DBMS > Lovefield vs. ObjectBox vs. Oracle Berkeley DB vs. Realm vs. Sphinx

System Properties Comparison Lovefield vs. ObjectBox vs. Oracle Berkeley DB vs. Realm vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

Extremely fast embedded database for small devices, IoT and Mobile

Widely used in-process key-value store

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Search engine

Secondary database models

Time Series DBMS

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

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

google.github.io/lovefield

objectbox.io

www.oracle.com/database/technologies/related/berkeleydb.html

realm.io

sphinxsearch.com

Technical documentation

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

docs.objectbox.io

docs.oracle.com/cd/E17076_05/html/index.html

realm.io/docs

sphinxsearch.com/docs

Developer

Google

ObjectBox Limited

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Realm, acquired by MongoDB in May 2019

Sphinx Technologies Inc.

Initial release

2014

2017

1994

2014

2001

Current release

2.1.12, February 2017

18.1.40, May 2020

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache License 2.0

Open Source

commercial license available

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 and C++

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

Android
iOS
Linux
macOS
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Android
Backend: server-less
iOS
Windows

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

SQL interfaced based on SQLite is available

SQL-like query language (SphinxQL)

APIs and other access methods

Proprietary native API

Proprietary protocol

Supported programming languages

JavaScript

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

.Net
Java

with Android only
Objective-C
React Native
Swift

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

Triggers

Using read-only observers

yes

only for the SQL API

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

online/offline synchronization between client and server

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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, 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

In-Memory realm

User concepts

Access control

yes

More information provided by the system vendor

News

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29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 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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