DBMS > GigaSpaces vs. Hypertable vs. Microsoft Azure Cosmos DB vs. searchxml

System Properties Comparison GigaSpaces vs. Hypertable vs. Microsoft Azure Cosmos DB vs. searchxml

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

An open source BigTable implementation based on distributed file systems such as Hadoop

Globally distributed, horizontally scalable, multi-model database service

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Native XML DBMS
Search engine

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

www.gigaspaces.com

azure.microsoft.com/services/cosmos-db

www.searchxml.net/category/products

Technical documentation

docs.gigaspaces.com/latest/landing.html

learn.microsoft.com/azure/cosmos-db

www.searchxml.net/support/handouts

Developer

Gigaspaces Technologies

Hypertable Inc.

Microsoft

informationpartners gmbh

Initial release

2000

2009

2014

2015

Current release

15.5, September 2020

0.9.8.11, March 2016

1.0

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

GNU version 3. Commercial license available

commercial

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

C++

Server operating systems

Linux
macOS
Solaris
Windows

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Windows

Data scheme

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

yes

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language

APIs and other access methods

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

C++ API
Thrift

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

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

.Net
C++
Java
Python
Scala

C++
Java
Perl
PHP
Python
Ruby

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

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

yes

JavaScript

yes

on the application server

Triggers

yes, event driven architecture

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

selectable replication factor on file system level

yes

Implicit feature of the cloud service

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

multiple readers, single writer

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

Domain, group and role-based access control at the document level and for application services

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