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ArangoDB vs JanusGraph: What are the differences?
- Data Model: ArangoDB is a multi-model database that supports key/value, document, and graph data models natively, allowing users to mix and match these data models within a single query. In contrast, JanusGraph is a graph database that focuses solely on graph data modeling, making it more specialized for graph-related operations.
- Native Graph Processing: ArangoDB provides built-in graph processing capabilities, allowing users to run graph algorithms and traversals directly within the database engine. On the other hand, JanusGraph relies on external graph processing frameworks like Apache Spark or Apache Giraph for advanced graph analytics.
- Consistency Models: ArangoDB offers different consistency models such as full, sync, and linearizable to cater to various application requirements. JanusGraph primarily follows the eventual consistency model, which may lead to slightly outdated or conflicting data during network partitions or failures.
- Scalability: ArangoDB supports horizontal scalability through cluster configurations, enabling users to distribute data across multiple nodes for high availability and performance. JanusGraph also supports scaling out by utilizing distributed storage backends like Apache Cassandra or HBase for storing graph data.
- Deployment Options: ArangoDB can be deployed as a standalone server, a cluster setup, or in a distributed environment based on user needs. JanusGraph, being an open-source project, provides flexibility in deployment options but requires additional setup and configuration for distributed deployments compared to ArangoDB.
- Query Language: ArangoDB uses its query language called AQL (ArangoDB Query Language) that supports SQL-like syntax for querying data across different data models. In contrast, JanusGraph follows the Gremlin query language, which is a graph traversal language for querying graph databases.
In Summary, ArangoDB offers multi-model flexibility, built-in graph processing, various consistency models, scalability options, versatile deployment choices, and a unique AQL query language, while JanusGraph specializes in graph data modeling, relies on external graph processing frameworks, follows an eventual consistency model, supports scaling with distributed storage backends, requires specific setup for deployment, and uses the Gremlin query language.
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Pros of JanusGraph
Pros of ArangoDB
- Grahps and documents in one DB37
- Intuitive and rich query language26
- Good documentation25
- Open source25
- Joins for collections21
- Foxx is great platform15
- Great out of the box web interface with API playground14
- Good driver support6
- Low maintenance efforts6
- Clustering6
- Easy microservice creation with foxx5
- You can write true backendless apps4
- Managed solution available2
- Performance0
Pros of JanusGraph
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Cons of ArangoDB
Cons of JanusGraph
Cons of ArangoDB
- Web ui has still room for improvement3
- No support for blueprints standard, using custom AQL2
Cons of JanusGraph
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What is ArangoDB?
A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
What is JanusGraph?
It is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.
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What companies use ArangoDB?
What companies use JanusGraph?
What companies use ArangoDB?
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What tools integrate with ArangoDB?
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What are some alternatives to ArangoDB and JanusGraph?
Neo4j
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
PostgreSQL
PostgreSQL is an advanced object-relational database management system
that supports an extended subset of the SQL standard, including
transactions, foreign keys, subqueries, triggers, user-defined types
and functions.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
OrientDB
It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records.