DBMS > GigaSpaces vs. Postgres-XL vs. RavenDB vs. SpatiaLite

System Properties Comparison GigaSpaces vs. Postgres-XL vs. RavenDB vs. SpatiaLite

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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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open Source Operational and Transactional Enterprise NoSQL Document Database

Spatial extension of SQLite

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Document store

Spatial DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.52
Rank	#256	Overall
	#117	Relational DBMS

Score	3.01
Rank	#101	Overall
	#17	Document stores

Score	1.72
Rank	#149	Overall
	#3	Spatial DBMS

Website

www.gigaspaces.com

www.postgres-xl.org

ravendb.net

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

docs.gigaspaces.com/latest/landing.html

www.postgres-xl.org/documentation

ravendb.net/docs

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Gigaspaces Technologies

Hibernating Rhinos

Alessandro Furieri

Initial release

2000

2014

since 2012, originally named StormDB

2010

2008

Current release

15.5, September 2020

10 R1, October 2018

5.4, July 2022

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

Mozilla public license

Open Source

AGPL version 3, commercial license available

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

C++

Server operating systems

Linux
macOS
Solaris
Windows

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

server-less

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

distributed, parallel query execution

SQL-like query language (RQL)

yes

APIs and other access methods

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID, Cluster-wide transaction available

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

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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