DBMS > Datomic vs. Raima Database Manager vs. RDF4J vs. RocksDB vs. Spark SQL

System Properties Comparison Datomic vs. Raima Database Manager vs. RDF4J vs. RocksDB vs. Spark SQL

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

RDBMS with a small footprint, either embedded into an application or used as a database server

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

RDF store

Key-value store

Relational DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.49
Rank	#259	Overall
	#121	Relational DBMS
	#21	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

docs.datomic.com

docs.raima.com

rdf4j.org/documentation

github.com/facebook/rocksdb/wiki

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

Developer

Cognitect

Raima Inc.

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

Facebook, Inc.

Apache Software Foundation

Initial release

2012

1984

2004

2013

2014

Current release

1.0.6735, June 2023

15, June 2021

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

BSD

Open Source

Apache 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

Java

C++

Scala

Server operating systems

All OS with a Java VM

Android
HP-UX
Integrity RTOS
iOS
Linux
OS X
QNX
Solaris
VxWorks
Windows

Linux
OS X
Unix
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

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.

Exporting/Importing of XML structures possible

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

JDBC
ODBC

Supported programming languages

Clojure
Java

.Net
C
C++
Objective-C
PL/SQL

Java
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

based on the ANSI/ISO SQL Persistent Stored Modules (PSM) specification

yes

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

horizontal partitioning

none

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

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

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

recommended only for testing and development

yes

User concepts

Access control

More information provided by the system vendor

Specific characteristics

Raima Database Manager (RDM) is an embedded time-series database optimized to run...
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Competitive advantages

Decreased time-to-market Industry-standard interfaces & tools for making your job...
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Typical application scenarios

Typical use cases include: Internet of Things (IoT) Embedded In-Memory Database Time-Series...
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Key customers

Infor, ABB, The Boeing Company, BAE Systems, Dow Jones, Dun & Bradstreet, Fujitsu,...
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Market metrics

Over 30 000 applications developers worldwide have field-tested RDM products More...
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Licensing and pricing models

Just as the technical aspects of Raima Database Manager are flexible, so are the...
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News

Raima Unveils RaimaDB 16.0: Elevating Database Performance and User Experience
29 February 2024

Raima Announces Release of Raima Database Manager (RDM) v15, a Powerful Embedded Time Series Database
10 June 2021

Raima Announces Release of Raima Database Manager (RDM) v14.2, a Powerful IoT Edge Embedded Database
8 October 2020

Raima Joins BlackBerry QNX Channel Partner Program to Deliver Safety-Critical and Secure Software Solutions
1 October 2020

Raima Expands Their Reseller Network in Asia
19 May 2020

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