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

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

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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

Widely used in-process key-value store

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational 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

Spatial DBMS

Document store

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

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

bangdb.com

www.citusdata.com

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

rocksdb.org

Technical documentation

docs.bangdb.com

docs.citusdata.com

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

github.com/facebook/rocksdb/wiki

Developer

Sachin Sinha, BangDB

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Facebook, Inc.

Initial release

2012

2010

1994

2013

Current release

BangDB 2.0, October 2021

8.1, December 2018

18.1.40, May 2020

9.2.1, May 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

AGPL, commercial license also available

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

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Implementation language

C, 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

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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.

yes

specific XML type available, but no XML query functionality

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

standard, with numerous extensions

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

C++ API
Java API

Supported programming languages

C
C#
C++
Java
Python

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

.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.

Triggers

yes, Notifications (with Streaming only)

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

other methods possible by using 3rd party extensions

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

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