DBMS > Blueflood vs. GigaSpaces vs. Microsoft Azure Cosmos DB vs. Oracle Rdb

System Properties Comparison Blueflood vs. GigaSpaces vs. Microsoft Azure Cosmos DB vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	0.02
Rank	#344	Overall
	#31	Time Series DBMS

Score	0.97
Rank	#175	Overall
	#30	Document stores
	#6	Object oriented DBMS

Score	40.22
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.18
Rank	#162	Overall
	#77	Relational DBMS

Website

blueflood.io

www.gigaspaces.com

azure.microsoft.com/services/cosmos-db

www.oracle.com/technetwork/database/database-technologies/rdb/overview/index.html

Technical documentation

github.com/rackerlabs/blueflood/wiki

docs.gigaspaces.com/latest/landing.html

docs.microsoft.com/azure/cosmos-db

Developer

Rackspace

Gigaspaces Technologies

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2013

2000

2014

1984

Current release

15.5, September 2020

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; Commercial licenses 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

Java, C++, .Net

Server operating systems

Linux
OS X

Linux
macOS
Solaris
Windows

hosted

HP Open VMS

Data scheme

predefined scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

JSON types

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.

XML can be used for describing objects metadata

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language

yes

APIs and other access methods

HTTP REST

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

Supported programming languages

.Net
C++
Java
Python
Scala

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

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes, event driven architecture

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

yes, on a single node

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.

yes

User concepts

Access control

Role-based access control

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

GigaSpaces, the pioneer of In-Memory Computing , drives enterprise digital transformation...
» more

Competitive advantages

AI Driven Autonomous Operations – GigaSpaces’ unique AI driven technology simplifies...
» more

Typical application scenarios

Application Acceleration, Smart Caching, BI Acceleration on Fresh Data, Digital Integration...
» more

Key customers

Morgan Stanley, Bank of America, Goldman Sachs, PriceRunner, Cerner, Societe Generale,...
» more

Market metrics

Forrester has positioned GigaSpaces as a strong performer in The Forrester Wave™:...
» more

Licensing and pricing models

GigaSpaces offers both open source and commercial editions with annual subscriptions....
» more

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