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

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

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 drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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)

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	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

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

Website

www.datomic.com

github.com/dragonflydb/dragonfly
www.dragonflydb.io

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

www.oushu.com/product/oushuDB

rocksdb.org

Technical documentation

docs.datomic.com

www.dragonflydb.io/docs

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

www.oushu.com/documentation

github.com/facebook/rocksdb/wiki

Developer

Cognitect

DragonflyDB team and community contributors

Circonus LLC.

Oushu

Facebook, Inc.

Initial release

2012

2023

2017

2013

Current release

1.0.7075, December 2023

1.0, March 2023

V0.10.20, January 2018

4.0.1, August 2020

9.2.1, May 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

BSL 1.1

commercial

Open Source

BSD

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++

C and C++

C++

Server operating systems

All OS with a Java VM

Linux

Data scheme

yes

scheme-free

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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

APIs and other access methods

RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

HTTP API

JDBC
ODBC

C++ API
Java API

Supported programming languages

Clojure
Java

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

.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

Server-side scripts

Stored procedures

yes

Transaction Functions

Lua

yes, in Lua

yes

Triggers

By using transaction functions

publish/subscribe channels provide some trigger functionality

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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

User concepts

Access control

Password-based authentication

Kerberos, SSL and role based access

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