DBMS > Lovefield vs. Netezza vs. RocksDB vs. Sphinx

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

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Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

Data warehouse and analytics appliance part of IBM PureSystems

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

Relational DBMS

Key-value store

Search engine

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

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

google.github.io/lovefield

www.ibm.com/products/netezza

rocksdb.org

sphinxsearch.com

Technical documentation

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

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

Developer

Google

IBM

Facebook, Inc.

Sphinx Technologies Inc.

Initial release

2014

2000

2013

2001

Current release

2.1.12, February 2017

9.2.1, May 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

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

JavaScript

C++

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

Linux

included in appliance

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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.

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC
OLE DB

C++ API
Java API

Proprietary protocol

Supported programming languages

JavaScript

C
C++
Fortran
Java
Lua
Perl
Python
R

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

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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, 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

Users with fine-grained authorization concept

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