DBMS > Amazon Redshift vs. Google Cloud Datastore vs. JanusGraph vs. NSDb vs. VelocityDB

System Properties Comparison Amazon Redshift vs. Google Cloud Datastore vs. JanusGraph vs. NSDb vs. VelocityDB

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Name

Description

Large scale data warehouse service for use with business intelligence tools

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A Graph DBMS optimized for distributed clusters

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

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

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	4.36
Rank	#72	Overall
	#12	Document stores

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

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

cloud.google.com/datastore

janusgraph.org

nsdb.io

velocitydb.com

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/datastore/docs

docs.janusgraph.org

nsdb.io/Architecture

velocitydb.com/UserGuide

Developer

Amazon (based on PostgreSQL)

Google

Linux Foundation; originally developed as Titan by Aurelius

VelocityDB Inc

Initial release

2012

2008

2017

2011

Current release

0.6.3, February 2023

7.x

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Java, Scala

Server operating systems

hosted

Linux
OS X
Unix
Windows

Linux
macOS

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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.

Secondary indexes

restricted

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

JDBC
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

gRPC
HTTP REST
WebSocket

.Net

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Clojure
Java
Python

Java
Scala

.Net

Server-side scripts

Stored procedures

user defined functions

in Python

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

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

Multi-source replication using Paxos

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

yes

Relationships in graphs

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

Using Apache Lucene

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

User authentification and security via Rexster Graph Server

Based on Windows Authentication

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