Neo4j vs Titan

Overview

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Titan

Stacks38

Followers56

Votes0

Neo4j vs Titan: What are the differences?

Introduction

In this article, we will compare Neo4j and Titan, two popular graph databases, and highlight their key differences.

Data Model: Neo4j follows a property graph model, where data is represented in the form of nodes, relationships, and properties. Titan, on the other hand, uses a distributed graph model, which enables scalability across multiple machines for handling large datasets efficiently.
Scalability: While Neo4j can scale vertically by adding more resources to a single machine, it has limitations in terms of horizontal scalability. Titan, being a distributed graph database, can scale horizontally by adding more machines to the cluster, making it suitable for handling large-scale graph datasets.
Storage Backend: Neo4j uses a native graph storage backend, where data is stored on disk in a proprietary format optimized for graph operations. Titan, on the other hand, supports multiple storage backends, including Apache Cassandra, Apache HBase, and others, making it more flexible in terms of storage options.
Query Language: Neo4j uses the Cypher query language, which is a powerful and expressive language specially designed for querying and manipulating graph data. Titan, on the other hand, supports the Apache TinkerPop Gremlin query language, which is a general-purpose graph traversal language that works across different graph databases.
Consistency vs Availability: Neo4j focuses on strong consistency, ensuring that all reads and writes to the database reflect the latest state of the data. Titan, on the other hand, emphasizes availability, trading off some degree of consistency for improved scalability and fault tolerance.
Community and Ecosystem: Neo4j has a larger and more mature community, with a wide range of plugins, tools, and resources available for developers. Titan, although it has a smaller community, benefits from being part of the Apache Software Foundation, and therefore has the advantage of being associated with other popular and widely used projects.

In summary, Neo4j is a property graph database with a strong community and emphasis on consistency, while Titan is a distributed graph database with flexible storage options, horizontal scalability, and focus on availability.

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Advice on Neo4j, Titan

Jaime

none at none

Aug 31, 2020

Needs advice

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

56.4k views56.4k

Comments

Detailed Comparison

Neo4j	Titan
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.	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.
intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API	-
Statistics
GitHub Stars 15.3K	GitHub Stars -
GitHub Forks 2.5K	GitHub Forks -
Stacks 1.2K	Stacks 38
Followers 1.4K	Followers 56
Votes 351	Votes 0
Pros & Cons
Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost	No community feedback yet

What are some alternatives to Neo4j, Titan?

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 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 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

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

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 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.

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.

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 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

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.

Related Comparisons

Neo4j vs Titan: What are the differences?

Introduction

In this article, we will compare Neo4j and Titan, two popular graph databases, and highlight their key differences.

Data Model: Neo4j follows a property graph model, where data is represented in the form of nodes, relationships, and properties. Titan, on the other hand, uses a distributed graph model, which enables scalability across multiple machines for handling large datasets efficiently.
Scalability: While Neo4j can scale vertically by adding more resources to a single machine, it has limitations in terms of horizontal scalability. Titan, being a distributed graph database, can scale horizontally by adding more machines to the cluster, making it suitable for handling large-scale graph datasets.
Storage Backend: Neo4j uses a native graph storage backend, where data is stored on disk in a proprietary format optimized for graph operations. Titan, on the other hand, supports multiple storage backends, including Apache Cassandra, Apache HBase, and others, making it more flexible in terms of storage options.
Query Language: Neo4j uses the Cypher query language, which is a powerful and expressive language specially designed for querying and manipulating graph data. Titan, on the other hand, supports the Apache TinkerPop Gremlin query language, which is a general-purpose graph traversal language that works across different graph databases.
Consistency vs Availability: Neo4j focuses on strong consistency, ensuring that all reads and writes to the database reflect the latest state of the data. Titan, on the other hand, emphasizes availability, trading off some degree of consistency for improved scalability and fault tolerance.
Community and Ecosystem: Neo4j has a larger and more mature community, with a wide range of plugins, tools, and resources available for developers. Titan, although it has a smaller community, benefits from being part of the Apache Software Foundation, and therefore has the advantage of being associated with other popular and widely used projects.

Neo4j vs Titan

Overview

Neo4j vs Titan: What are the differences?