DBMS > GigaSpaces vs. Riak KV vs. Snowflake vs. Sphinx

System Properties Comparison GigaSpaces vs. Riak KV vs. Snowflake vs. Sphinx

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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

Distributed, fault tolerant key-value store

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

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Search engine

Secondary database models

Graph DBMS
Search engine

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	4.44
Rank	#80	Overall
	#9	Key-value stores

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

www.gigaspaces.com

www.snowflake.com

sphinxsearch.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

www.tiot.jp/riak-docs/riak/kv/latest

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

Developer

Gigaspaces Technologies

OpenSource, formerly Basho Technologies

Snowflake Computing Inc.

Sphinx Technologies Inc.

Initial release

2000

2009

2014

2001

Current release

15.5, September 2020

3.2.0, December 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache version 2, commercial enterprise edition

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

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Implementation language

Java, C++, .Net

Erlang

C++

Server operating systems

Linux
macOS
Solaris
Windows

Linux
OS X

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

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

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

Secondary indexes

yes

restricted

yes

full-text index on all search fields

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

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

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Proprietary protocol

Supported programming languages

.Net
C++
Java
Python
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Erlang

user defined functions

Triggers

yes, event driven architecture

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

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

selectable replication factor

yes

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

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

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

Role-based access control

yes, using Riak Security

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

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