DBMS > AnzoGraph DB vs. Cubrid vs. Google Cloud Datastore vs. Oracle NoSQL

System Properties Comparison AnzoGraph DB vs. Cubrid vs. Google Cloud Datastore vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store

Document store
Key-value store
Relational DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Website

cambridgesemantics.com/anzograph

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

cloud.google.com/datastore

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

cubrid.org/manuals

cloud.google.com/datastore/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Cambridge Semantics

CUBRID Corporation, CUBRID Foundation

Google

Oracle

Initial release

2018

2008

2011

Current release

2.3, January 2021

11.0, January 2021

23.3, December 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

Java

Server operating systems

Linux

Linux
Windows

hosted

Linux
Solaris SPARC/x86

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

yes, details here

optional

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

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

SQL-like query language (GQL)

SQL-like DML and DDL statements

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

ADO.NET
JDBC
ODBC
OLE DB

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

RESTful HTTP API

Supported programming languages

C++
Java
Python

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

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

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

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

Multi-source replication using Paxos

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

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
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

not needed in graphs

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

configurable

ACID within a storage node (=shard)

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

off heap cache

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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

Access rights for users and roles

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More resources

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