DBMS > LevelDB vs. RDF4J vs. RethinkDB vs. Sequoiadb vs. Splice Machine

System Properties Comparison LevelDB vs. RDF4J vs. RethinkDB vs. Sequoiadb vs. Splice Machine

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

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

NewSQL database with distributed OLTP and SQL

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Key-value store

RDF store

Document store

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	2.84
Rank	#106	Overall
	#19	Document stores

Score	0.48
Rank	#260	Overall
	#41	Document stores
	#121	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

github.com/google/leveldb

Technical documentation

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

rdf4j.org/documentation

rethinkdb.com/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

splicemachine.com/how-it-works

Developer

Google

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

The Linux Foundation

since July 2017

Sequoiadb Ltd.

Splice Machine

Initial release

2011

2004

2009

2013

2014

Current release

1.23, February 2021

2.4.1, August 2020

3.1, March 2021

License

Commercial or Open Source

Open Source

BSD

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2

Open Source

Server: AGPL; Client: Apache V2

Open Source

AGPL 3.0, commercial license 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

C++

Java

C++

Java

Server operating systems

Illumos
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Linux

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

oid, date, timestamp, binary, regex

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

proprietary protocol using JSON

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
PHP
Python

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

.Net
C++
Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

JavaScript

yes

Java

Triggers

yes

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

range based

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Atomic single-document operations

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

in-memory storage is supported as well

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

yes

users and table-level permissions

simple password-based access control

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

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