DBMS > GeoSpock vs. GigaSpaces vs. OrigoDB vs. Snowflake vs. Sphinx

System Properties Comparison GeoSpock vs. GigaSpaces vs. OrigoDB vs. Snowflake vs. Sphinx

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

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

A fully ACID in-memory object graph database

Cloud-based data warehousing service for structured and semi-structured data

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Document store
Object oriented DBMS

Relational DBMS

Search engine

Secondary database models

Time Series DBMS

Graph DBMS
Search engine

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	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

geospock.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

origodb.com/docs

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

Developer

GeoSpock

Gigaspaces Technologies

Robert Friberg et al

Snowflake Computing Inc.

Sphinx Technologies Inc.

Initial release

2000

2009

under the name LiveDB

2014

2001

Current release

2.0, September 2019

15.5, September 2020

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

commercial

Open Source

GPL version 2, commercial licence available

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, Javascript

Java, C++, .Net

C++

Server operating systems

hosted

Linux
macOS
Solaris
Windows

Linux
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

yes

Secondary indexes

temporal, categorical

yes

full-text index on all search fields

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-99 for query and DML statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC

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

.NET Client API
HTTP API
LINQ

CLI Client
JDBC
ODBC

Proprietary protocol

Supported programming languages

.Net
C++
Java
Python
Scala

.Net

JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes, event driven architecture

yes

Domain Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

horizontal partitioning

client side managed; servers are not synchronized

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

The original contents of fields are not stored in the Sphinx index.

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 table

Role-based access control

Role based authorization

Users with fine-grained authorization concept, user roles and pluggable authentication

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