DBMS > Bangdb vs. CouchDB vs. GBase vs. Microsoft Azure Cosmos DB

System Properties Comparison Bangdb vs. CouchDB vs. GBase vs. Microsoft Azure Cosmos DB

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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

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

bangdb.com

couchdb.apache.org

www.gbase.cn

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.bangdb.com

docs.couchdb.org/en/stable

learn.microsoft.com/azure/cosmos-db

Developer

Sachin Sinha, BangDB

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

General Data Technology Co., Ltd.

Microsoft

Initial release

2012

2005

2004

2014

Current release

BangDB 2.0, October 2021

3.3.3, December 2023

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Erlang

C, Java, Python

Server operating systems

Linux

Android
BSD
Linux
OS X
Solaris
Windows

Linux

hosted

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

JSON types

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

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

via views

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL like support with command line tool

Standard with numerous extensions

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
C API
JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

C
C#
C++
Java
Python

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

JavaScript

Triggers

yes, Notifications (with Streaming only)

yes

JavaScript

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

improved architecture with release 2.0

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

atomic operations within a single document possible

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

strategy: optimistic locking

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

User concepts

Access control

yes (enterprise version only)

Access rights for users can be defined per database

yes

Access rights can be defined down to the item level

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