DBMS > Geode vs. IBM Cloudant vs. Kingbase vs. Microsoft Azure Cosmos DB

System Properties Comparison Geode vs. IBM Cloudant vs. Kingbase vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Database as a Service offering based on Apache CouchDB

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1.51
Rank	#147	Overall
	#25	Key-value stores

Score	2.85
Rank	#95	Overall
	#18	Document stores

Score	0.44
Rank	#258	Overall
	#117	Relational DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

geode.apache.org

www.ibm.com/products/cloudant

www.kingbase.com.cn

azure.microsoft.com/services/cosmos-db

Technical documentation

geode.apache.org/docs

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

BeiJing KINGBASE Information technologies inc.

Microsoft

Initial release

2002

2010

1999

2014

Current release

1.1, February 2017

V8.0, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Erlang

C and Java

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
Windows

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language (OQL)

Standard with numerous extensions

SQL-like query language

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

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

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

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

Server-side scripts

Stored procedures

user defined functions

View functions (Map-Reduce) in JavaScript

user defined functions

JavaScript

Triggers

yes

Cache Event Listeners

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Multi-source replication

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

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

yes, on a single node

atomic operations within a document possible

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights per client and object definable

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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