DBMS > Faircom DB vs. LeanXcale vs. LMDB vs. MonetDB vs. SQLite

System Properties Comparison Faircom DB vs. LeanXcale vs. LMDB vs. MonetDB 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.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A high performant, light-weight, embedded key-value database library

A relational database management system that stores data in columns

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

www.faircom.com/products/faircom-db

www.leanxcale.com

www.symas.com/symas-embedded-database-lmdb

www.monetdb.org

www.sqlite.org

Technical documentation

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

www.lmdb.tech/doc

www.monetdb.org/Documentation

www.sqlite.org/docs.html

Developer

FairCom Corporation

LeanXcale

Symas

MonetDB BV

Dwayne Richard Hipp

Initial release

1979

2015

2011

2004

2000

Current release

V12, November 2020

0.9.32, January 2024

Dec2023 (11.49), December 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Mozilla Public 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++

Server operating systems

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

easily portable to other OSs

Linux
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

server-less

Data scheme

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

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

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

through Apache Derby

yes

SQL 2003 with some extensions

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

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

C
Java
Scala

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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++

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding via remote tables

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).

none

Source-replica replication available in experimental status

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate 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

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

In-memory capabilities

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

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

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