DBMS > CouchDB vs. Google BigQuery vs. Hazelcast vs. Interbase vs. NuoDB

System Properties Comparison CouchDB vs. Google BigQuery vs. Hazelcast vs. Interbase vs. NuoDB

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Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

Light-weight proven RDBMS

originally from Borland

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

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store

JSON support with IMDG 3.12

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Trend Chart

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	4.08
Rank	#75	Overall
	#41	Relational DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Website

couchdb.apache.org

cloud.google.com/bigquery

hazelcast.com

www.embarcadero.com/products/interbase

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

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

docs.embarcadero.com/products/interbase

doc.nuodb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Hazelcast

Embarcadero

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2005

2010

2008

1984

2013

Current release

3.3.3, December 2023

5.3.6, November 2023

InterBase 2020, December 2019

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache Version 2; commercial licenses available

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

Erlang

Java

C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

All OS with a Java VM

Android
iOS
Linux
OS X
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

the object must implement a serialization strategy

export as XML data possible

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

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

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

yes

Interbase procedure and trigger language

Java, SQL

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Sharding

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Replicated Map

Interbase Change Views

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

Since BigQuery is designed for querying data

one or two-phase-commit; repeatable reads; read commited

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Multiversion concurreny control

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 can be defined per database

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

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