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

JanusGraph vs Titan

OverviewComparisonAlternatives

Overview

Titan
Titan
Stacks38
Followers56
Votes0
JanusGraph
JanusGraph
Stacks43
Followers96
Votes0

JanusGraph vs Titan: What are the differences?

Introduction

JanusGraph and Titan are both graph databases that are designed for storing and traversing highly connected data. While they share similarities, there are several key differences between the two.

  1. Data Model: JanusGraph utilizes a labelled property graph data model, which consists of vertices and edges that can have arbitrary properties. On the other hand, Titan uses a similar data model but refers to vertices as "nodes" and edges as "relationships".

  2. Scalability: JanusGraph is built to support horizontal scalability, allowing users to distribute data across multiple machines and handle large-scale workloads. In contrast, Titan's scalability capabilities are more limited and it is primarily designed for single-server deployments.

  3. Backends: JanusGraph supports a wide range of backends for storing data, including Apache Cassandra, Apache HBase, Google Cloud Bigtable, and others. Titan, on the other hand, primarily relies on Apache Cassandra for storage but also offers support for other backends like Apache HBase and Oracle BerkeleyDB.

  4. Development and Maintenance: JanusGraph is an actively maintained open-source project that is led by a diverse community of contributors. It is a fork of Titan and includes various bug fixes, performance improvements, and additional features that were not present in the original Titan. Titan, on the other hand, is no longer actively maintained and users are encouraged to migrate to JanusGraph for ongoing support and development.

  5. Transaction Management: JanusGraph supports a more advanced transaction management system compared to Titan. It provides features like ACID transactions, isolation levels, and concurrent access control, making it suitable for applications that require strong consistency and data integrity. Titan, on the other hand, primarily relies on eventual consistency and does not offer the same level of transactional support.

  6. Query Language: While both JanusGraph and Titan use the popular Apache TinkerPop Gremlin query language, JanusGraph also supports a native query language called JanusGraph Query Language (JGQL). JGQL provides a more expressive and efficient way to query the graph data in JanusGraph, allowing users to write complex queries with ease. Titan, on the other hand, primarily relies on Gremlin for querying the data.

In Summary, JanusGraph offers a more scalable, actively maintained, and feature-rich alternative to Titan for storing and querying graph data, with support for a diverse range of backends and advanced transaction management capabilities.

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Detailed Comparison

Titan
Titan
JanusGraph
JanusGraph

Titan 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. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.

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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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
38
Stacks
43
Followers
56
Followers
96
Votes
0
Votes
0
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 Titan, JanusGraph?

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.

Dgraph

Dgraph

Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.

RedisGraph

RedisGraph

RedisGraph is a graph database developed from scratch on top of Redis, using the new Redis Modules API to extend Redis with new commands and capabilities. Its main features include: - Simple, fast indexing and querying - Data stored in RAM, using memory-efficient custom data structures - On disk persistence - Tabular result sets - Simple and popular graph query language (Cypher) - Data Filtering, Aggregation and ordering

Cayley

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.

Blazegraph

Blazegraph

It is a fully open-source high-performance graph database supporting the RDF data model and RDR. It operates as an embedded database or over a client/server REST API.

Graph Engine

Graph Engine

The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set.

FalkorDB

FalkorDB

FalkorDB is developing a novel graph database that revolutionizes the graph databases and AI industries. Our graph database is based on novel but proven linear algebra algorithms on sparse matrices that deliver unprecedented performance up to two orders of magnitude greater than the leading graph databases. Our goal is to provide the missing piece in AI in general and LLM in particular, reducing hallucinations and enhancing accuracy and reliability. We accomplish this by providing a fast and interactive knowledge graph, which provides a superior solution to the common solutions today.

TypeDB

TypeDB

TypeDB is a database with a rich and logical type system. TypeDB empowers you to solve complex problems, using TypeQL as its query language.

Memgraph

Memgraph

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop applications you never thought possible in days, not months.

Akutan

Akutan

A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.

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