DBMS > Firebird vs. OpenQM vs. Splice Machine

System Properties Comparison Firebird vs. OpenQM vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Firebird is an open source RDBMS forked from Borland's InterBase

QpenQM is a high-performance, self-tuning, multi-value DBMS

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

Primary database model

Relational DBMS

Multivalue DBMS

Relational DBMS

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

Score	21.06
Rank	#31	Overall
	#19	Relational DBMS

Score	0.33
Rank	#286	Overall
	#10	Multivalue DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

www.firebirdsql.org

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

splicemachine.com

Technical documentation

www.firebirdsql.org/en/reference-manuals

splicemachine.com/how-it-works

Developer

Firebird Foundation

Rocket Software, originally Martin Phillips

Splice Machine

Initial release

2000

As fork of Borland's InterBase

1993

2014

Current release

5.0.0, January 2024

3.4-12

3.1, March 2021

License

Commercial or Open Source

Open Source

Initial Developer's Public License

Open Source

GPLv2, extended commercial license 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

C and C++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

with some exceptions

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

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
Basic
C
Java
Objective C
PHP
Python

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

Server-side scripts

Stored procedures

PSQL

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

yes

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

Access rights can be defined down to the item level

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

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More resources

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