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ArangoDB vs Cayley: What are the differences?

# Key Differences between ArangoDB and Cayley

ArangoDB and Cayley are both popular choices for graph databases, each with its set of features and strengths. Here are the key differences between ArangoDB and Cayley:

1. **Data Model**: ArangoDB is a multi-model database, supporting document, key/value, and graph data models, offering more flexibility in data representation. On the other hand, Cayley is a graph database focused solely on graph data, providing more specialized functionality for handling connected data.

2. **Query Language**: ArangoDB uses the AQL (ArangoDB Query Language), a SQL-like query language that is powerful and expressive, making it easier to work with complex queries. In contrast, Cayley uses Gremlin, a graph traversal language used in the graph database world, which may have a steeper learning curve for users unfamiliar with graph databases.

3. **Scalability**: ArangoDB is known for its scalability with its cluster and sharding capabilities, allowing users to distribute data across multiple servers for improved performance and availability. Cayley, on the other hand, may have limitations in scaling to large datasets due to its design constraints.

4. **Community and Ecosystem**: ArangoDB has a strong community and ecosystem, with a variety of plugins, extensions, and integrations available, providing users with additional tools and functionalities. In comparison, Cayley's community and ecosystem may be smaller in size, potentially limiting the availability of resources and support.

5. **Consistency Model**: ArangoDB supports multi-document ACID transactions, providing transactional guarantees for complex operations on the database. In contrast, Cayley follows a more relaxed consistency model, focusing on eventual consistency and trading off strong consistency for improved performance in distributed systems.

6. **Use Cases**: ArangoDB is suitable for a wide range of use cases, including social networks, recommendation systems, and content management systems, due to its multi-model capabilities. Cayley, on the other hand, is ideal for applications that heavily rely on graph data, such as knowledge graphs, network analysis, and semantic web applications.

In Summary, ArangoDB and Cayley differ in their data models, query languages, scalability, community support, consistency models, and use cases, offering users varied options depending on their specific requirements.
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Pros of ArangoDB
Pros of Cayley
  • 37
    Grahps and documents in one DB
  • 26
    Intuitive and rich query language
  • 25
    Good documentation
  • 25
    Open source
  • 21
    Joins for collections
  • 15
    Foxx is great platform
  • 14
    Great out of the box web interface with API playground
  • 6
    Good driver support
  • 6
    Low maintenance efforts
  • 6
  • 5
    Easy microservice creation with foxx
  • 4
    You can write true backendless apps
  • 2
    Managed solution available
  • 0
  • 7
    Full open source

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Cons of ArangoDB
Cons of Cayley
  • 3
    Web ui has still room for improvement
  • 2
    No support for blueprints standard, using custom AQL
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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 Cayley?

    Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned.

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    What companies use ArangoDB?
    What companies use Cayley?
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      What tools integrate with ArangoDB?
      What tools integrate with Cayley?
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        What are some alternatives to ArangoDB and Cayley?
        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 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 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.
        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.
        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.
        See all alternatives