DBMS > FatDB vs. LevelDB vs. ObjectBox vs. Sphinx

System Properties Comparison FatDB vs. LevelDB vs. ObjectBox vs. Sphinx

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

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

Primary database model

Document store
Key-value store

Key-value store

Object oriented DBMS
Vector DBMS

Search engine

Secondary database models

Time Series DBMS

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

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Website

github.com/google/leveldb

github.com/objectbox
objectbox.io

sphinxsearch.com

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

docs.objectbox.io

sphinxsearch.com/docs

Developer

FatCloud

Google

ObjectBox Limited

Sphinx Technologies Inc.

Initial release

2012

2011

2017

2001

Current release

1.23, February 2021

4.0 (May 2024)

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Bindings are released under Apache 2.0

Apache License 2.0

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

C++

C and C++

C++

Server operating systems

Windows

Illumos
Linux
OS X
Windows

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, plus "flex" map-like types

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

Via inetgration in SQL Server

SQL-like query language (SphinxQL)

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Proprietary native API

Proprietary protocol

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

via applications

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Data sync between devices allowing occasional connected databases to work completely offline

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

with automatic compression on writes

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.

User concepts

Access control

Can implement custom security layer via applications

yes

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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