DBMS > Datomic vs. EsgynDB vs. Firebird vs. RisingWave

System Properties Comparison Datomic vs. EsgynDB vs. Firebird vs. RisingWave

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Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Relational DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	20.50
Rank	#30	Overall
	#18	Relational DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Website

www.datomic.com

www.esgyn.cn

www.firebirdsql.org

www.risingwave.com/database

Technical documentation

docs.datomic.com

www.firebirdsql.org/en/reference-manuals

docs.risingwave.com/docs/current/intro

Developer

Cognitect

Esgyn

Firebird Foundation

RisingWave Labs

Initial release

2012

2015

2000

As fork of Borland's InterBase

2022

Current release

1.0.7075, December 2023

5.0.0, January 2024

1.2, September 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Initial Developer's Public License

Open Source

Apache Version 2.0

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.

Implementation language

Java, Clojure

C++, Java

C and C++

Rust

Server operating systems

All OS with a Java VM

Linux

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Docker
Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

JDBC
PostgreSQL wire protocol

Supported programming languages

Clojure
Java

All languages supporting JDBC/ODBC/ADO.Net

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

Java Stored Procedures

PSQL

UDFs in Python or Java

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication between multi datacenters

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

Users and Roles

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