DBMS > BigObject vs. LevelDB vs. MySQL vs. RavenDB vs. Stardog

System Properties Comparison BigObject vs. LevelDB vs. MySQL vs. RavenDB vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

Widely used open source RDBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Relational DBMS

Key/Value like access via memcached API

Document store

Graph DBMS
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	0.13
Rank	#333	Overall
	#147	Relational DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

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

Website

bigobject.io

github.com/google/leveldb

www.mysql.com

ravendb.net

www.stardog.com

Technical documentation

docs.bigobject.io

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

dev.mysql.com/doc

ravendb.net/docs

docs.stardog.com

Developer

BigObject, Inc.

Google

Oracle

since 2010, originally MySQL AB, then Sun

Hibernating Rhinos

Stardog-Union

Initial release

2015

2011

1995

2010

Current release

1.23, February 2021

8.4.0, April 2024

5.4, July 2022

7.3.0, May 2020

License

Commercial or Open Source

commercial

free community edition available

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL version 3, commercial license available

commercial

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

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

C++

C and C++

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Raspberry Pi
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like query language (RQL)

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

APIs and other access methods

fluentd
ODBC
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

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

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes

proprietary syntax

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Multi-source replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

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.

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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, Cluster-wide transaction available

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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 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

Authorization levels configured per client per database

Access rights for users and roles

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