DBMS > Faircom DB vs. Geode vs. GeoMesa vs. PouchDB vs. SQLite

System Properties Comparison Faircom DB vs. Geode vs. GeoMesa vs. PouchDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store
Relational DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.faircom.com/products/faircom-db

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

geode.apache.org/docs

www.geomesa.org/documentation/stable/user/index.html

pouchdb.com/guides

www.sqlite.org/docs.html

Developer

FairCom Corporation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

CCRi and others

Apache Software Foundation

Dwayne Richard Hipp

Initial release

1979

2002

2014

2012

2000

Current release

V12, November 2020

1.1, February 2017

4.0.5, February 2024

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache License 2.0

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

Java

Scala

JavaScript

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

All OS with a Java VM

the JDK (8 or later) is also required

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

server-less

Data scheme

schema free, schema optional, schema required, partial schema,

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language (OQL)

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java Client API
Memcached protocol
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
All JVM based languages
C++
Groovy
Java
Scala

JavaScript

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

View functions in JavaScript

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

depending on storage layer

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication

depending on storage layer

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

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 per server, database, table, and transaction

Eventual Consistency

depending on storage layer

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

In-memory capabilities

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

yes

depending on storage layer

yes

User concepts

Access control

Fine grained access rights according to SQL-standard with additional protections for files

Access rights per client and object definable

yes

depending on the DBMS used for storage

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