DBMS > Datomic vs. InterSystems Caché vs. Microsoft Azure Cosmos DB vs. RDF4J

System Properties Comparison Datomic vs. InterSystems Caché vs. Microsoft Azure Cosmos DB vs. RDF4J

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Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

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

A multi-model DBMS and application server

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

RDF store

Secondary database models

Document store

Spatial DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

www.datomic.com

www.intersystems.com/products/cache

azure.microsoft.com/services/cosmos-db

rdf4j.org

Technical documentation

docs.datomic.com

docs.intersystems.com

learn.microsoft.com/azure/cosmos-db

rdf4j.org/documentation

Developer

Cognitect

InterSystems

Microsoft

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

Initial release

2012

1997

2014

2004

Current release

1.0.7075, December 2023

2018.1.4, May 2020

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

Java

Server operating systems

All OS with a Java VM

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Unix
Windows

Data scheme

yes

depending on used data model

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

.NET Client API
JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Clojure
Java

C#
C++
Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

JavaScript

yes

Triggers

By using transaction functions

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

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

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

Access rights for users, groups and roles

Access rights can be defined down to the item level

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