DBMS > HugeGraph vs. mSQL vs. Sadas Engine vs. XTDB

System Properties Comparison HugeGraph vs. mSQL vs. Sadas Engine vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

mSQL (Mini SQL) is a simple and lightweight RDBMS

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Graph DBMS

Relational DBMS

Document store

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/hugegraph
hugegraph.apache.org

hughestech.com.au/products/msql

www.sadasengine.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

hugegraph.apache.org/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

www.xtdb.com/docs

Developer

Baidu

Hughes Technologies

SADAS s.r.l.

Juxt Ltd.

Initial release

2018

1994

2006

2019

Current release

0.9

4.4, October 2021

8.0

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free licenses can be provided

commercial

free trial version available

Open Source

MIT License

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

C++

Clojure

Server operating systems

Linux
macOS
Unix

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

AIX
Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

A subset of ANSI SQL is implemented

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

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

JDBC
ODBC
Proprietary protocol

HTTP REST
JDBC

Supported programming languages

Groovy
Java
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C#
C++
Groovy
Java
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes

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

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Users, roles and permissions

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

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