DBMS > LeanXcale vs. LevelDB vs. Machbase Neo vs. RDF4J

System Properties Comparison LeanXcale vs. LevelDB vs. Machbase Neo vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Key-value store
Relational DBMS

Key-value store

Time Series DBMS

RDF store

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

www.leanxcale.com

github.com/google/leveldb

machbase.com

rdf4j.org

Technical documentation

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

machbase.com/dbms

rdf4j.org/documentation

Developer

LeanXcale

Google

Machbase

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

Initial release

2015

2011

2013

2004

Current release

1.23, February 2021

V8.0, August 2023

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

free test version available

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Server operating systems

Illumos
Linux
OS X
Windows

Linux
macOS
Windows

Linux
OS X
Unix
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through Apache Derby

SQL-like query language

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
Java
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

none

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

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

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

volatile and lookup table

User concepts

Access control

simple password-based access control

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