DBMS > Cachelot.io vs. CouchDB vs. Google BigQuery vs. NuoDB vs. PouchDB

System Properties Comparison Cachelot.io vs. CouchDB vs. Google BigQuery vs. NuoDB vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

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

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

cachelot.io

couchdb.apache.org

cloud.google.com/bigquery

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

pouchdb.com

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

doc.nuodb.com

pouchdb.com/guides

Developer

Apache Software Foundation

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

Google

Dassault Systèmes

originally NuoDB, Inc.

Apache Software Foundation

Initial release

2015

2005

2010

2013

2012

Current release

3.3.3, December 2023

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

Apache version 2

commercial

limited edition free

Open Source

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++

Erlang

C++

JavaScript

Server operating systems

FreeBSD
Linux
OS X

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

via views

yes

via views

SQL

Support of SQL

yes

APIs and other access methods

Memcached protocol

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

JavaScript

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

Java, SQL

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

improved architecture with release 2.0

none

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

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Eventual 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

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

MVCC

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Temporary table

yes

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)

Standard SQL roles/ privileges, Administrative Users

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