DBMS > Microsoft Azure Cosmos DB vs. MonetDB vs. NCache vs. Newts vs. TinkerGraph

System Properties Comparison Microsoft Azure Cosmos DB vs. MonetDB vs. NCache vs. Newts vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

A relational database management system that stores data in columns

Open-Source and Enterprise in-memory Key-Value Store

Time Series DBMS based on Cassandra

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Document store
Search engine

Using distributed Lucene

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

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

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

azure.microsoft.com/services/cosmos-db

www.monetdb.org

www.alachisoft.com/ncache

opennms.github.io/newts

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

learn.microsoft.com/azure/cosmos-db

www.monetdb.org/Documentation

www.alachisoft.com/resources/docs

github.com/OpenNMS/newts/wiki

Developer

Microsoft

MonetDB BV

Alachisoft

OpenNMS Group

Initial release

2014

2004

2005

2014

2009

Current release

Dec2023 (11.49), December 2023

5.3.3, April 2024

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

Enterprise Edition available

Open Source

Apache 2.0

Open Source

Apache 2.0

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#, .NET, .NET Core, Java

Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

APIs and other access methods

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

IDistributedCache
JCache
LINQ
Proprietary native API

HTTP REST
Java API

TinkerPop 3

Supported programming languages

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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Java

Groovy
Java

Server-side scripts

Stored procedures

JavaScript

yes, in SQL, C, R

support for stored procedures with SQL-Server CLR

Triggers

JavaScript

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding via remote tables

yes

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

yes, with selectable consistency level

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking and pessimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

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 can be defined down to the item level

fine grained access rights according to SQL-standard

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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