DBMS > Altibase vs. DolphinDB vs. Firebase Realtime Database vs. Fujitsu Enterprise Postgres vs. H2

System Properties Comparison Altibase vs. DolphinDB vs. Firebase Realtime Database vs. Fujitsu Enterprise Postgres vs. H2

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Primary database model

Secondary database models

Relational DBMS

Document store
Spatial DBMS

Spatial DBMS

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

Score	1.07
Rank	#186	Overall
	#87	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Website

altibase.com

www.dolphindb.com

firebase.google.com/products/realtime-database

www.postgresql.fastware.com

www.h2database.com

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.dolphindb.cn/en/help200/index.html

firebase.google.com/docs/database

www.postgresql.fastware.com/product-manuals

www.h2database.com/html/main.html

Developer

Altibase

DolphinDB, Inc

Google

acquired by Google 2014

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Thomas Mueller

Initial release

1999

2018

2012

2005

Current release

v7.3, 2023, August 2023

v2.00.4, January 2022

Fujitsu Enterprise Postgres 14, January 2022

2.2.220, July 2023

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

free community version available

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Server operating systems

AIX
HP-UX
Linux

Linux
Windows

hosted

Linux
Windows

All OS with a Java VM

Data scheme

yes

schema-free

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

ANSI SQL-92

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

C
C++
Java

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Java
JavaScript
Objective-C

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

limited functionality with using 'rules'

user defined functions

Java Stored Procedures and User-Defined Functions

Triggers

yes

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

partitioning by range, list and by hash

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

fine grained access rights according to SQL-standard

Administrators, Users, Groups

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

100% compatible with community PostgreSQL
» more

Competitive advantages

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more

Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
» more

Licensing and pricing models

Core based licensing
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

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