DBMS > BaseX vs. Blueflood vs. Cubrid vs. Faircom EDGE vs. Splice Machine

System Properties Comparison BaseX vs. Blueflood vs. Cubrid vs. Faircom EDGE vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Scalable TimeSeries DBMS based on Cassandra

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Native XML DBMS

Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

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

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

basex.org

blueflood.io

cubrid.com (korean)
cubrid.org (english)

www.faircom.com/products/faircom-edge

splicemachine.com

Technical documentation

docs.basex.org

github.com/rax-maas/blueflood/wiki

cubrid.org/manuals

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

splicemachine.com/how-it-works

Developer

BaseX GmbH

Rackspace

CUBRID Corporation, CUBRID Foundation

FairCom Corporation

Splice Machine

Initial release

2007

2013

2008

1979

2014

Current release

11.0, June 2024

11.0, January 2021

V3, October 2020

3.1, March 2021

License

Commercial or Open Source

Open Source

BSD license

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

AGPL 3.0, commercial license available

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

Java

C, C++, Java

ANSI C, C++

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X

Linux
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

predefined scheme

yes

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

yes

Typing

predefined data types such as float or date

XQuery supports types

yes

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

yes

ANSI SQL queries

yes

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

HTTP REST

ADO.NET
JDBC
ODBC
OLE DB

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

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

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

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

.Net, JavaScript, C/C++

yes

Java

Triggers

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

File partitioning

Customizable business rules for partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate 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

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes, multi-version concurrency control (MVCC)

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

Users with fine-grained authorization concept on 4 levels

fine grained access rights according to SQL-standard

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

Access rights for users, groups and roles according to SQL-standard

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