DBMS > BigObject vs. EJDB vs. Galaxybase vs. Google Cloud Datastore vs. NuoDB

System Properties Comparison BigObject vs. EJDB vs. Galaxybase vs. Google Cloud Datastore vs. NuoDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

Scalable, ACID-compliant native distributed parallel graph platform

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

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

bigobject.io

github.com/Softmotions/ejdb

galaxybase.com

cloud.google.com/datastore

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

docs.bigobject.io

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

cloud.google.com/datastore/docs

doc.nuodb.com

Developer

BigObject, Inc.

Softmotions

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Google

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2015

2012

2017

2008

2013

Current release

Nov 20, November 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

GPLv2

commercial

limited edition free

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 and Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

server-less

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

yes

schema-free

Strong typed schema

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

SQL-like DML and DDL statements

SQL-like query language (GQL)

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

in-process shared library

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

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

ADO.NET
JDBC
ODBC

Supported programming languages

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

Go
Java
Python

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

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

Server-side scripts

Stored procedures

Lua

user defined procedures and functions

using Google App Engine

Java, SQL

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes

Managed transparently by NuoDB

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

none

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.

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

typically not needed, however similar functionality with collection joins possible

yes

Relationships in graphs

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Read/Write Locking

yes

MVCC

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

Temporary table

User concepts

Access control

Role-based access control

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

Standard SQL roles/ privileges, Administrative Users

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More resources

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