DBMS > CouchDB vs. Faircom EDGE vs. H2 vs. jBASE vs. RocksDB

System Properties Comparison CouchDB vs. Faircom EDGE vs. H2 vs. jBASE vs. RocksDB

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.

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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

A robust multi-value DBMS comprising development tools and middleware

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

Multivalue DBMS

Key-value store

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

couchdb.apache.org

www.faircom.com/products/faircom-edge

www.h2database.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

rocksdb.org

Technical documentation

docs.couchdb.org/en/stable

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

www.h2database.com/html/main.html

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

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

FairCom Corporation

Thomas Mueller

Rocket Software (formerly Zumasys)

Facebook, Inc.

Initial release

2005

1979

2005

1991

2013

Current release

3.3.3, December 2023

V3, October 2020

2.2.220, July 2023

5.7

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Restricted, free version available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

ANSI C, C++

Java

C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

AIX
Linux
Windows

Linux

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

optional

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

SQL

Support of SQL

yes

ANSI SQL queries

yes

Embedded SQL for jBASE in BASIC

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

C++ API
Java API

Supported programming languages

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

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Java

.Net
Basic
Jabbascript
Java

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

View functions in JavaScript

yes

.Net, JavaScript, C/C++

Java Stored Procedures and User-Defined Functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

File partitioning

Customizable business rules for partitioning

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

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
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

atomic operations within a single document possible

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

across SQL and NoSQL

yes, multi-version concurrency control (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

User concepts

Access control

Access rights for users can be defined per database

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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