DBMS > Blueflood vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. NCache vs. TigerGraph

System Properties Comparison Blueflood vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. NCache vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Time Series DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Graph DBMS

Secondary database models

Spatial DBMS

Document store
Search engine

Using distributed Lucene

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

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

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

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

blueflood.io

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

www.alachisoft.com/ncache

www.tigergraph.com

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

www.alachisoft.com/resources/docs

docs.tigergraph.com

Developer

Rackspace

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Alachisoft

Initial release

2013

2010

2014

2005

2017

Current release

5.3.3, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Enterprise Edition available

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#, .NET, .NET Core, Java

C++

Server operating systems

Linux
OS X

hosted

Linux
Windows

Linux

Data scheme

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

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

SQL-like query language (GSQL)

APIs and other access methods

HTTP REST

RESTful HTTP/JSON API

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

IDistributedCache
JCache
LINQ
Proprietary native API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

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

C++
Java

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

JavaScript

support for stored procedures with SQL-Server CLR

yes

Triggers

yes

JavaScript

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

yes, with selectable consistency level

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

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

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking and pessimistic locking

ACID

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 for users can be defined per database

Access rights can be defined down to the item level

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

Role-based access control

More information provided by the system vendor

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Typical application scenarios

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Market metrics

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