DBMS > AlaSQL vs. JanusGraph vs. Newts vs. SAP Adaptive Server vs. Stardog

System Properties Comparison AlaSQL vs. JanusGraph vs. Newts vs. SAP Adaptive Server vs. Stardog

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Time Series DBMS based on Cassandra

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

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

Primary database model

Document store
Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

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

Website

alasql.org

janusgraph.org

opennms.github.io/newts

www.sap.com/products/technology-platform/sybase-ase.html

www.stardog.com

Technical documentation

github.com/AlaSQL/alasql

docs.janusgraph.org

github.com/OpenNMS/newts/wiki

help.sap.com/docs/SAP_ASE

docs.stardog.com

Developer

Andrey Gershun & Mathias R. Wulff

Linux Foundation; originally developed as Titan by Aurelius

OpenNMS Group

SAP, Sybase

Stardog-Union

Initial release

2014

2017

2014

1987

2010

Current release

0.6.3, February 2023

16.0

7.3.0, May 2020

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache 2.0

Open Source

Apache 2.0

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)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

Java

C and C++

Java

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Unix
Windows

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

yes

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

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

APIs and other access methods

JavaScript API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP REST
Java API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

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

JavaScript

Clojure
Java
Python

Java

C
C++
Cobol
Java
Java
Perl
PHP
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Java and Transact-SQL

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

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

based on Cassandra

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationships in graphs

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Access rights for users and roles

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