DBMS > EJDB vs. Google Cloud Bigtable vs. Oracle NoSQL vs. RDFox

System Properties Comparison EJDB vs. Google Cloud Bigtable vs. Oracle NoSQL vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

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

High performance knowledge graph and semantic reasoning engine

Primary database model

Document store

Key-value store
Wide column store

Document store
Key-value store
Relational DBMS

Graph DBMS
RDF store

Secondary database models

Relational DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

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

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

github.com/Softmotions/ejdb

cloud.google.com/bigtable

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

www.oxfordsemantic.tech

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

cloud.google.com/bigtable/docs

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

docs.oxfordsemantic.tech

Developer

Softmotions

Google

Oracle

Oxford Semantic Technologies

Initial release

2012

2015

2011

2017

Current release

23.3, December 2023

6.0, Septermber 2022

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

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

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

Java

C++

Server operating systems

server-less

hosted

Linux
Solaris SPARC/x86

Linux
macOS
Windows

Data scheme

schema-free

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

yes

RDF schemas

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

optional

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

SQL-like DML and DDL statements

APIs and other access methods

in-process shared library

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

RESTful HTTP API

RESTful HTTP API
SPARQL 1.1

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

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

C
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

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

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

Atomic single-row operations

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

yes

User concepts

Access control

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

Access rights for users and roles

Roles, resources, and access types

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