DBMS > Citus vs. IRONdb vs. Oracle Berkeley DB vs. RocksDB

System Properties Comparison Citus vs. IRONdb vs. Oracle Berkeley DB vs. RocksDB

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Editorial information provided by DB-Engines

Name

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

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Widely used in-process key-value store

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

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

Secondary database models

Document store

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

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

Website

www.citusdata.com

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

www.oracle.com/database/technologies/related/berkeleydb.html

rocksdb.org

Technical documentation

docs.citusdata.com

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

docs.oracle.com/cd/E17076_05/html/index.html

github.com/facebook/rocksdb/wiki

Developer

Circonus LLC.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Facebook, Inc.

Initial release

2010

2017

1994

2013

Current release

8.1, December 2018

V0.10.20, January 2018

18.1.40, May 2020

8.11.4, April 2024

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Open Source

commercial license available

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

C and C++

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

specific XML type available, but no XML query functionality

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

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

yes

SQL interfaced based on SQLite is available

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API

C++ API
Java API

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes, in Lua

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

configurable replication factor, datacenter aware

Source-replica replication

yes

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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