DBMS > MySQL vs. Oracle Berkeley DB vs. Stardog vs. SurrealDB

System Properties Comparison MySQL vs. Oracle Berkeley DB vs. Stardog vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Widely used open source RDBMS

Widely used in-process key-value store

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

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

Primary database model

Relational DBMS

Key/Value like access via memcached API

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Graph DBMS
RDF store

Document store
Graph DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

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

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

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

Website

www.mysql.com

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

www.stardog.com

surrealdb.com

Technical documentation

dev.mysql.com/doc

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

docs.stardog.com

surrealdb.com/docs

Developer

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Stardog-Union

SurrealDB Ltd

Initial release

1995

1994

2010

2022

Current release

8.3.0, January 2024

18.1.40, May 2020

7.3.0, May 2020

v1.1.1, January 2024

License

Commercial or Open Source

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

commercial license available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

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

Java

Rust

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC
Proprietary native API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.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
Clojure
Groovy
Java
JavaScript
Python
Ruby

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

only for the SQL API

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationships in graphs

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles

yes, based on authentication and database rules

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