DBMS > Apache Kylin vs. BaseX vs. Faircom DB vs. HugeGraph vs. mSQL

System Properties Comparison Apache Kylin vs. BaseX vs. Faircom DB vs. HugeGraph vs. mSQL

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A fast-speed and highly-scalable Graph DBMS

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Relational DBMS

Native XML DBMS

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Website

kylin.apache.org

basex.org

www.faircom.com/products/faircom-db

github.com/hugegraph
hugegraph.apache.org

hughestech.com.au/products/msql

Technical documentation

kylin.apache.org/docs

docs.basex.org

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

hugegraph.apache.org/docs

Developer

Apache Software Foundation, originally contributed from eBay Inc

BaseX GmbH

FairCom Corporation

Baidu

Hughes Technologies

Initial release

2015

2007

1979

2018

1994

Current release

3.1.0, July 2020

10.7, August 2023

V12, November 2020

0.9

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD license

commercial

Restricted, free version available

Open Source

Apache Version 2.0

commercial

free licenses can be provided

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

Java

ANSI C, C++

Java

Server operating systems

Linux

Linux
OS X
Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
macOS
Unix

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

XQuery supports types

yes, ANSI SQL Types, JSON, typed binary structures

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

also supports composite index and range index

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes, ANSI SQL with proprietary extensions

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Groovy
Java
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

asynchronous Gremlin script jobs

Triggers

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

depending on used storage backend, e.g. Cassandra and HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

none

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

multiple readers, single writer

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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 on 4 levels

Fine grained access rights according to SQL-standard with additional protections for files

Users, roles and permissions

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More resources

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