DBMS > Amazon Redshift vs. Blueflood vs. Firebase Realtime Database vs. HugeGraph

System Properties Comparison Amazon Redshift vs. Blueflood vs. Firebase Realtime Database vs. HugeGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable TimeSeries DBMS based on Cassandra

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

A fast-speed and highly-scalable Graph DBMS

Primary database model

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

aws.amazon.com/redshift

blueflood.io

firebase.google.com/products/realtime-database

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.aws.amazon.com/redshift

github.com/rax-maas/blueflood/wiki

firebase.google.com/docs/database

hugegraph.apache.org/docs

Developer

Amazon (based on PostgreSQL)

Rackspace

Google

acquired by Google 2014

Baidu

Initial release

2012

2013

2012

2018

Current release

0.9

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
OS X

hosted

Linux
macOS
Unix

Data scheme

yes

predefined scheme

schema-free

yes

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.

Secondary indexes

restricted

yes

also supports composite index and range index

SQL

Support of SQL

yes

does not fully support an SQL-standard

APIs and other access methods

JDBC
ODBC

HTTP REST

Android
iOS
JavaScript API
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

All languages supporting JDBC/ODBC

Java
JavaScript
Objective-C

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

limited functionality with using 'rules'

asynchronous Gremlin script jobs

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

yes, based on authentication and database rules

Users, roles and permissions

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