DBMS > Datomic vs. IRONdb vs. Realm vs. RocksDB vs. Stardog

System Properties Comparison Datomic vs. IRONdb vs. Realm vs. RocksDB vs. Stardog

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.datomic.com

www.circonus.com/solutions/time-series-database/

realm.io

rocksdb.org

www.stardog.com

Technical documentation

docs.datomic.com

docs.circonus.com/irondb/category/getting-started

realm.io/docs

github.com/facebook/rocksdb/wiki

docs.stardog.com

Developer

Cognitect

Circonus LLC.

Realm, acquired by MongoDB in May 2019

Facebook, Inc.

Stardog-Union

Initial release

2012

2017

2014

2013

2010

Current release

1.0.6735, June 2023

V0.10.20, January 2018

9.2.1, May 2024

7.3.0, May 2020

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

BSD

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

C and C++

C++

Java

Server operating systems

All OS with a Java VM

Linux

Android
Backend: server-less
iOS
Windows

Linux

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP API

HTTP API

C++ API
Java API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Clojure
Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

C
C++
Go
Java
Perl
Python
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes, in Lua

runs within the applications so server-side scripts are unnecessary

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

By using transaction functions

yes

Change Listeners

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Automatic, metric affinity per node

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

configurable replication factor, datacenter aware

none

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

yes

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

yes

In-Memory realm

yes

User concepts

Access control

yes

Access rights for users and roles

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