Mongoose vs Neo4j

Overview

Mongoose

Stacks2.4K

Followers1.4K

Votes56

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Mongoose vs Neo4j: What are the differences?

Data Model: Mongoose is an Object Data Modeling (ODM) library for MongoDB which helps in defining schemas and modeling application data. On the other hand, Neo4j is a graph database that stores data in a graph-like structure with nodes, relationships, and properties, allowing for efficient traversal of relationships between data points.
Query Language: Mongoose uses the MongoDB query language which is primarily JSON-like. In contrast, Neo4j utilizes the Cypher query language which is specifically designed for querying graph data. Cypher makes it easy to perform complex graph traversals and pattern matching operations.
Scalability: Mongoose is designed to work with MongoDB, which is a horizontally scalable database system. Neo4j, being a graph database, excels in handling complex relationships and is often chosen for scenarios where the relationships between data are the main focus.
Performance: In terms of performance, Mongoose and MongoDB are known for their speed and efficiency in handling large volumes of data. Neo4j, being a graph database, can provide faster traversal of relationships between data points, making it a powerful tool for scenarios where relationship querying is crucial.
Flexibility: Mongoose provides a schema-based approach to data modeling, which can be beneficial for applications with predefined data structures. Neo4j, on the other hand, offers more flexibility in data modeling due to the nature of graph databases, allowing for dynamic creation of nodes and relationships.
Use Cases: While Mongoose and MongoDB are commonly used for a wide range of applications, Neo4j is often preferred for applications where complex relationships need to be modeled and queried efficiently, such as social networks, recommendation systems, and fraud detection.

In Summary, Mongoose and Neo4j differ in terms of their data modeling approach, query languages, scalability, performance, flexibility, and use cases, catering to different requirements in the world of database management.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Mongoose, Neo4j

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

Mongoose	Neo4j
Let's face it, writing MongoDB validation, casting and business logic boilerplate is a drag. That's why we wrote Mongoose. Mongoose provides a straight-forward, schema-based solution to modeling your application data and includes built-in type casting, validation, query building, business logic hooks and more, out of the box.	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.
-	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 -	GitHub Stars 15.3K
GitHub Forks -	GitHub Forks 2.5K
Stacks 2.4K	Stacks 1.2K
Followers 1.4K	Followers 1.4K
Votes 56	Votes 351
Pros & Cons
Pros 17 Well documented 17 Several bad ideas mixed together 10 JSON 8 Actually terrible documentation 2 Recommended and used by Valve. See steamworks docs Cons 3 Model middleware/hooks are not user friendly	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
Integrations
Node.js MongoDB	No integrations available

What are some alternatives to Mongoose, Neo4j?

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.

Mongoid

The philosophy of Mongoid is to provide a familiar API to Ruby developers who have been using Active Record or Data Mapper, while leveraging the power of MongoDB's schemaless and performant document-based design, dynamic queries, and atomic modifier operations.

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.

Related Comparisons

Mongoose vs Neo4j: What are the differences?

Data Model: Mongoose is an Object Data Modeling (ODM) library for MongoDB which helps in defining schemas and modeling application data. On the other hand, Neo4j is a graph database that stores data in a graph-like structure with nodes, relationships, and properties, allowing for efficient traversal of relationships between data points.
Query Language: Mongoose uses the MongoDB query language which is primarily JSON-like. In contrast, Neo4j utilizes the Cypher query language which is specifically designed for querying graph data. Cypher makes it easy to perform complex graph traversals and pattern matching operations.
Scalability: Mongoose is designed to work with MongoDB, which is a horizontally scalable database system. Neo4j, being a graph database, excels in handling complex relationships and is often chosen for scenarios where the relationships between data are the main focus.
Performance: In terms of performance, Mongoose and MongoDB are known for their speed and efficiency in handling large volumes of data. Neo4j, being a graph database, can provide faster traversal of relationships between data points, making it a powerful tool for scenarios where relationship querying is crucial.
Flexibility: Mongoose provides a schema-based approach to data modeling, which can be beneficial for applications with predefined data structures. Neo4j, on the other hand, offers more flexibility in data modeling due to the nature of graph databases, allowing for dynamic creation of nodes and relationships.
Use Cases: While Mongoose and MongoDB are commonly used for a wide range of applications, Neo4j is often preferred for applications where complex relationships need to be modeled and queried efficiently, such as social networks, recommendation systems, and fraud detection.

Mongoose vs Neo4j

Overview