Apache TinkerPop vs Neo4j

Overview

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Apache TinkerPop

Stacks0

Followers1

Votes0

Apache TinkerPop vs Neo4j: What are the differences?

Introduction: Apache TinkerPop and Neo4j are both graph databases that are used for storing and analyzing structured data. However, there are several key differences between the two.

Data Modeling Approach: Apache TinkerPop uses a property graph model, which is based on vertices and edges with properties. This allows for more flexible and dynamic data modeling, as properties can be dynamically added or removed from vertices and edges. On the other hand, Neo4j uses a labeled property graph model, where nodes represent entities and relationships connect them. This allows for more rigid and predefined data modeling.
Querying Language: Apache TinkerPop uses the Gremlin query language, which is a graph traversal language that allows users to perform complex queries on graph data. Gremlin provides a set of operators and functions that can be used to navigate and manipulate the graph. In contrast, Neo4j uses a query language called Cypher, which is specifically designed for querying graph data. Cypher provides a more SQL-like syntax for querying and manipulating the graph.
Support for Multiple Data Stores: Apache TinkerPop is not tied to a specific data store and can be used with various graph databases, such as Neo4j, JanusGraph, and Amazon Neptune. This allows users to switch between different graph databases without changing their code. On the other hand, Neo4j is a specific graph database that is designed to work only with Neo4j data stores.
Community and Ecosystem: Apache TinkerPop has a larger community and ecosystem compared to Neo4j. TinkerPop has a wide range of contributors and users, which results in more frequent updates, bug fixes, and new features. It also has a rich ecosystem with various tools and frameworks built on top of it, such as Apache Gremlin server and Apache Giraph. Neo4j, although it has a substantial community and ecosystem, does not have the same level of diversity and breadth as TinkerPop.
Scalability and Performance: Apache TinkerPop is designed to be highly scalable and can handle large-scale graph data processing. It provides features like automatic query optimization, distributed query execution, and support for parallel processing. Neo4j is also scalable but may have limitations when dealing with extremely large datasets or complex graph queries. TinkerPop's focus on scalability and performance gives it an edge in scenarios that require handling massive graph data.
Licensing: Apache TinkerPop is released under the Apache License 2.0, which is a permissive open-source license. This allows users to freely use, modify, and distribute the software without any restrictions. Neo4j, on the other hand, has a dual licensing model. It is available under the community edition, released under the GPLv3 license, which has certain limitations on its usage and redistribution. The enterprise edition of Neo4j, which provides additional features and support, requires a commercial license.

In summary, Apache TinkerPop and Neo4j differ in their data modeling approach, querying language, support for multiple data stores, community and ecosystem, scalability and performance, as well as licensing.

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Detailed Comparison

Neo4j	Apache TinkerPop
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.	Home page of The Apache Software Foundation
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 0
Followers 1.4K	Followers 1
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, Apache TinkerPop?

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.

Titan

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.

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.

Related Comparisons

Apache TinkerPop vs Neo4j: What are the differences?

Introduction: Apache TinkerPop and Neo4j are both graph databases that are used for storing and analyzing structured data. However, there are several key differences between the two.

Data Modeling Approach: Apache TinkerPop uses a property graph model, which is based on vertices and edges with properties. This allows for more flexible and dynamic data modeling, as properties can be dynamically added or removed from vertices and edges. On the other hand, Neo4j uses a labeled property graph model, where nodes represent entities and relationships connect them. This allows for more rigid and predefined data modeling.
Querying Language: Apache TinkerPop uses the Gremlin query language, which is a graph traversal language that allows users to perform complex queries on graph data. Gremlin provides a set of operators and functions that can be used to navigate and manipulate the graph. In contrast, Neo4j uses a query language called Cypher, which is specifically designed for querying graph data. Cypher provides a more SQL-like syntax for querying and manipulating the graph.
Support for Multiple Data Stores: Apache TinkerPop is not tied to a specific data store and can be used with various graph databases, such as Neo4j, JanusGraph, and Amazon Neptune. This allows users to switch between different graph databases without changing their code. On the other hand, Neo4j is a specific graph database that is designed to work only with Neo4j data stores.
Community and Ecosystem: Apache TinkerPop has a larger community and ecosystem compared to Neo4j. TinkerPop has a wide range of contributors and users, which results in more frequent updates, bug fixes, and new features. It also has a rich ecosystem with various tools and frameworks built on top of it, such as Apache Gremlin server and Apache Giraph. Neo4j, although it has a substantial community and ecosystem, does not have the same level of diversity and breadth as TinkerPop.
Scalability and Performance: Apache TinkerPop is designed to be highly scalable and can handle large-scale graph data processing. It provides features like automatic query optimization, distributed query execution, and support for parallel processing. Neo4j is also scalable but may have limitations when dealing with extremely large datasets or complex graph queries. TinkerPop's focus on scalability and performance gives it an edge in scenarios that require handling massive graph data.
Licensing: Apache TinkerPop is released under the Apache License 2.0, which is a permissive open-source license. This allows users to freely use, modify, and distribute the software without any restrictions. Neo4j, on the other hand, has a dual licensing model. It is available under the community edition, released under the GPLv3 license, which has certain limitations on its usage and redistribution. The enterprise edition of Neo4j, which provides additional features and support, requires a commercial license.

Apache TinkerPop vs Neo4j

Overview