DBMS > H2GIS vs. Prometheus vs. RDF4J vs. Spark SQL vs. SQream DB

System Properties Comparison H2GIS vs. Prometheus vs. RDF4J vs. Spark SQL vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

Spatial extension of H2

Open-source Time Series DBMS and monitoring system

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Secondary database models

Relational DBMS

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

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

Technical documentation

www.h2gis.org/docs/home

prometheus.io/docs

rdf4j.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

docs.sqream.com

Developer

CNRS

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

Apache Software Foundation

SQream Technologies

Initial release

2013

2015

2004

2014

2017

Current release

3.5.0 ( 2.13), September 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

LGPL 3.0

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

commercial

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

Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

Numeric data only

yes

yes, ANSI Standard SQL Types

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

Java

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Java
PHP
Python

Java
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

based on H2

yes

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes, utilizing Spark Core

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

yes

by Federation

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate 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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

yes

based on H2

More information provided by the system vendor

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