DBMS > GigaSpaces vs. Lovefield vs. RocksDB vs. Sphinx

System Properties Comparison GigaSpaces vs. Lovefield vs. RocksDB vs. Sphinx

Please select another system to include it in the comparison.

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

Embeddable relational database for web apps written in pure JavaScript

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Relational DBMS

Key-value store

Search engine

Secondary database models

Graph DBMS
Search engine

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	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.gigaspaces.com

google.github.io/lovefield

rocksdb.org

sphinxsearch.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

github.com/google/lovefield/blob/master/docs/spec_index.md

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

Developer

Gigaspaces Technologies

Google

Facebook, Inc.

Sphinx Technologies Inc.

Initial release

2000

2014

2013

2001

Current release

15.5, September 2020

2.1.12, February 2017

8.11.4, April 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache 2.0

Open Source

BSD

Open Source

GPL version 2, commercial licence available

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

JavaScript

C++

Server operating systems

Linux
macOS
Solaris
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

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

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language

via JavaScript builder pattern

SQL-like query language (SphinxQL)

APIs and other access methods

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

C++ API
Java API

Proprietary protocol

Supported programming languages

.Net
C++
Java
Python
Scala

JavaScript

C
C++
Go
Java
Perl
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Triggers

yes, event driven architecture

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

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

none

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

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

using MemoryDB

yes

User concepts

Access control

Role-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.