DBMS > CouchDB vs. Derby vs. Google BigQuery vs. PouchDB vs. VoltDB

System Properties Comparison CouchDB vs. Derby vs. Google BigQuery vs. PouchDB vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Large scale data warehouse service with append-only tables

JavaScript DBMS with an API inspired by CouchDB

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

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	9.30
Rank	#45	Overall
	#7	Document stores

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

couchdb.apache.org

db.apache.org/derby

cloud.google.com/bigquery

pouchdb.com

www.voltdb.com

Technical documentation

docs.couchdb.org/en/stable

db.apache.org/derby/manuals/index.html

cloud.google.com/bigquery/docs

pouchdb.com/guides

docs.voltdb.com

Developer

Apache Software Foundation

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

Apache Software Foundation

Google

Apache Software Foundation

VoltDB Inc.

Initial release

2005

1997

2010

2012

2010

Current release

3.3.3, December 2023

10.17.1.0, November 2023

7.1.1, June 2019

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache version 2

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

JavaScript

Java, C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

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

Linux
OS X

for development

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

Secondary indexes

yes

via views

yes

via views

yes

SQL

Support of SQL

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

JDBC

RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

View functions in JavaScript

Java Stored Procedures

user defined functions

in JavaScript

View functions in JavaScript

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication
Source-replica replication

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

Eventual Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

atomic operations within a single document possible

ACID

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Users and roles with access to stored procedures

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