DBMS > DataFS vs. Oracle Berkeley DB vs. SurrealDB

System Properties Comparison DataFS vs. Oracle Berkeley DB vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Widely used in-process key-value store

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Object oriented DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Graph DBMS

Secondary database models

Graph DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

newdatabase.com

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

surrealdb.com

Technical documentation

dev.mobiland.com/Overview.xsp

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

surrealdb.com/docs

Developer

Mobiland AG

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SurrealDB Ltd

Initial release

2018

1994

2022

Current release

1.1.263, October 2022

18.1.40, May 2020

v1.1.1, January 2024

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

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

Rust

Server operating systems

Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

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

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

SQL-like query language

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
C
C#
C++
VB.Net

.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

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Windows-Profile

yes, based on authentication and database rules

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