JanusGraph
Scalable, distributed open-source graph database
Founded
2017
Starting Price
Free
Category
Developer Tools
Last updated March 8, 2026
About JanusGraph
JanusGraph is a scalable, distributed graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges across a multi-machine cluster. A Linux Foundation project, it supports Apache TinkerPop and the Gremlin graph traversal language, with pluggable storage backends including Apache Cassandra, Apache HBase, and Google Cloud Bigtable.
Pros & Cons
Pros
- Highly scalable for massive graphs with billions of vertices and edges
- Flexible pluggable storage backends let you choose the best fit for your infrastructure
- Fully open-source with no licensing costs under Apache 2.0
- Strong ACID transaction support for real-time workloads
- Backed by the Linux Foundation with contributions from IBM, Google, and Amazon
Key Features
Massive Scalability
Handles graphs with hundreds of billions of vertices and edges distributed across a multi-machine cluster
Pluggable Storage Backends
Supports Apache Cassandra, Apache HBase, Google Cloud Bigtable, and Oracle BerkeleyDB as storage backends
Gremlin Query Language
Native support for Apache TinkerPop and the Gremlin graph traversal language for expressive graph queries
ACID Transactions
Supports ACID-compliant transactional processing for real-time OLTP workloads with data consistency
Full-Text & Geo Search
Integrates with Elasticsearch, Solr, and Lucene for full-text, geo, and numeric range search on graph data
OLAP with Apache Spark
Supports global graph analytics (OLAP) through Apache Spark integration for large-scale batch processing
Horizontal Write Scaling
Write throughput scales near-linearly with Cassandra or ScyllaDB clusters using appropriate partitioning
Pricing
Open Source
Free
- Full graph database functionality
- All storage backend support
- Gremlin query language
- ACID transactions
- Full-text and geo search
Best For
Knowledge Graphs
Build and query enterprise-scale knowledge graphs connecting entities, relationships, and metadata across massive datasets
IoT & Metadata Graphs
Handle ingestion-heavy workloads from IoT sensors and device metadata with horizontal write scaling
Fraud Detection
Traverse complex relationship networks to identify fraudulent patterns and suspicious connections in financial data
Social Network Analysis
Model and analyze large-scale social networks with complex relationship traversals and community detection
