DBMS > Amazon Redshift vs. JanusGraph vs. MarkLogic vs. NSDb vs. VelocityDB

System Properties Comparison Amazon Redshift vs. JanusGraph vs. MarkLogic vs. NSDb vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A Graph DBMS optimized for distributed clusters

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

Operational and transactional Enterprise NoSQL database

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Graph DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Time Series DBMS

Graph DBMS
Object oriented DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

aws.amazon.com/redshift

janusgraph.org

www.progress.com/marklogic

nsdb.io

velocitydb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.janusgraph.org

www.progress.com/marklogic/documentation

nsdb.io/Architecture

velocitydb.com/UserGuide

Developer

Amazon (based on PostgreSQL)

Linux Foundation; originally developed as Titan by Aurelius

MarkLogic Corp.

VelocityDB Inc

Initial release

2012

2017

2001

2017

2011

Current release

0.6.3, February 2023

11.0, December 2022

7.x

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Java, Scala

Server operating systems

hosted

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Linux
macOS

Any that supports .NET

Data scheme

yes

schema-free

Schema can be enforced

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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.

yes

Secondary indexes

restricted

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL92

SQL-like query language

APIs and other access methods

JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

gRPC
HTTP REST
WebSocket

.Net

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java
Python

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java
Scala

.Net

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via XQuery or JavaScript

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in graphs

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

yes, with Range Indexes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

Role-based access control at the document and subdocument levels

Based on Windows Authentication

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