DBMS > DolphinDB vs. H2 vs. PostGIS vs. RDF4J

System Properties Comparison DolphinDB vs. H2 vs. PostGIS vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

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.

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

Spatial extension of PostgreSQL

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

Primary database model

Secondary database models

Relational DBMS

Spatial DBMS

Relational DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

Technical documentation

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

www.h2database.com/html/main.html

postgis.net/documentation

rdf4j.org/documentation

Developer

DolphinDB, Inc

Thomas Mueller

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

Initial release

2018

2005

2004

Current release

v2.00.4, January 2022

2.2.220, July 2023

3.4.2, February 2024

License

Commercial or Open Source

commercial

free community version available

Open Source

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

Open Source

GPL v2.0

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.

Implementation language

C++

Java

Server operating systems

Linux
Windows

All OS with a Java VM

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

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

SQL-like query language

yes

APIs and other access methods

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

JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

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

Java

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

yes

based on PostgreSQL

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

based on PostgreSQL

none

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

yes

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Administrators, Users, Groups

fine grained access rights according to SQL-standard

yes

based on PostgreSQL

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More resources

DB-Engines blog posts

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