DBMS > Cubrid vs. Dgraph vs. EsgynDB vs. GigaSpaces vs. Google Cloud Bigtable

System Properties Comparison Cubrid vs. Dgraph vs. EsgynDB vs. GigaSpaces vs. Google Cloud Bigtable

Editorial information provided by DB-Engines

Name

Description

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

Distributed and scalable native Graph DBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Key-value store
Wide column store

Secondary database models

Graph DBMS
Search engine

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

Score	1.31
Rank	#169	Overall
	#78	Relational DBMS

Score	1.48
Rank	#158	Overall
	#15	Graph DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

Website

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

dgraph.io

www.esgyn.cn

www.gigaspaces.com

cloud.google.com/bigtable

Technical documentation

cubrid.org/manuals

dgraph.io/docs

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigtable/docs

Developer

CUBRID Corporation, CUBRID Foundation

Dgraph Labs, Inc.

Esgyn

Gigaspaces Technologies

Google

Initial release

2008

2016

2015

2000

2015

Current release

11.0, January 2021

15.5, September 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

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++, Java

Java, C++, .Net

Server operating systems

Linux
Windows

Linux
OS X
Windows

Linux

Linux
macOS
Solaris
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Supported programming languages

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

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

All languages supporting JDBC/ODBC/ADO.Net

.Net
C++
Java
Python
Scala

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Synchronous replication via Raft

Multi-source replication between multi datacenters

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Internal replication in Colossus, and regional replication between two clusters in different zones

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

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

User concepts

Access control

fine grained access rights according to SQL-standard

Planned for future releases

fine grained access rights according to SQL-standard

Role-based access control

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

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