DBMS > Drizzle vs. EsgynDB vs. Google Cloud Bigtable vs. NuoDB vs. ToroDB

System Properties Comparison Drizzle vs. EsgynDB vs. Google Cloud Bigtable vs. NuoDB vs. ToroDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

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

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Document store

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/bigtable

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

github.com/torodb/server

Technical documentation

cloud.google.com/bigtable/docs

doc.nuodb.com

Developer

Drizzle project, originally started by Brian Aker

Esgyn

Google

Dassault Systèmes

originally NuoDB, Inc.

8Kdata

Initial release

2008

2015

2013

2016

Current release

7.2.4, September 2012

License

Commercial or Open Source

Open Source

GNU GPL

commercial

limited edition free

Open Source

AGPL-V3

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

Server operating systems

FreeBSD
Linux
OS X

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

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

ADO.NET
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/ADO.Net

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

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

Server-side scripts

Stored procedures

Java Stored Procedures

Java, SQL

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

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

yes

Managed transparently by NuoDB

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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

Standard SQL roles/ privileges, Administrative Users

Access rights for users and roles

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More resources

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