DBMS > Firebird vs. Ignite vs. Linter vs. Milvus vs. RDF4J

System Properties Comparison Firebird vs. Ignite vs. Linter vs. Milvus vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

RDBMS for high security requirements

A DBMS designed for efficient storage of vector data and vector similarity searches

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Vector DBMS

RDF store

Secondary database models

Spatial DBMS

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

Score	21.06
Rank	#31	Overall
	#19	Relational DBMS

Score	3.64
Rank	#89	Overall
	#13	Key-value stores
	#48	Relational DBMS

Score	0.10
Rank	#350	Overall
	#153	Relational DBMS

Score	1.81
Rank	#144	Overall
	#5	Vector DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Website

Technical documentation

www.firebirdsql.org/en/reference-manuals

apacheignite.readme.io/docs

milvus.io/docs/overview.md

rdf4j.org/documentation

Developer

Firebird Foundation

Apache Software Foundation

relex.ru

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2000

As fork of Borland's InterBase

2015

1990

2019

2004

Current release

5.0.0, January 2024

Apache Ignite 2.6

2.3.4, January 2024

License

Commercial or Open Source

Open Source

Initial Developer's Public License

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C and C++

C++, Java, .Net

C and C++

C++, Go

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux
OS X
Solaris
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C++
Go
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

PSQL

yes (compute grid and cache interceptors can be used instead)

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes (replicated cache)

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

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

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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