DBMS > Bangdb vs. Cachelot.io vs. Oracle Berkeley DB vs. SWC-DB vs. Yanza

System Properties Comparison Bangdb vs. Cachelot.io vs. Oracle Berkeley DB vs. SWC-DB vs. Yanza

Editorial information provided by DB-Engines

Name

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

Description

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

In-memory caching system

Widely used in-process key-value store

A high performance, scalable Wide Column DBMS

Time Series DBMS for IoT Applications

Primary database model

Document store
Graph DBMS
Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

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	0.00
Rank	#383	Overall
	#60	Key-value stores

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

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

bangdb.com

cachelot.io

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

github.com/kashirin-alex/swc-db
www.swcdb.org

yanza.com

Technical documentation

docs.bangdb.com

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

Developer

Sachin Sinha, BangDB

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Alex Kashirin

Yanza

Initial release

2012

2015

1994

2020

2015

Current release

BangDB 2.0, October 2021

18.1.40, May 2020

0.5, April 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Simplified BSD License

Open Source

commercial license available

Open Source

GPL V3

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

C++

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

C++

Server operating systems

Linux

FreeBSD
Linux
OS X

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

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

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

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

SQL interfaced based on SQLite is available

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Memcached protocol

Proprietary protocol
Thrift

HTTP API

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.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++

any language that supports HTTP calls

Server-side scripts

Stored procedures

Triggers

yes, Notifications (with Streaming only)

yes

only for the SQL API

yes

Timer and event based

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

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Source-replica replication

none

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

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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)

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