DBMS > Firebird vs. IBM Db2 Event Store vs. ReductStore

System Properties Comparison Firebird vs. IBM Db2 Event Store vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

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

Distributed Event Store optimized for Internet of Things use cases

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

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

Score	20.50
Rank	#30	Overall
	#18	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

www.firebirdsql.org

www.ibm.com/products/db2-event-store

github.com/reductstore
www.reduct.store

Technical documentation

www.firebirdsql.org/en/reference-manuals

www.ibm.com/docs/en/db2-event-store

www.reduct.store/docs

Developer

Firebird Foundation

IBM

ReductStore LLC

Initial release

2000

As fork of Borland's InterBase

2017

2023

Current release

5.0.0, January 2024

2.0

1.9, March 2024

License

Commercial or Open Source

Open Source

Initial Developer's Public License

commercial

free developer edition available

Open Source

Business Source License 1.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

C++, Rust

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux

Linux, macOS, Windows for the developer addition

Docker
Linux
macOS
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

PSQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual 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

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

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