DBMS > BigObject vs. Google Cloud Datastore vs. MySQL vs. Newts vs. NuoDB

System Properties Comparison BigObject vs. Google Cloud Datastore vs. MySQL vs. Newts vs. NuoDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

Widely used open source RDBMS

Time Series DBMS based on Cassandra

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

bigobject.io

cloud.google.com/datastore

www.mysql.com

opennms.github.io/newts

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

Technical documentation

docs.bigobject.io

cloud.google.com/datastore/docs

dev.mysql.com/doc

github.com/OpenNMS/newts/wiki

doc.nuodb.com

Developer

BigObject, Inc.

Google

Oracle

since 2010, originally MySQL AB, then Sun

OpenNMS Group

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2015

2008

1995

2014

2013

Current release

8.4.0, April 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

commercial

limited edition free

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

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)

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (GQL)

yes

with proprietary extensions

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST
Java API

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
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

Java

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

Server-side scripts

Stored procedures

Lua

using Google App Engine

yes

proprietary syntax

Java, SQL

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on Cassandra

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

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

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 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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

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

not for MyISAM storage engine

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

table locks or row locks depending on storage engine

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

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

Users with fine-grained authorization concept

no user groups or roles

Standard SQL roles/ privileges, Administrative Users

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