DBMS > MongoDB vs. MySQL vs. Neo4j

System Properties Comparison MongoDB vs. MySQL vs. Neo4j

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Editorial information provided by DB-Engines

Name

Description

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

Widely used open source RDBMS

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Primary database model

Document store

Relational DBMS

Key/Value like access via memcached API

Graph DBMS

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0
Vector DBMS

currently available in the MongoDB Atlas cloud service only

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	402.51
Rank	#5	Overall
	#1	Document stores

Score	964.98
Rank	#2	Overall
	#2	Relational DBMS

Score	47.91
Rank	#20	Overall
	#1	Graph DBMS

Website

www.mongodb.com

www.mysql.com

neo4j.com

Technical documentation

www.mongodb.com/docs/manual

dev.mysql.com/doc

neo4j.com/docs

Developer

MongoDB, Inc

Oracle

since 2010, originally MySQL AB, then Sun

Neo4j, Inc.

Initial release

2009

1995

2007

Current release

7.0.5, January 2024

9.0.0, July 2024

5.23, August 2024

License

Commercial or Open Source

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL version3, commercial licenses available

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Java, Scala

Server operating systems

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

Data scheme

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

yes

schema-free and schema-optional

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

SQL

Support of SQL

Read-only SQL queries via the MongoDB Atlas SQL Interface

yes

with proprietary extensions

APIs and other access methods

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

ADO.NET
JDBC
ODBC
Proprietary native API

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

Supported programming languages

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Kotlin
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

JavaScript

yes

proprietary syntax

yes

User defined Procedures and Functions

Triggers

yes

in MongoDB Atlas only

yes

via event handler

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes using Neo4j Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

Multi-source replication
Source-replica replication

Causal Clustering using Raft protocol

available in in Enterprise Version only

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Immediate Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

not for MyISAM storage engine

yes

Relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-document ACID Transactions with snapshot isolation

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

optional, enabled by default

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

In-memory storage engine introduced with MongoDB version 3.2

yes

User concepts

Access control

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

More information provided by the system vendor

News

Announcing the Neo4j Graph Workload for Microsoft Fabric
15 May 2025

Run Cypher From Java
13 May 2025

Integrating Neo4j With LangChain4j for GraphRAG Vector Stores and Retrievers
12 May 2025

This Week in Neo4j: NODES, Knowledge Graph, Graph Analytics, MCP and more
10 May 2025

Aura Graph Analytics: A Technical Deep Dive
7 May 2025

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