DBMS > Datomic vs. GraphDB

System Properties Comparison Datomic vs. GraphDB

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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

Enterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.

Primary database model

Relational DBMS

Graph DBMS
RDF store

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

Score	1.49
Rank	#142	Overall
	#64	Relational DBMS

Score	2.59
Rank	#96	Overall
	#7	Graph DBMS
	#3	RDF stores

Website

www.datomic.com

www.ontotext.com

Technical documentation

docs.datomic.com

graphdb.ontotext.com/documentation

Developer

Cognitect

Ontotext

Initial release

2012

2000

Current release

1.0.7180, July 2024

10.4, October 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Some plugins of GraphDB Workbench are open sourced

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

Server operating systems

All OS with a Java VM

All OS with a Java VM
Linux
OS X
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support; RDF shapes

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

yes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes

SQL

Support of SQL

stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC

APIs and other access methods

RESTful HTTP API

GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1

Supported programming languages

Clojure
Java

.Net
C#
Clojure
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

well-defined plugin interfaces; JavaScript server-side extensibility

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)

Foreign keys

Referential integrity

yes

Constraint checking

Transaction concepts

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

ACID

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 capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.

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