DBMS > LevelDB vs. Microsoft Azure Table Storage vs. MySQL vs. RavenDB vs. RDF4J

System Properties Comparison LevelDB vs. Microsoft Azure Table Storage vs. MySQL vs. RavenDB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

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

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used open source RDBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Key-value store

Wide column store

Relational DBMS

Key/Value like access via memcached API

Document store

RDF store

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

github.com/google/leveldb

azure.microsoft.com/en-us/services/storage/tables

www.mysql.com

ravendb.net

rdf4j.org

Technical documentation

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

dev.mysql.com/doc

ravendb.net/docs

rdf4j.org/documentation

Developer

Google

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Hibernating Rhinos

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2011

2012

1995

2010

2004

Current release

1.23, February 2021

8.4.0, April 2024

5.4, July 2022

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL version 3, commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

Java

Server operating systems

Illumos
Linux
OS X
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

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

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

optimistic locking

ACID

not for MyISAM storage engine

ACID, Cluster-wide transaction available

ACID

Isolation support depends on the API used

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

with automatic compression on writes

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

Authorization levels configured per client per database

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