DBMS > IBM Db2 vs. Kyligence Enterprise vs. Microsoft Azure Cosmos DB vs. Riak KV

System Properties Comparison IBM Db2 vs. Kyligence Enterprise vs. Microsoft Azure Cosmos DB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Common in IBM host environments, 2 different versions for host and Windows/Linux

A distributed analytics engine for big data, built on top of Apache Kylin

Globally distributed, horizontally scalable, multi-model database service

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS

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

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.ibm.com/products/db2

kyligence.io/kyligence-enterprise

azure.microsoft.com/services/cosmos-db

Technical documentation

www.ibm.com/docs/en/db2

learn.microsoft.com/azure/cosmos-db

www.tiot.jp/riak-docs/riak/kv/latest

Developer

IBM

Kyligence, Inc.

Microsoft

OpenSource, formerly Basho Technologies

Initial release

1983

host version

2016

2014

2009

Current release

12.1, October 2016

3.2.0, December 2022

License

Commercial or Open Source

commercial

free version is available

commercial

Open Source

Apache version 2, commercial enterprise edition

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

C and C++

Java

Erlang

Server operating systems

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

hosted

Linux
OS X

Data scheme

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.

Secondary indexes

yes

All properties auto-indexed by default

restricted

SQL

Support of SQL

yes

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
ODBC
RESTful HTTP API

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

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

JavaScript

Erlang

Triggers

yes

JavaScript

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

yes

Implicit feature of the cloud service

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

yes, using Riak Security

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