DBMS > Datomic vs. IRONdb vs. OushuDB vs. RocksDB vs. VoltDB

System Properties Comparison Datomic vs. IRONdb vs. OushuDB vs. RocksDB vs. VoltDB

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 data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

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

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

www.datomic.com

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

www.oushu.com/product/oushuDB

rocksdb.org

www.voltdb.com

Technical documentation

docs.datomic.com

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

www.oushu.com/documentation

github.com/facebook/rocksdb/wiki

docs.voltdb.com

Developer

Cognitect

Circonus LLC.

Oushu

Facebook, Inc.

VoltDB Inc.

Initial release

2012

2017

2013

2010

Current release

1.0.7075, December 2023

V0.10.20, January 2018

4.0.1, August 2020

9.2.1, May 2024

11.3, April 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

BSD

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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, C++

Server operating systems

All OS with a Java VM

Linux

Linux
OS X

for development

Data scheme

yes

schema-free

yes

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

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

Full-featured ANSI SQL support

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP API

HTTP API

JDBC
ODBC

C++ API
Java API

Java API
JDBC
RESTful HTTP/JSON API

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

C
C++

C
C++
Go
Java
Perl
Python
Ruby

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

yes, in Lua

yes

Java

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

Automatic, metric affinity per node

yes

horizontal partitioning

Sharding

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

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

yes

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

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

yes

Snapshots and command logging

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

Kerberos, SSL and role based access

Users and roles with access to stored procedures

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