DBMS > GigaSpaces vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. OrientDB

System Properties Comparison GigaSpaces vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. OrientDB

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Editorial information provided by DB-Engines

Name

Description

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

A Wide Column Store for rapid development using massive semi-structured datasets

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Document store
Graph DBMS
Key-value store

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

www.gigaspaces.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

orientdb.org

Technical documentation

docs.gigaspaces.com/latest/landing.html

learn.microsoft.com/azure/cosmos-db

www.orientdb.com/docs/last/index.html

Developer

Gigaspaces Technologies

Microsoft

OrientDB LTD; CallidusCloud; SAP

Initial release

2000

2014

2012

2010

Current release

15.5, September 2020

3.2.29, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
macOS
Solaris
Windows

hosted

All OS with a Java JDK (>= JDK 6)

Data scheme

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

SQL-like query language, no joins

APIs and other access methods

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

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

RESTful HTTP API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON 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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

JavaScript

Java, Javascript

Triggers

yes, event driven architecture

JavaScript

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication

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*

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

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

relationship in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

ACID

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

Access rights based on private key authentication or shared access signatures

Access rights for users and roles; record level security configurable

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