DBMS > GridDB vs. Machbase vs. Neo4j

System Properties Comparison GridDB vs. Machbase vs. Neo4j

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Time Series DBMS

Graph DBMS

Secondary database models

Key-value store
Relational DBMS

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

Score	1.23
Rank	#159	Overall
	#11	Time Series DBMS

Score	0.01
Rank	#348	Overall
	#32	Time Series DBMS

Score	60.14
Rank	#19	Overall
	#1	Graph DBMS

Website

griddb.net

www.machbase.com

neo4j.com

Technical documentation

docs.griddb.net

endoc.machbase.com

neo4j.com/docs

Developer

Toshiba Corporation

Machbase

Neo4j, Inc.

Initial release

2013

2007

Current release

4.6, February 2021

V6.5, August 2021

4.4.6, April 2022

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

free test version available

Open Source

GPL version3, commercial licenses available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

C++

Java, Scala

Server operating systems

Linux

Linux
Windows

Linux

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

Data scheme

yes

schema-free and schema-optional

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

APIs and other access methods

Proprietary protocol

HTTP REST
JDBC
ODBC

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C#
C++
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Server-side scripts

Stored procedures

yes

User defined Procedures and Functions

Triggers

yes

via event handler

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes using Neo4j Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Causal Clustering using Raft protocol

available in in Enterprise Version only

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

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

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

volatile and lookup table

User concepts

Access control

Access rights for users can be defined per database

simple password-based access control

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

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Machbase is an extremely fast time series database which processes large volumes...
» more

Neo4j delivers graph technology that has been battle tested for performance and scale...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Fastest Database for IIoT, Independently verified, on Industry Standard Benchmarks....
» more

Neo4j is the market leader, graph database category creator, and the most widely...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Distributed Query In Machbase Cluster Edition, a broker node, which is responsible...
» more

Real-Time Recommendations Master Data Management Identity and Access Management Network...
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Machbase has over 50 customers, including Samsung, POSCO, and Hyundai.
» more

Over 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
» more

Market metrics

GitHub trending repository
» more

Real-time data processing Real-time index creation Optimized DBMS for processing...
» more

Neo4j boasts the world's largest graph database ecosystem with more than 140 million...
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

Machbase provide several product editions, including: Intelligent Edge Device, Fog...
» more

GPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
» more

News

This Week in Neo4j: Graph Composition, Cypher Tips, Dataset Analysis, Kafka Messages, Running Backups, Legal AI, and More
21 May 2022

How Knowledge Graphs Drive More Sales With Real-Time Recommendation Engines
20 May 2022

Our Journey to Self-Discovery: Synthesizing 30 Years of Data Pipelines With Knowledge Graphs
17 May 2022

How Knowledge Graphs Drive More Sales With Real-Time Recommendation Engines
16 May 2022

This Week in Neo4j: Graphs4Good, GraphQL, Game of Life, Information Extraction, Cypher Map Projection, and More
14 May 2022