.g., users, products) and each connection (e.g., “follows” or “purchased”) is stored as key-value pairs, which Cassandra distributes based on partitioning strategies. This setup allows JanusGraph to handle billions of nodes and edges, making it suitable for applications like social networks, recommendation engines, or fraud detection systems. Additionally, its integration with Apache Spark enables distributed graph analytics, such as calculating page ranks or community detection, across large datasets.

Another example is Amazon Neptune, a fully managed graph database service from AWS. Neptune is distributed by default, replicating data across multiple Availability Zones (AZs) within a region to ensure durability and availability. It supports both the Property Graph and RDF models, with query languages like Gremlin and SPARQL. Neptune’s storage layer automatically partitions data and scales read replicas to handle workload spikes. For developers, this eliminates the operational overhead of managing clusters while providing low-latency queries for connected data. Use cases include knowledge graphs, real-time recommendation systems, and network dependency analysis. Both JanusGraph and Neptune illustrate how distributed graph databases balance scalability, performance, and flexibility for modern applications.