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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. JanusGraph vs OrientDB

JanusGraph vs OrientDB

OverviewComparisonAlternatives

Overview

OrientDB
OrientDB
Stacks77
Followers107
Votes14
JanusGraph
JanusGraph
Stacks43
Followers96
Votes0

JanusGraph vs OrientDB: What are the differences?

Introduction

In the realm of graph databases, JanusGraph and OrientDB are among the top contenders, each offering distinct features and functionalities. Below are key differences between these two databases that would help users make an informed decision.

  1. Data Modeling Flexibility: JanusGraph provides users with a schema-optional approach, allowing for flexible and dynamic data modeling. On the other hand, OrientDB enforces a schema-based model, requiring users to define the schema beforehand. This makes JanusGraph more suitable for scenarios where the data structure may evolve over time or require a more agile approach.

  2. Scalability: JanusGraph is specifically designed for distributed graph processing, enabling horizontal scaling across multiple servers seamlessly. In contrast, while OrientDB supports clustering and replication for scalability, it is not as inherently optimized for massive scalability compared to JanusGraph. Thus, JanusGraph is better suited for handling large and growing datasets efficiently.

  3. Consistency Model: JanusGraph follows the TinkerPop framework, which employs the Apache TinkerPop Gremlin query language for graph traversal. In contrast, OrientDB uses its own query language and follows the ACID properties for data consistency within transactions. Depending on the specific requirements of the application, the choice between a Gremlin-based system like JanusGraph or a SQL-like system like OrientDB can greatly impact development and query optimization strategies.

  4. Supported APIs and Integrations: JanusGraph supports various graph and NoSQL databases as storage backends, providing users with flexibility in choosing the most suitable data store for their needs. OrientDB, on the other hand, is an all-in-one solution that combines graph database capabilities with document and object-oriented database features. This makes OrientDB a convenient choice for applications requiring a versatile database with diverse data models.

  5. Community and Support: JanusGraph is an open-source project maintained by the Linux Foundation, with active contributions from a wide community of developers and users. In contrast, while OrientDB is also open-source, it is primarily developed and supported by OrientDB Ltd., which may impact the availability of resources and community-driven features for users. Depending on the preference for community-driven development or commercial support, users may lean towards one platform over the other.

  6. Performance Optimization: JanusGraph places a strong emphasis on optimizing graph traversals and queries, making it well-suited for applications requiring complex graph analytics and traversals. OrientDB, with its multi-model capabilities, may offer superior performance for applications that primarily rely on document or relational data models. Choosing the right tool depends on the specific performance requirements of the application, be it graph-centric or multi-model in nature.

In Summary, the choice between JanusGraph and OrientDB boils down to data modeling flexibility, scalability needs, consistency models, supported APIs, community support, and performance optimization based on the specific requirements of the application.

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Detailed Comparison

OrientDB
OrientDB
JanusGraph
JanusGraph

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.

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.

-
Elastic and linear scalability for a growing data and user base; Data distribution and replication for performance and fault tolerance; Multi-datacenter high availability and hot backups; Support for ACID and eventual consistency; Support for various storage backends: HBase, Cassandra, Bigtable, DynamoDB, BerkeleyDB; Support for global graph data analytics, reporting, and ETL through integration with big data platforms: Spark, Giraph, Hadoop; Support for geo, numeric range, and full-text search via: ElasticSearch, Solr, Lucene; Native integration with the Apache TinkerPop graph stack; Open source under the Apache 2 license
Statistics
Stacks
77
Stacks
43
Followers
107
Followers
96
Votes
14
Votes
0
Pros & Cons
Pros
  • 4
    Great graphdb
  • 2
    Open source
  • 2
    Great support
  • 1
    Highly-available
  • 1
    Rest api
Cons
  • 4
    Unstable
No community feedback yet
Integrations
No integrations available
Apache Spark
Apache Spark
Amazon DynamoDB
Amazon DynamoDB
Cassandra
Cassandra
Apache Solr
Apache Solr
ScyllaDB
ScyllaDB

What are some alternatives to OrientDB, JanusGraph?

MongoDB

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.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

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.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

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.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

Neo4j

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.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

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