DBMS > Cubrid vs. Dolt vs. Google Cloud Datastore vs. gStore vs. JaguarDB

System Properties Comparison Cubrid vs. Dolt vs. Google Cloud Datastore vs. gStore vs. JaguarDB

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A MySQL compatible DBMS with Git-like versioning of data and schema

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A native Graph DBMS to store and maintain very large RDF datasets.

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Relational DBMS

Document store

Graph DBMS
RDF store

Key-value store
Vector DBMS

Secondary database models

Document store

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.04
Rank	#359	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

cubrid.com (korean)
cubrid.org (english)

github.com/dolthub/dolt
www.dolthub.com

cloud.google.com/datastore

en.gstore.cn

www.jaguardb.com

Technical documentation

cubrid.org/manuals

docs.dolthub.com

cloud.google.com/datastore/docs

en.gstore.cn/#/enDocs

www.jaguardb.com/support.html

Developer

CUBRID Corporation, CUBRID Foundation

DoltHub Inc

Google

DataJaguar, Inc.

Initial release

2008

2018

2008

2016

2015

Current release

11.0, January 2021

1.2, November 2023

3.3 July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

BSD

Open Source

GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++, Java

C++

the server part. Clients available in other languages

Server operating systems

Linux
Windows

Linux
macOS
Windows

hosted

Linux

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL

Support of SQL

yes

SQL-like query language (GQL)

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

CLI Client
HTTP REST

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API
SPARQL 1.1

JDBC
ODBC

Supported programming languages

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

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
PHP
Python

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

currently in alpha release

using Google App Engine

yes

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication using Paxos

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

yes

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Only one user is configurable, and must be specified in the config file at startup

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users, roles and permissions, Role-Based Access Control (RBAC) supported

rights management via user accounts

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